esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 1 Introduction  
The global fish industry is experiencing rapid growth and has become a crucial source of high-quality protein. With advancements in fishing and aquaculture technologies, the market now offers an increasingly diverse range of fish species, encompassing both marine and freshwater varieties to cater to the varied preferences of consumers. Fish are not only diverse in species but also highly nutritious, containing up to 20% high-quality protein by weight, which has a high biological value due to the presence of essential amino acids, unsaturated fatty acids (such as omega-3), lipid-soluble vitamins, and minerals like calcium, phosphorus, and selenium, all of which are essential for human health [1, 2, 3, 4, 5].  
However, fish are highly perishable, a characteristic that presents significant challenges to the industry. Once caught, fish quickly undergo a series of complex spoilage processes, including autolytic enzymatic spoilage, oxidative spoilage, and microbial growth [6]. Their high perishability stems from intrinsic compositional and structural traits: a moisture content of approximately 75-80%, which fosters bacterial growth; low connective tissue levels that render muscle fragile and susceptible to enzymatic degradation; abundant free amino acids and non-protein nitrogen compounds that fuel microbial metabolism and produce off-flavors and toxic metabolites; and a near-neutral pH (6.0-7.0) that optimizesboth enzyme activity and microbial proliferation [7, 8]. This spoilage is further accelerated by the high levels of non-protein nitrogen compounds in fish flesh, low acidity (pH > 6), and the abundant resident microbiota [9]. Enzymatic and oxidative activities that occur after harvest lead to significant changes in sensory and nutritional properties, resulting in a loss of freshness, which consumers perceive as unfavorable changes in odor, flavor, and texture, raising serious food safety concerns [10]. The rate of spoilage is influenced by factors such as fish species, sanitary conditions on board, storage duration, and temperature.  
Despite extensive research, the existing literature often examines fish spoilage mechanisms and preservation methods in isolation rather than integrating mechanistic insights with preservation technologies. Consequently, there remains a significant knowledge gap regarding comprehensive, integrative approaches that effectively target specific spoilage pathways. By providing a thorough mechanistic analysis of post-harvest spoilage processes--including enzymatic autolysis, lipid oxidation, and microbial proliferation--and simultaneously evaluating state-of-the-art preservation technologies designed to inhibit these pathways, this review bridges that gap. By linking spoilage mechanisms directly to targeted interventions, we offer a mechanism-based framework to guide the development of more effective and sustainable fish preservation strategies, addressing the dual imperatives of food-quality enhancement and safety assurance in the global fish industry.
## 4 Conclusions  
In this study, NIR spectroscopy was used to rapidly identify liquor quality. Baseline correction and noise filter preprocessing were performed using a combination of adaptive iterative reweighted penalized least squares and SG convolutional smoothing. To ensure that more feature information was retained after the preprocessing of the NIR spectra, a multi-class SVM prediction model was used to evaluate different preprocessing parameters. Multiple sets of preprocessing parameters with the highest prediction accuracy were selected for feature extraction. The CARS method was used to extract feature wavelengths. The dependent variable in this study was the category attribute, and DPLSR was used to improve the CARS method. The KPCA method was used to further reduce the dimension of the feature wavelengths to improve the accuracy and generalization ability of the prediction model. The H-MSVM prediction model was built based on the principal components obtained by the KPCA method. Through the abovementioned methods, the accuracy of the prediction model was effectively improved, and the identification result of the best parameter combination reached more than 96%. The results showed that NIR is an effective tool for the nondestructive and rapid identification analysis of liquor quality, which can be applied to the on-line detection of liquor manufacturing and solve the problem of artificial sensory evaluation. At the same time, this method will provide an effective method for the qualitative analysis of substances containing high-dimensional components, and can be applied to the production of food, chemicals, and medical drugs.  
Conceptualization: X.T.; methodology and writing original draft: G.Z.; experiment and data acquisition: Y.P., X.L. and T.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Plan Project of Sichuan Provincial, grant number 2022 YFS0554; the Liquor-Making Biological Technology and Application of the Key Laboratory of Sichuan Province Open Project, grant number NJ2022-06; the Scientific Research and Innovation Team Program of the Sichuan University of Science and Engineering, grant number SUSE652B005; Wuliangye and Sichuan University of Science and Engineering Industry--University --Research Project, grant number CXY2022 ZR007; and the Science and Technology Achievement Transformation Project of Sichuan University of Science and Engineering, grant number HXY01, China. The authors acknowledge this support.  
Not applicable.
# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 5 Conclusions  
In terms of the center spot search, compared with ordinary template matching, using invariant moments for matching was much faster. On the one hand, because the invariant moments digitized the image, the information to be processed at one time was 72% less than that for template matching using images for the operation. The invariant moment of every point of the source image would be calculated in advance and saved in a database as long as the invariant moments of the matched region were calculated, and the similarity to the database was calculated. Template matching generates a weighting matrix during matching and the weighting matrix must be created again whenever the template is changed, which takes considerable time.  
The proposed system uses the aspect ratio to check whether the light spot is complete or not. The positioning method is selected according to the complete image of the light spot; thus, the complex computation of all images is unnecessary, and the load on the overall system is reduced.  
Conceptualization, C.-S.L., Y.-L.H., Y.-C.L. and Y.-C.H.; methodology, Y.-C.H. and C.-S.L.; software, Y.-C.H.; validation, C.-S.L., Y.-C.H. and S.-H.C.; formal analysis, Y.-C.H. and C.-S.L.; investigation, S.-H.C.; resources, C.-S.L.; data curation, Y.-C.H.; writing--original draft preparation, C.-S.L.; writing--review and editing, C.-S.L. and S.-H.C.; visualization, S.-H.C.; supervision, C.-S.L.; project administration, C.-S.L., Y.-C.L., and Y.-L.H.; funding acquisition, C.-S.L., Y.-L.H., and Y.-C.L.  
This research was funded by the Ministry of Science and Technology, under Grant No. MOST 106-2221-E-035-053-MY2 and MOST107-2321-B-035-001.  
The authors 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.
# Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method
## 5 Conclusions  
In this study, an automatic identification method based on the random decrement technique was developed to detect VIV events from massive long-term continuous monitoring data. A simple COV-based threshold was demonstrated to successfully distinguish VIV from random vibration through numerical simulation of the three-DOF structure. The method was applied to automatically select out the VIV of the Xihoumen Bridge by using three-month monitoring data of acceleration, and the characteristics of VIV of the bridge was evaluated.  
From the three-month monitoring data, a total number of 60 VIV events were successfully identified. The frequency range of VIV was between 0.1-0.5 Hz, which corresponds to the modal frequencies of the second to ninth vertical modes of stiffening girders. At wind velocity 5-18 m/s,  
Figure 23: Relationship between 10 min mean wind velocity and root mean squares (RMS) of displacement at quarter span.  
Figure 24: Relationship between turbulence intensity and root mean squares (RMS) of displacement at quarter span.  
different vertical modes were excited according to Stouhal number principle. Each mode had its own lock-in wind velocity; the Stouhal number was approximately 0.11. VIV happened most frequently for the third mode and sixth mode, which are symmetrical modes of vibration. A possible reason is that the symmetrical modes were less damped than the unsymmetrical modes. The maximum vibration amplitude of VIV at measurement locations was about 14 cm. Correlation with mean wind shows that the VIV happened mostly for the wind direction perpendicular to the bridge axis. VIV of some modes was observed for turbulence intensity up to 13%, and was less affected by turbulence intensity. The identified VIV events could be helpful for further understanding of VIV as well as its correlation with wind parameters. Evaluation of aerodynamic damping will also be carried out using the identified VIV data.  
Formal analysis, Z.H., and Y.L.; Methodology, X.H. and Q.W.; Motivation, Z.C.  
The work was finally supported by NSFC (No. 51422806).  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Impact of Synthesis Time  
To explore the influence of synthesis time, membranes were prepared over 24, 36, 48, 60, and 72 h. Gas-tightness testing (Table S8) revealed incomplete membrane formation across the substrate surface after 24 and 36 h (N\({}_{2}\) permeance of 6.78 and 0.25 \(\times\) 10\({}^{-7}\) [mol/(m\({}^{2}\)sPa)], respectively). In other words, inadequate crystal intergrowth, resulting in intercrystalline gaps, was observed for shorter synthesis times. Additionally, the presence of unreacted Si within the framework composition due to insufficient reaction time can negatively impact membrane performance. Nevertheless, by increasing the duration of synthesis to 48 h, the SSZ-13 layer was formed on the substrate surface (N\({}_{2}\) permeance of 0 [mol/(m\({}^{2}\)sPa)]). Based on Figure 3b, high ideal CO\({}_{2}\)/CH\({}_{4}\) permselectivity at a synthesis time of 48 h is attributed to the formation of a well-interconnected membrane structure. Extending the synthesis time to 72 h, resulted in decreased ideal CO\({}_{2}\)/CH\({}_{4}\) and H\({}_{2}\)/SF\({}_{6}\) permselectivity from 103 to 39 and from 125 to 76, respectively, due to increased membrane thickness, hindering CO\({}_{2}\) permeance, and the potential formation of undesired phases [47].
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 4 Calculation of Jacobian Matrix Elements Using Exact Zeoppritz and Biot-Gassmann Equations
### The Calculation of \(\frac{\partial A}{\partial m_{j}}\)
#### 4.1.1 Partial Derivative of Matrix A with Respect to \(K_{d1}\)  
The trigonometric functions of \(\alpha\), \(\beta\), \(\alpha^{\prime}\) and \(\beta^{\prime}\) are related to the bulk modulus \(K_{d1}\) of the medium, letting:  
\[\xi_{kd1}=\frac{\partial}{\partial K_{d1}}\Bigg{(}\frac{v_{s1}}{v_{p1}}\Bigg{)} =\frac{1}{v_{p1}^{2}}\bigg{(}v_{p1}\frac{\partial v_{s1}}{\partial K_{d1}}-v_ {s1}\frac{\partial v_{p1}}{\partial K_{d1}}\bigg{)}=\frac{v_{s1}}{v_{p1}} \big{(}\tau_{kd1}^{v1}-\tau_{kd1}^{vp1}\big{)} \tag{18}\]  
where \(\tau_{kd1}^{vp1}=\frac{1}{v_{p1}}\frac{\partial v_{p1}}{\partial K_{d1}}\) and \(\tau_{kd1}^{v1}=\frac{1}{v_{s1}}\frac{\partial v_{s1}}{\partial K_{d1}}\). Then we have:  
\[\frac{\partial\sin\beta}{\partial K_{d1}}=\frac{\partial}{\partial K_{d1}} \Bigg{(}\frac{v_{s1}}{v_{p1}}\Bigg{)}\sin\alpha=\frac{v_{p1}}{v_{s1}}\frac{ \partial}{\partial K_{d1}}\Bigg{(}\frac{v_{s1}}{v_{p1}}\Bigg{)}\sin\beta=\eta _{kd1}\sin\beta \tag{19}\]  
where \(\eta_{kd1}=\frac{v_{p1}}{v_{s1}}\frac{\partial}{\partial K_{d1}}\Big{(}\frac{ v_{s1}}{v_{p1}}\Big{)}=\frac{v_{p1}}{v_{s1}}\xi_{kd1}\). Since \(\alpha\) is independent of \(K_{d1}\), the partial derivatives of the trigonometric functions of angle \(\alpha\) with respect to \(K_{d1}\) are equal to zero. The detailed derivations of other partial derivatives are shown in Appendix A.  
Thus, the partial derivative of matrix A with respect to \(K_{d1}\) is:  
\[\frac{\partial A}{\partial K_{d1}}=\tau_{kd1}^{vp1}\left[\begin{array}{cccc}0& -\frac{\eta_{kd1}}{v^{p1}}\tan\beta\sin\beta&\sin\alpha^{\prime}&\tan\beta^{ \prime}\sin\beta^{\prime}\\ 0&\frac{v_{kd1}}{v^{p1}_{kd1}}\sin\beta&\tan\alpha^{\prime}\sin\alpha^{\prime}& -\sin\beta^{\prime}\\ \tau_{kd1}^{vp1}(a_{31}-1)&\frac{\eta_{kd1}}{v^{p1}}a_{32}(2-\tan^{2}\beta)&- \bigg{(}3a_{33}+\frac{2\rho_{2}}{\rho_{1}}\frac{v_{p2}}{v_{p1}}\bigg{)}&a_{3 4}(\tan^{2}\beta^{\prime}-2)\\ a_{41}\bigg{(}2\frac{\eta_{kd1}}{v^{p1}_{kd1}}+1\bigg{)}&\frac{v^{p11}_{kd1}}{v^ {p1}_{kd1}}a_{42}+\frac{2\eta_{kd1}}{v^{p1}_{kd1}}(a_{42}-v_{s1})&a_{43}\big{(} \tan^{2}\alpha^{\prime}-1\big{)}&-2(a_{44}+\frac{\rho_{2}}{\rho_{1}}v_{s2})\\ \end{array}\right] \tag{20}\]
## 5 Conclusions  
This paper showed a comparison between different approaches for the estimation of functional joint axes. The work was conducted at a simulation level by developing a multi-body model resembling the lower limb of a subject, which was used to generate simulated motion capture data. The helical axis and SARA approaches were used to estimate the AoR of the simulated ankle joint for several motion patterns and testing conditions. The main conclusions are reported in the following:  
1. For the 1-DoF and 2-DoFs configurations, the SARA proves to be more accurate than the helical axis methodology, regardless of the different conditions considered;
2. For the 6-DoFs configuration, the SARA outperforms the helical axis approach when a high level of noise is considered or when the number of samples used to discretize the gait cycle is limited;
3. The IHA methodology allows a description of joint kinematics that is intrinsically less constrained than the one enabled by the SARA. This could be useful when a more detailed, time-dependent representation of joint kinematics is necessary.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 2 Massive MIMO System Model  
The uplink of an uncoded massive MIMO system is studied, and consists of \(B\) antennas configured at the received side serving \(U\) single-antenna users within the same time-frequency resource [21]. Figure 1 illustrates a schematic diagram. And \(B\) is much greater than \(U\).  
The \(U\times 1\) dimensional symbol vector transmitted by total users in the meantime is \(s_{c}=\left[s_{1},s_{2},\cdots,s_{U}\right]\), where \(s_{U}\in\Omega\) is the symbol vector sent by the \(U_{th}\) user and \(\Omega\) is the set of modulated symbols for Quadrature Amplitude Modulation (QAM). The channel gain between the antennas at the receiving and transmitting apparatuses form the channel matrix \(\widetilde{H}_{c}\in\mathbb{C}^{B\times U}\), where \(\widetilde{h}_{c_{B,U}}\) is the channel coefficient between the \(U_{th}\) transmit and the \(B_{th}\) receive antenna. Then, the reception signal vector has the form [21]  
\[y_{c}=\widetilde{H}_{c}s_{c}+n_{c} \tag{1}\]  
where \(n_{c}\) denotes a \(B\times 1\) dimensional complex Additive White Gaussian Noise (AWGN) vector with zero mean, and the covariance matrix is \(\sigma^{2}I\). To simplify the operation, Equation (1) is transformed into an analogue real-valued model as [21]  
\[y=Hs+n \tag{2}\]  
The assumption is that there is perfect channel state information available at the receiving end.  
Figure 1: Signal-detection framework for massive MIMO communications.
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 2 Related Works  
Object detection, a cornerstone of computer vision significantly advanced by deep learning [16], is critical for autonomous driving. This application demands high accuracy and high Frames Per Second (FPS) to navigate complex traffic environments featuring occlusion, varying object scales (especially small objects) and irregular target geometries [1]. Recent efforts also focus on robustness to adverse conditions [24] and computational efficiency for on-vehicle deployment [25].  
Early deep learning detectors included two-stage architectures like Faster R-CNN [10] and Cascade R-CNN [26], which achieved high precision but often at the cost of lower FPS, limiting their suitability for high-FPS systems [1; 11]. Single-shot detectors, such as SSD [12] and RetinaNet [27] (using FPN [28]) offered improved speed. The You Only Look Once (YOLO) series [29] became prominent for its favorable speed-accuracy balance [16; 30].  
Modern detectors employ various techniques for complex scenes. Multi-scale feature fusion (e.g., FPN [28], PANet [31], BiFPN [32]) remains vital. Deformable Convolutions (DCNs), including DCNv3 (a key part of models like InternImage [33]), provide adaptive spatial sampling crucial for irregular shapes [34; 35; 33]. Attention mechanisms, such as Large Kernel Attention (LKA) [36] which efficiently captures long-range dependencies, are also integrated to enhance feature representation [37; 38; 39; 40].  
While SOTA models like InternImage-H [33] achieve high accuracy on benchmarks (e.g., BDD100K [22]), their computational cost can be prohibitive for high-FPS deployment [41]. Similarly, top KITTI [23] performers often use multi-modal data (camera-LiDAR) or complex 3D processing [42; 43], which pose practical challenges [44; 45]. Thus, efficient,camera-only detectors remain crucial. This has driven innovation in fast detectors like the YOLO series (YOLOv5-YOLOv10 [15, 18, 46, 47]) and others [2, 48, 49]. Our work, DGSS-YOLOv8s, builds on YOLOv8s, focusing on targeted enhancements for small, occluded, and irregularly shaped objects--critical for safety--while maintaining high FPS, unlike general-purpose SOTA models, which may not optimize for these specific challenging scenarios under demanding latency constraints.  
Bounding box regression accuracy, especially for small and occluded objects, is another focus. Beyond standard IoU extensions [50, 51], specialized losses like Shape-IoU [20] (shape/scale-aware) and Inner-IoU [21] (adaptive overlap quality) offer improved localization. However, concurrently addressing small size, irregular shape, occlusion, and strict high-FPS demands in an efficient, unified framework remains a significant challenge, motivating our proposed architectural enhancements.
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 2 Methodology  
### The Proposed Method  
In this paper, a heat load prediction method for a DHS based on AE-GWO-GRU is designed and implemented. The overall framework of the system consists of two main parts: data analysis and processing, and model training and prediction. The overall framework is shown in Figure 2.  
1. The data analysis and processing part mainly includes four steps: data acquisition, data cleaning, data analysis, and data processing. 1. Data Acquisition: Multi-type sensors in the DHS acquire system operating parameters and outdoor meteorological factors during each cycle. These data are then uploaded and stored in the database. 2. Data Cleaning: Outliers, abnormal zeros, and missing values in the original data set are processed to obtain a complete and high-quality multi-dimensional heat load time series. 3. Data Analysis: The analysis includes examining the impact of system lag on prediction modeling and determining the prediction period. Additionally, the correlation between the influencing factors of heat load is investigated to determine the input features and dimensions for the prediction model. 4. Data Processing: The multi-dimensional heat load time series data are augmented and then divided into training, validation, and test sets in a ratio of 6:2:2. The data are normalized to a form suitable for model training and prediction.  
2. The model training and prediction component includes basic model training and model parameters optimization. 1. Basic Model Training: The GRU basic prediction model is trained using the default model parameters. 2. Model Parameters Optimization: The GWO algorithm optimizes the key model parameters and determines the optimal parameter combination. 3. Online Prediction: The GRU model is trained and used for prediction with the optimal parameter combination.
# Grating-like Terahertz Metasurface for Large-Deflection-Angle Beam Manipulations
###### Abstract  
Multifunctional terahertz beam manipulations have attracted much attention because of the potential for wide-scale applications in terahertz imaging, communications, etc. In this work, a grating-like terahertz reflective-type metasurface is designed for terahertz beam manipulations on the basis of a frequency-scanning mechanism. The theoretical calculation based on the grating principle has predicted that the metasurface grating can steer the deflected beam from 59.5\({}^{\circ}\) to 47.3\({}^{\circ}\) as the frequency of the perpendicularly incident terahertz wave changes between 0.87 and 1.02 THz. The large-deflection-angle frequency-scanning performance is validated by both numerical simulations and experimental tests. The metasurface grating developed in this work possesses the potential for applications in terahertz beam steering and beam-splitting devices.  
metasurface; terahertz; grating; frequency-scanning +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: These authors contributed equally to this work.
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 3 Weld Seam Extraction
### Edge Convexity Validation  
With all the valid vertices obtained, a weld seam can be seen as an edge connecting two vertices that share two intersection planes. However, not all the edges are valid weld seams. A simple example is shown in Figure 10, and except \(p_{1}p_{2}\) (in red color), all the other edges are invalid weld seams.  
By intuition, we recognize that weld seams must represent \"concave edges,\" while the remaining edges are \"convex edges.\" A concave edge is formed by two surfaces with an angle between \(0^{\circ}\) to \(180^{\circ}\), and the angle has to be outside of the entity. Through the use of the half-edge data structure, also known as the doubly connected edge list [23], we observe the following pattern. As illustrated in Figure 11, for two adjacent planes, \(\mathbf{e}_{1}\) and \(\mathbf{e}_{2}\) are the twin half-edges of their common edge \(p_{1}p_{2}\). \(\mathbf{n}_{1}\) and \(\mathbf{n}_{2}\) are the corresponding normal vectors. We introduce variable \(\varsigma\) as an indicator of the convexity of an edge:  
\[\varsigma=(\mathbf{n}_{1}\times\mathbf{n}_{2})\cdot\mathbf{e}_{1}. \tag{18}\]  
Figure 11: Concave edge (**a**) and convex edge (**b**).  
Figure 10: Edges connected by vertices.  
The conclusion is as follows: when \(\varsigma<0\), edge \(p_{1}p_{2}\) is concave; when \(\varsigma>0\), edge \(p_{1}p_{2}\) is convex; when \(\varsigma=0\), the two planes forming edge \(p_{1}p_{2}\) are coplanar. With this principle, all the convex edges can be ruled out.  
By expressing the intersection points in each plane with the half-edge data structures, all the intersection points and edges constitute a half-edge mesh, where the edge convexity indicator is used to validate concave edges as valid weld seams.