esa-sceva/satcom-chunk-collection huggingface dataset:
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
###### Abstract  
Light field (LF) cameras can record multi-view images from a single scene, and these images can provide spatial and angular information to improve the performance of image super-resolution (SR). However, it is a challenge to incorporate distinctive information from different LF views. At the same time, due to the inherent resolution of the image sensor, high spatial and angular resolution are trade-off problems. In this paper, we propose a progressive multi-scale fusion network (PMFN) to improve the LFSR performance. Specifically, a progressive feature fusion block (PFFB) based on an encoder-and-decoder structure is designed to implicitly align disparities and integrate complementary information between complementary views. The core module of the PFFB is a dual-branch multi-scale fusion module (DMFM), which can integrate the information from a reference view and auxiliary views to produce a fusion feature. Each DMFM consists of two parallel branches, which have different receptive fields to fuse hierarchical features from complementary views. Three DMFMs with a dense connection are used in the PFFB, which can fully exploit multi-level features to improve the SR performance. Experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves state-of-the-art performance among existing methods. Moreover, quantitative results show that our method can also generate faithful details.  
light field; super-resolution; multi-scale; feature fusion; complementary information +
Footnote †: journal: Medical Image and Image Processing  
1
## 2 An Overview
### Self Sovereign Identity and Building Artifacts
Unlike the centralized, third-party, federated, and user-centric models, which require the verifying entity to somehow reach out to the issuer to verify digital credentials presented to them by the subjects, the SSI model allows the issuer to leave all necessary proofs (cryptographic proofs such as digital signatures and timestamps) in a decentralized public ledger so that anyone can verify them against it....Digital wallets: Digital wallets are portable and secure personal repositories; ideally in the form of a mobile app, they allow us to manage our identifiers, authenticators, data, and verifiable credentials within our phones, which are completely protected and under our control. We decide what information we disclose to whom in the form of verifiable presentations....\"_  
In the proposed building digitization model, we assume that the building artifacts and all the individuals/organizations that are involved in its design/construction/maintenance during its entire life cycle, which goes from its conception to its demolition, are identified through a digital identity, precisely through a DID. This is because, as defined above, a digital identity, hence his/her/its corresponding identifier, allows for unique identification of an entity that can correspond to both a natural person or legal entity but also to things, i.e., building artifacts. Each digital identity owns precise Verifiable Credentials (VC) with which some of its attributes are certified, for example the possession of a driving license, the possession of a school diploma and a birth certificate, in the case of a natural person, the possession of certificates of super parties, of competence, independence and impartiality in the case of certification bodies or Public Administrations, the possession of certifications of professionalism in the case of engineers, architects and surveyors, or the possession of energy  
Figure 1: SSI data model proposed by EBSI. performance certificates, certificates of town planning compliance, or electrical system certificates in the case of building artifacts. From this model arises a trilateral relationship, in which there is the _issuer_ who issues the credentials, which can be the public or private body, or the identity itself, that certifies a certain status, the _holder_ of the credentials that stores them in his/her/its wallet and presents them to third parties when needed, and finally the _verifier_ to whom the credentials are presented. Through the use of DIDs and their DID documents, the SSI model provides for checking the validity of the credentials of a digital identity. These credentials have the characteristic of being permanent, cryptographically verifiable, decentralized and solvable, i.e., able to identify not only the public key of the issuer but also the address connected to it. The DIDs have already been implemented in the W3C context and, therefore, already constitute a standard.
# Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch
## 2 Materials and Methods
### Combination of Reliability Prediction and FMEA  
In this paper, a reliability prediction method of the loading and unloading truss robot for CNC punch is proposed by using the component counting method and fuzzy theory. The specific idea is as follows: firstly, the failure rate of the existing components is used to predict the failure rate and MTBF value of the electrical control system by the counting method of components. Secondly, based on the predicted results of the electrical control system, the failure rate and MTBF values of the mechanical system and pneumatic system are predicted by using the fuzzy theory. Finally, the failure rate and MTBF value of the loading and unloading truss robot for CNC punch are predicted, and the weak link is analyzed.  
Then, the failure frequency, failure mode and main failure reasons of the loading and unloading truss robot for CNC punch system are calculated through FMEA analysis of the collected the loading and unloading truss robot for CNC punch fault data, and the weak link is obtained, in order to verify the effectiveness of the reliability prediction method proposed in this paper and the accuracy of the predicted results. The detailed procedure flow chart is shown in Figure 1.
## 2 Large-Scale Triaxial Test with Different End Restraint Conditions  
### Test Equipment  
The test equipment was the YLSZ30-3 high-pressure large-scale triaxial shear apparatus and a microfriction load-transfer plate developed by the Yangtze River Scientific Research Institute for triaxial tests. Figure 1 shows the YLSZ30-3 high-pressure triaxial shear apparatus. It is a stress and strain control triaxial apparatus composed of a vertical pressure loading and control system, a confining pressure loading and control system, a triaxial pressure chamber, a loading frame, a high-voltage accumulator, a displacement and volume change measurement system, load sensors, and a data acquisition system. Itsmaximum confining pressure is 6 MPa. Its maximum vertical load is 2500 kN. Its maximum vertical stroke is 300 mm. Its shear speed can be adjusted in the range of 0.2-2 mm/min, and its vertical load voltage stabilization error is not more than 1%.  
To solve the end-restraint problem in triaxial tests of coarse-grained soil, a load-transfer plate with a microfriction load for triaxial compression testing in geotechnical engineering was developed by the Yangtze River Scientific Research Institute. Using this, the contact between the soil and loading plate is varied from the whole contact to the distributed contact and from the sliding friction to the rolling friction.  
A schematic of the plate structure is shown in Figure 2a, and a photograph of the physical product is shown in Figure 2b. The load-transfer plate is a 300-mm-diameter, 60-mm-thick disc that consists of a circular load-bearing plate, a sliding block stop bar, guide rails, steel balls, and sliding blocks. The sliding block stop bar is placed in the middle of the circular load-bearing plate. The plate has 24 rectangular grooves that are radially and evenly distributed in the circumferential direction. A guide rail with an arc-shaped groove is embedded in each rectangular groove. Steel balls are arranged in arc-shaped grooves, and multiple turns of sliding blocks are arranged on the guide rail in the radial direction of the circular load-bearing plate. The sliding blocks can slide freely on the guide rail in the radial direction through contact between the steel balls and the guide rail. In this mechanical structure, multiple turns of automatic sliding blocks are used to apply an axial load to a specimen. The steel balls that connect the sliding block and the circular load-bearing plate convert the sliding friction to rolling friction. As a result, there is no or minimal friction (microfriction) in the tangential direction of the contact interface between the specimen and the load-transfer plates. Thus, the specimen can freely deform in the tangential direction of the contact surface, effectively solving the end-restraint problem. Therefore, triaxial shear tests of coarse-grained soil with end-restraint and end-free (microfriction) conditions can be carried out with this device and the YLSZ30-3 high-pressure triaxial shear apparatus.  
Figure 1: The large-scale triaxial shear apparatus.
## 5 Results and Discussion  
### Case-I Building  
From NTHA outputs, it is observed that the allowable story drift of the governing performance level (i.e., 4 percent) is not exceeded for all ground motions (Figure 7). Design drifts determined from the design displacements are not exceeded when compared to the mean NTHA drifts. Mean NTHA drifts are low compared to design drifts though the mean plus one standard deviation and design drifts are approximately equal (Figure 7). The NTHA drifts tend to follow design drift profile; thus, the method produces a design that reflects the design drift (Figure 7). It is observed that NTHA drifts are nearly the same for all frames. The fact that design drifts are being exceeded for some ground motions is acceptable as long as the mean NTHA drifts from the eleven ground motions did not exceed the allowable drifts. Since the design by the proposed method is performed using the design response spectrum which corresponds to the mean response spectrum of the eleven ground motions, it is expected that some ground motions result in the design drifts being exceeded. The NTHA displacements are below the design displacements, except for one ground motion. Mean plus one standard deviation NTHA displacements are closer to the design displacements at the lower-half stories compared to the top-half stories (Figure 8). Mean NTHA displacements and drifts are almost identical for all frames (Figure 9). For example, the difference in 20 th-story mean NTHA displacements at the extreme frames A and E is 0.35 percent. This signifies that the proposed method works well for plan-asymmetric buildings when the plan configuration is identical throughout the height.  
Figure 7: Case-I drift. (**a**) Case-I frame A’s drift; (**b**) Case-I frame B’s; (**c**) Case-I frame C’s drift; (**d**) Case-I frame D’s drift; (**e**) Case-I frame E’s drift; (**f**) Case-I center of mass’s drift.  
Figure 8: Case-I story’s displacements. (**a**) Case-I frame A’s displacement; (**b**) Case-I frame B’s displacement; (**c**) Case-I frame C’s displacement; (**d**) Case-I frame D’s displacement; (**e**) Case-I frame E’s displacement; (**f**) Case-I center of mass’s displacement.  
To check whether allowable hinge rotations for the CP performance level are exceeded, usage ratio graphs of Perform-3D V.7 are used. Usage ratio graphs depict the ratio of hinge rotation demand of the ground motions to the allowable hinge rotation through time. An average of 0.8 is achieved for the beams' usage ratio as shown in Figure 10a-c. For the columns' usage ratio an average of 0.2 is obtained, which is predominantly from hinge rotations at the base of the building (Figure 10d-f). It can be said that from the hinge status point of view, the procedure produces economical design and a desirable collapse mechanism (i.e., a beam-sway mechanism.)
## 4 The Proposed Measuring Method
### Image Pre-Processing  
Initially, the captured RGB mode image was converted to grayscale using the following equation.  
\[F(i,j)=0.30R(i,j)+0.59G(i,j)+0.11B(i,j) \tag{1}\]  
where \(R(i,j)\), \(G(i,j)\), and \(B(i,j)\) are intensity values of the R, G, and B components of point \((i,j)\). A grayscale image of a spool with eight rib plates is shown in Figure 6a.  
View transfer was then applied to reconstruct the front view image of the spool by perspective-transform principle using Equations (2) and (3) [36]. The process is shown in Figure 6b.  
\[\left(\begin{array}{c}x^{\prime}\\ y^{\prime}\\ w^{\prime}\end{array}\right)=\left[\begin{array}{ccc}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{array}\right]\left(\begin{array}{c}u\\ v\\ 1\end{array}\right) \tag{2}\]  
\[\left\{\begin{array}{c}x=x^{\prime}/w^{\prime}\\ y=y^{\prime}/w^{\prime}\end{array}\right. \tag{3}\]  
where (\(x\),\(y\)) is the position of the point in the new coordinate corresponding to (\(u\),\(v\)) in the original coordinate. Equation (2) is usually abbreviated as \(\mathrm{X}^{\prime}=\mathrm{RX}\), where R is a homogeneous matrix calculated from given transform parameters.  
The ideal ROI (region of interest) is an annulus area concentric with the center bore. To simplify the computation, a minimum enclosing rectangle range was intercepted as the real ROI, as shown in Figure 6c.
# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 5 Conclusions  
In this paper, we explored and demonstrated the importance of fish semantic segmentation in fish school feeding behavior quantification. A FSFS-Net algorithm was proposed to achieve two-class pixel-wise classification in fish feeding image. Especially, the SPSA module designed was able to capture long-range dependencies from the feature in an image. Moreover, we raised an effective LMSM that could extract multi-scale features to learn contextual information. The experimental results show that the proposed method achieved a performance of 79.62% mIoU score on annotated fish feeding dataset, surpassing other semantic segmentation algorithms such as U-Net, SegNet, FCN, DeepLab v3 plus, GCN, HRNet-w48, DDRNet, LinkNet, BiSeNet v2, DANet, and CCNet. The competitive performance on fish feeding datasets shows that our method proposed can contribute to quantifying fish school feeding intensity. In the future, we will design a network that can obtain accurate edge information from the low stage while simultaneously obtaining semantic information from the high stage, thereby eliminating some of the original edge's lack of semantic information. Moreover, we need to conduct further research to validate our algorithm against diverse fish species, number of fish, fish maturity, and tank design.  
Conceptualization, L.Y.; Methodology, L.Y.; Validation, L.Y.; Writing--Original draft preparation, L.Y.; Supervision, Y.C. and T.S.; Writing--Reviewing and Editing, D.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China 'Analysis and feature recognition on feeding behavior of fish school in facility farming based on machine vision' (No. 62076244), the Beijing Digital Agriculture Innovation Consortium Project (No. BAIC10-2022), Yunnan Fundamental Research Projects (No. 202301 AV070003), the Yunnan Reserve Talents of Young and Middle-aged Academic and Technical Leaders (No. 2019 HB005), the Yunnan Young Top Talents of Ten Thousands Plan (No. 201873), and the Major Science and Technology Projects in Yunnan Province (No. 202002 AB080001-8).  
Not applicable.  
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.
## 4 Experimental Results and Analysis
### Comparison with Baseline
Compared with the baseline methods, the accuracy of our proposed model is significantly improved. This is because the BERT module and GCN module of the model extract semantic features and co-word features, respectively, to provide support for downstream classification tasks. As a text classification model with a simple network structure, TextCNN cannot effectively extract classification features from short texts such as enterprise names. Therefore, the accuracy rate of TextCNN decreases significantly in the case of low data quality (Zhuji City). Bert-FC provides semantic features for the fully connected layer through the pre-trained model BERT, but lacks the features of co-word relations, so its classification effect is inferior to Word2 Vec-GCN and BERT-GCN. Although TextGCN improves the accuracy of classification by using the graph structure, it still lacks sufficient semantic information. Compared with Word2 Vec-GCN, BERT-GCN adopts a more advanced semantic representation method, and the classification results also reflect the fact that BERT is more effective than Word2 Vec. The analysis of the classification results showed that some Class F (wholesale and retail trade) enterprises can be easily categorized as Class C (manufacturing), such as \"Zhuji Zhongfu Chemical Fiber Co., Ltd.\" and \"Zhuji Tianhai Powder bead Breeding Co., Ltd.\" By analyzing the main business information of these enterprises, we found that their business scope is relatively wide, and some of these enterprises integrate manufacturing and sales. Their naming conventions also tend to lean towards those of manufacturing enterprises. Our model had difficulty classifying these enterprises, but in terms of water usage statistics, the water usage of these enterprises is similar to that of the manufacturing industry. Therefore, these errors will not affect downstream water usage analysis.
## 2 Materials and Methods
### Instrument Conditions
#### 2.5.3 UHPLC-Q-TOF-MS Analysis  
The samples were analyzed using AcquityTM UHPLC chromatography system coupled to a Xevo G2 QTOF mass spectrometer (Waters, Manchester, UK). The mass spectrometer was equipped with an atmospheric pressure ionization (API) source operating with an electrospray ionization (ESI) interface. The detector configuration includes a hexapole, a quadrupole, a collision cell, and a time of flight as analyzer (QTOF). Chromatographic separation was performed using an ACQUITY UPLC(r) BEH C18 column (2.1 \(\times\) 100 mm, 1.7 \(\upmu\)m particle size, Agilent Technologies Spain, S.L., Madrid, Spain) at a flow rate of 0.3 mL/min. The gradient elution was performed using ultrapure water with 0.1% formic acid as mobile phase A and methanol with 0.1% formic acid as mobile phase B. The gradient started at 5% phase B and increased linearly to 100% phase B over 15 min, followed by a 2 min re-equilibration to the initial conditions. The injection volume was 10 \(\upmu\)L. Electrospray ionization (ESI) was operated in positive mode (ESI+), and data were acquired in the sensitivity mode with a capillary voltage of 1 kV and a sampling cone voltage of 30 V. Leucine-enkephalin [M+H]\({}^{+}\), \(m/z\) 556.2766, was used as the lock-mass compound for real-time mass correction. Data acquisition was performed in the data-independent acquisition (DIA) mode using high-definition MSE, which simultaneously collects data at a low collision energy (6 eV) and elevated collision energy (ramped from 20 to 40 eV). Argon was employed as the collision gas, while nitrogen served as the ion mobility separation (IMS) gas. The IMS gas flow rate was set at 25 mL/min, with a wave velocity of 250 m/s and an IMS pulse height of 45 V. Following the completion of the migration assays, the simulant samples were extracted and analyzed using UPLC-IMS-QTOF. Calibration curves for quantitation were prepared using standard solutions in ethanol. The structural identification of the detected peaks was achieved through UPLC-Q-TOF-MS analysis, utilizing accurate mass measurements. Molecular structures were determined using the ChemSpider chemical database (Royal Society of Chemistry 2025) by comparing the experimentally obtained accurate masses.
## 3 Results
### Simulations in Semi-Infinite Phantoms
#### 3.1.3 Errors from Theoretical Fits  
In Figure 4, retrieved values of the absolute Brownian flow coefficients (from each fit) are shown as contour maps from 35 simulations (spanning 7 absorption and 5 scattering coefficients) with fixed Brownian flow coefficient \(D_{B}=2\times 10^{-8}\) cm\({}^{2}\)/s, for three different SDS (Figure 4a: SDS = 0.15 cm; Figure 4b: SDS = 0.45 cm; Figure 4c: SDS = 0.75 cm). With decreasing SDS, the retrieved values of \(D_{B}\) increased from its expected (known) value of \(D_{B}=2\times 10^{-8}\) cm\({}^{2}\)/s. Figure 4d-f show the same data as Figure 4a-c, but as percent-errors relative to the true input value (of \(D_{B}=2\times 10^{-8}\) cm\({}^{2}\)/s). As seen before, errors associated with retrieved flow coefficients were highest for small SDS and low albedos. These data also indicate that extracted \(D_{B}\) depended on the optical properties of the media, and the SDS used.  
Figure 5 shows the averaged percent-error for extracted \(D_{B}\) with varying SDS, for the six \(D_{B}\) coefficients simulated (squares: \(5\times 10^{-9}\) cm\({}^{2}\)/s; up-triangles: \(1\times 10^{-8}\) cm\({}^{2}\)/s; circles: \(1.5\times 10^{-8}\) cm\({}^{2}\)/s; asterisk: \(2\times 10^{-8}\) cm\({}^{2}\)/s; diamonds: \(2.5\times 10^{-8}\) cm\({}^{2}\)/s; down-triangles: \(3\times 10^{-8}\) cm\({}^{2}\)/s). For each line (i.e., at fixed \(D_{B}\)), the percent errors at each SDS shown in Figure 5 was the mean across all 35 simulated models (spanning seven \(\mu_{a}\) and five \(\mu_{s}^{\prime}\) values with given \(D_{B}\)), while error-bars are standard-deviations. Abscissa of each line (for each \(D_{B}\)) are shown staggered in Figure 5 to show six distinct markers, since the computed errors (and distributions) were nearly identical across all \(D_{B}\) values. In other words, the percent-error in retrieved \(D_{B}\) was independent of the flow coefficient used and only depended on  
Figure 3: MC simulations (symbols) with fitted curves using Equation (2) (lines) via optimizing for the Brownian flow coefficient \(D_{B}\) (inverse model) to minimize \(\chi^{2}\) residuals. Simulated data shown in (**a,d**) are identical (in the same order) as Figure 2a,d. Data in (**a,c**) are for SDS of 0.15 cm (at low and high absorption coefficients respectively), while data in (**b,d**) are for SDS of 1.5 cm.  
the optical properties of the medium and the SDS used. These data are in agreement with findings published in a recent report that investigated short separation DCS with MC simulations [31].