esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study employs FLAC 3D to construct a numerical analysis model for investigating the impact load on high-piled wharfs. Utilizing this model, the research delves into the dynamic response and damage of reinforced concrete columns under various impact body and impact velocity parameters. The following conclusions are drawn:  
1. Upon distinct computations of structural displacements under equivalent static and dynamic loading, it was observed that the dynamic magnification factor is approximately 1.5. Hence, in the pursuit of structural design for high-piled wharves or when examining and investigating ship impacts on such structures, due consideration should be given to the consequential effects of dynamic amplification resulting from ship impacts.
2. The impact loads significantly influence the bending moments of the pile foundation, particularly impacting inclined piles more severely. However, the effect of ship impact on the axial force of the pile foundation is relatively minimal. As for the beam, ship collision has a negligible effect on its bending moment but notably influences its axial force.
3. Based on the maximum tensile stress criterion, it was found that a 20,000-ton vessel colliding with a high-piled wharf at 0.2 m/s causes concrete cracking in the inclined pile top area. A 40,000-ton vessel at the same speed results in concrete cracking in multiple piles, while a 20,000-ton vessel at 1 m/s induces cracks along each pile body, with several piles reaching the yield stage, causing significant structural damage. Accidental berthing of vessels, especially those exceeding the design capacity, results in more severe damage to the high-piled wharf structure.
4. In finite element simulations, modal flexibility can accurately identify the location of damage, with larger changes in modal flexibility corresponding to greater damage severity. Modal flexibility qualitatively reflects the extent of structural damage, providing a theoretical foundation for the application of damage identification in high-piled wharf pile foundations.  
Figure 37: Modal flexibility variation for different finite element conditions. (**a**) Unit 6; (**b**) Unit 12.  
**Author Contributions:** Methodology, X.X. and X.L.; Software, Y.Z.; Formal analysis, C.H. and X.L.; Investigation, X.X. and C.H.; Resources, X.D.; Data curation, X.D. and X.L.; Writing--original draft, A.L.; Writing--review & editing, X.X. and Y.Z.; Visualization, A.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors 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.
# Ship Route Oil Spill Modeling: A Case Study of the Northeast Brazil Event, 2019
###### Abstract  
In this study, we investigate the circulation and chemical processes associated with the deposition of the largest oil spill that reached the northeast coast of Brazil during the second half of 2019. Using the Oil Spill Contingency And Response model (OSCAR), we performed both deterministic and probabilistic simulations of oil spills from tanker ships that were present in the sea in the region at the time. The study used a dataset comprising the latitudinal distribution of oil sightings along the coast between 31 August and 2 December 2019 (box plot analysis) provided by the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). The total amount of oil that reached the coast during this period (approximately 5000 tons) and the date and location of the first sighting (30 August, in the southern part of the state of Paraba (PB)) were also used as parameters to assess the results of the 31 simulations conducted for ships on route near the area of interest between July and August 2019. The results indicate that a leak having occurred through a mobile source is the most plausible hypothesis for explaining the observed temporal-spatial arrival of the oil leaks along the Brazilian coastline. We suggest that prevention, monitoring, and international cooperation are essential for reducing the risks of future environmental accidents of the kind analyzed in this study and to protect the environment and communities affected.  
OSCAR; oil weathering; oil mass balance +
Footnote †: journal: Applied sciences  
Article
## 1 Introduction  
There is increasing global attention on the quality and freshness of food products. The staple food group rich in essential nutrients are fresh and minimally processed fruit and vegetables, which contain an abundance of vitamins, minerals and biologically active substances preserved despite the exclusion of heat treatment [1; 2; 3]. The market for these goods is growing especially in rich countries. These consumers expect shortening of meal preparation time and the availability of semi-finished products or dishes for direct consumption based on vegetables and fruits (ready-to-eat). An innovative group of products created as a result of consumer expectations are ready-to-eat products subjected to minimal processing limited to cutting or pre-mixing (fresh-cut) [4; 5; 6; 7].  
According to the recommendations of the World Health Organization (WHO), the consumption of vegetables and fruits for an adult is assumed to be at least 400 g/day. Some countries recommend even higher doses, for example, the German Nutrition Association recommends a daily consumption of 250 g of fruit and 400 g of vegetables, excluding potatoes, sweet potatoes, cassava and other starch roots. These products are a source of many antioxidants (vitamins C and E or \(\beta\)-carotene) necessary for maintaining health, proper growth and development, especially in children [8; 9]. The consumption of low-processed fruit and vegetables in the USA covers about 48% of the market [10]. The content of minerals, vitamins and health-promoting substances such as antioxidants can contribute to the prevention of many civilisation diseases, such as cancer or heart disease [11; 12]. Consumers are looking for healthy food but also food convenient to use. This results from a change in lifestyle dictated by a large amount of time spent at work and the increasing number of people living alone. In order to adapt to such a mode, the product must save time in meal preparation; be pre-processed (washed, cut or peeled) and be suitable for storage for a longer period of time. Depending on the product, this is most often 4-7 days, but there are products that can be stored for up to 21 days [13; 14; 15; 16].  
Low-processed food, despite its nutritional value, is highly exposed to microbiological contamination, as well as less resistant to cold storage than other processed food. It is therefore important to ensure the best possible quality of the initial product and appropriate storage conditions.  
In order to retard vegetables' and fruits' adverse physiological changes, specially developed edible coatings are used together with additives such as herbs, essential oils or other active substances. It is also worth noting that about 30% of horticultural products such as fruit and vegetables are infected with pathogens, pests and other microorganisms not only during transport and storage but also during processing, as those commodities are especially prone to all kinds of microbial contamination. This may also be influenced by many pre-harvest factors that can impact or decrease susceptibility to spoilage [17; 18; 19; 20].
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 3 Algorithm Framework and Modules
### Attention Convolutional Sparse Codec  
To obtain the local optimal solution of the objective function, this study selects a Convolutional Sparse Coding iterative model [19], where \(z_{0}=0,k=0,1,\ldots,K-1\). The iterative update formula for the model is as follows:  
\[z_{k+1}=S_{\theta}(z_{k})+w_{e}*x-w_{d}*z_{k}, \tag{5}\]  
where \(w_{e}\in\mathbb{R}^{s\times s\times c\times m}\), \(w_{d}\in\mathbb{R}^{s\times s\times m\times c}\), and \(\theta\in\mathbb{R}^{m}_{+}\) are fully trainable variables. LCSC can quickly update the dictionary filter parameters through iterative training, which is suitable for convolutional neural network architectures. In multimodal image fusion applications, the dictionary convolution filters employed in this method face several challenges. First, the convolutional dictionary is overly simplistic, which limits its effectiveness. Furthermore, its  
Figure 2: The overall framework and processing pipeline of ICSF.  
robustness is lacking, making it difficult to adapt to the complexities of multimodal scenes. In addition, relying on single-layer convolutions is inadequate for capturing deep features and can result in overfitting during training.  
The self-attention mechanism from the Transformer model [20] is introduced to address these issues. This mechanism designs the Attention Dictionary Block (ADB), which replaces the dictionary convolution filters. The ADB module can extract local features through a multi-objective self-attention mechanism and record attention channel information by channel encoding. This module contains three dictionary filters: \(w_{q}\), \(w_{k}\), and \(w_{v}\), where \(w_{q}\) and \(w_{k}\) are used to extract attention channel information, and \(w_{v}\) is used to extract content information. Given the input \(x\in\mathbb{R}^{l\times c}\), the implementation can be formalized as follows:  
\[W_{ADB}=\lambda(x*w_{q})\times(x*w_{k})^{T}\times(x*w_{v}), \tag{6}\]  
where \(\times\) indicates the matrix multiplication operation, and \(\lambda\) is the normalization parameter. The introduction of ADB increases the computational complexity of a single iteration. However, its attention channel encoding can rapidly promote content encoding sparsity. As a result, ADB, as a dictionary filter, significantly reduces the number of iterations, decreases the number of parameters, and improves the encoding quality. The overall structure of the codec module is shown in Figure 3.
## 3 Methods
### Graph Partitioning  
We now address another ingredient of our combination of techniques, which posses both conceptual as well as technical significance in our results. A fundamental challenge in graph neural networks is the need for a large space to store the graph and the representation vectors created for each node. To address this issue, one of our contributions is to apply Cluster-GCN algorithm [23], which partitions the graph into smaller clusters, as will be explained below. The Cluster-GCN algorithm utilizes a graph-clustering structure to address the challenge posed by large-scale graph neural networks. In order to overcome the need for extensive memory and storage for the graph and its node representation vectors, the algorithm divides the graph into smaller subsets by using graph-clustering algorithms like METIS [24]. METIS aims to partition the graph into subgraphs of approximately equal size while minimizing the edge connections between them. The process involves graph coarsening, wherein vertices in the original graph are merged to create a smaller yet representative graph for efficient partitioning. After generating the initial subgraphs using the graph partitioning algorithm, the algorithm refines the partitioning in a recursive manner by the application of an uncoarsening algorithm. This recursive process propagates partitioning information from smaller to larger levels while maintaining the balance and size of the subgraphs. By dividing the graph into smaller clusters, the model's performance improves in terms of computational space and time. The decision to employ graph-clustering methods is driven by the aim to create partitions wherein connections within each group are strong, capturing the clustering and community structure of the graph effectively. This approach is particularly beneficial in node embeddings, as nodes and their neighbors usually belong to the same cluster; it enables efficient batch processing.
## 2 FlexRay Communication Protocol  
As shown in Figure 1, the communication of FlexRay network is in a cyclical manner. Cycles on the bus are counting by cycle counters where its range is set from 0 to the maximum number of the system configuration gCycleCountMax. Each communication cycle consists of four parts. They are static segment (ST), dynamic segment (DS), symbol window (SW), and network idle time (NIT). Among them, static segment transmits periodic messages using time division multiple access (TDMA) technology and suitable for high-confidence and security-critical communications [5; 6; 7].  
The FlexRay static segment consists of multiple time slots with the same size. The slot number is recorded by the static slot counter when the signal of frames is transmitting. A FlexRay frame is consists of the header segment, the payload segment, and trailer segment [8; 9; 10]. As shown in Figure 2, the length of valid data segment is 0-254 bytes. When a frame transmits over the bus, some bits are needed to encode it. Transmission start sequence (\(TSS\)) accounts for 3-15 bits, and frame start sequence (\(FSS\)) for 2 bits where byte start sequence (\(BSS\)) should be added at the front of every byte. Frame end sequence (\(FES\)) accounts for 2 bits. Communication idle delimiter (\(CID\)) takes another 11 bits and action point offset (\(APO\)) accounts for 1-63 macrotick (MT) [11; 12; 13].  
Then, the length of frame is expressed as:  
\[L_{FL}(j)=TSS+FSS+80+20w_{j}+FES \tag{1}\]  
Figure 1: FlexRay communication cycle. ST, static segment; DS, dynamic segment; SW, symbol window; NIT, network idle time.  
Figure 2: FlexRay message frame format.  
where: \(L_{FL}\) is the length of the message frame. Eightty bits is the sum of the header segment and the trailer segment.  
Static slot length can be expressed as:  
\[L_{ST}(j)=2\times APO+ceil\Big{(}\big{(}((L_{FL}+CID)\times t_{bit}+MinPD+MaxPD )(t_{MT}\times(1-CDM))^{-1}\big{)} \tag{2}\]  
where \(L_{ST}\) is the length of the static time slot in MT (Macrotick); \(ceil\) is the top integral function. \(t_{bit}\) is the time to transfer each bit; \(MinPD\) (Min Propagation Delay) is the minimum of propagation delay; \(MaxPD\) (Max Propagation Delay) is the maximum of propagation delay; \(t_{MT}\) is the duration of each macro \(t\); \(CDM\) (Clock Deviation Max) is the maximum of clock deviation (0.0015).  
It is assumed that there are \(i\) nodes in FlexRay bus, the \(FID\) number of each node is denote as \(FID_{e_{i}}\), (\(i=1,2\ldots n\)). The total number of \(FID\) of \(i\) nodes can be expressed as:  
\[FID=\sum_{i=1}^{n}FID_{e_{i}} \tag{3}\]  
Bandwidth utilization indicates how many of the allocated slots in the static segment are used to transmit signal [14]. Because of the different length of the FlexRay static message, the static slot length is determined by the length of the longest static message. Therefore, the purpose of FlexRay static segment scheduling is to ensure periodic message transmitted while minimizing the number of FIDs and maximizing bandwidth utilization.
# Experimental Study on the Torsional Behaviour of Prestressed HSC Hollow Beams
## 1 Introduction
From the foregoing, experimental studies on the behaviour of prestressed hollow beams under torsion are needed, in particular for HSC beams. This article presents an experimental study on the global behaviour of HSC hollow beams with uniform longitudinal prestress. The beams were loaded under pure torsion and tested up to failure. As for the previous referred studies, a uniform longitudinal prestress was applied. This was considered to be sufficient to give some indications on the influence of prestress on the behaviour of HSC hollow beams under torsion.
## 3 Strong Coupling Regime: Analytical Approximation
### Tunneling Current  
Following the general presentation of tunneling current in terms of probability amplitudes [1], we have for RO  
\[J_{T,p}=i\frac{\xi}{2}\Big{[}t_{a}\big{(}A_{p+1}-A_{p-1}\big{)}A_{p}^{*}+t_{b }\big{(}B_{p+1}-B_{p-1}\big{)}B_{p}^{*}\Big{]}+c.c.\] \[=-2e\sin\varphi\Big{(}t_{a}\big{|}A_{p}\big{|}^{2}+t_{b}\big{|}B_ {p}\big{|}^{2}\Big{)} \tag{21}\]  
For the case of a completely excited initial state, we obtain using (18) and (19):  
\[J_{T,p}=-\frac{2e\sin\varphi}{\Big{(}1+\Lambda^{2}\Big{)}^{2}}\bigg{[}t_{a} \Big{(}1+\Lambda^{4}+2\Lambda^{2}\cos\Omega t\Big{)}+4t_{b}\Lambda^{2}\sin^{2 }\frac{\Omega t}{2}\bigg{]} \tag{22}\]  
In this case, the tunnel current consists of two components: dc-current, and the component on the RO frequency (20). Its physical origin is similar to the so-called low-frequency RO in the two-level quantum systems with broken inversion symmetry [10]. As was predicted in [10], it leads to non-zero diagonal matrix elements of dipole moment. It is impossible for real atoms (excluding hydrogen), but implementable for artificial atoms (for example, self-organized semiconductor quantum dots). As a result, the inter-level optical transitions are accompanied by the intra-level periodical movement with Rabi-frequency, which is responsible for low-frequency radiation. It exists only in the case of asymmetry, which manifests itself in the non-identity of diagonal dipole matrix elements. In our case,the intra-band movement and correspondent asymmetry are associated with interatomic tunneling and different penetration values (\(t_{a}\
eq t_{b}\)), respectively.
# A Survey on Deep Learning in 3D CAD Reconstruction
## 7 Datasets
### ABC Dataset  
[PERSON] et al. [8] proposed the ABC dataset. Since a large number of datasets at that time were composed of acquired or synthesized point clouds or point grids, they did not meet the requirements of ground truth signals or parameter representation. Therefore, a new CAD model dataset, the ABC dataset, was proposed. It meets the requirements for a large size, ground truth signals, parameter representation, and scalability. It contains sufficient similar samples, diverse shapes, and high-quality ground truth values.  
The ABC dataset [8] was created by collecting and creating Onshape, a large online collection of CAD models, and it can be used to study geometric deep learning methods and applications. The ABC dataset contains more than 1 million individual and high-quality CAD models. The parametric description of the sampled surfaces and curves in the models can generate data in different formats and resolutions so that various geometric learning algorithms can be compared fairly. Since the ABC dataset is a proprietary format provided by the Onshape CAD software, it has some additional building information; however, the missing information needs to be retrieved by querying the Onshape API (Onshape API version 2024).
# Surrogate-Based Optimization of a Centrifugal Pump with
## 2 Problem Formulation  
The operating conditions (see Table 1) of the pump to design are provided by the Italian company Industrie Saleri Italo S.p.A. ([[http://www.saleri.com/](http://www.saleri.com/)]([http://www.saleri.com/](http://www.saleri.com/)), accessed on November 2021), as a representative working point for a high performance gasoline car.  
The objective is to design a pump that satisfies the operating conditions and maximize the hydraulic efficiency. In particular, the design must guarantee a prescribed total pressure rise \(\Delta p_{t}\) to ensure the proper flow rate in the cooling circuit. \(\Delta p_{t}\) is equal to \(p_{t4}-p_{t0}\), where \(p_{t4}\) is the total pressure at the volute outlet and \(p_{t0}\) the total pressure at the suction pipe. The hydraulic efficiency is defined as follows:  
\[\eta=\dot{Q}\Delta p_{t}/W, \tag{1}\]  
where \(\dot{Q}\) [m\({}^{3}\)/s] is the volumetric flow rate and \(W\) [\(W\)] the power at the impeller. The total pressure head value reported in Table 1 is constrained in the range \(\pm\)5% during the optimization process.  
The geometrical constraints, in general, are stringent and can be ascribed to packaging limitations and to feasibility requirements. The packaging constraints vary with the working point and the application, e.g., the maximum impeller diameter varies in a range from 30 to 90 mm. Table 2 summarizes the constraints for this case. The feasibility requirements depend on the manufacturing technology. The molding, mostly used for the pump manufacture, requires a minimum blade thickness of 2 mm and a minimum outlet blade angle.
## 2 Materials and Methods
### Mass Spectrometry Analysis
Regardless of the sampling method, a weighted amount of LO (1.8 g) was introduced in a 4 mL vial without any previous pretreatmentand let equilibrate with its vapors in a 50 \({}^{\circ}\)C thermal bath for 5 min under a stirring speed of 200 rpm (equilibration step). Subsequently, according to the static-HS-GC-MS method, 100 \(\upmu\)L of sample HS was drawn into a gas-tight syringe and directly injected into the gas chromatograph. Alternatively, the equilibration step was followed by the solid-phase micro-extraction (SPME) of the sample headspace aimed to selectively preconcentrate the volatile metabolites (extraction step). The medium polarity DVB/CAR/PDMS triple-coated fiber was selected for its wide range of polarity, making it the most suitable fiber for an untargeted analysis [34]. The SPME fiber was exposed to the oil headspace for 50 min at 50 \({}^{\circ}\)C under a stirring speed of 200 rpm [35]. HS-SPME method presents a number of advantages over other techniques, being extremely sensitive in unveiling even traces of low- or medium-polarity VOCs crucially contributing to the sensory properties of an edible matrix that, as in the presented case, is proposed to be included in a pharmaceutical preparation. Moreover, the exclusive extraction of VOCs makes this analysis extremely clean and free from solvent contaminants [36]. The gaseous analytes were separated through an Agilent Technologies 6850 gas chromatograph coupled with an Agilent Technologies 5975 mass spectrometer, equipped with the non-polar capillary column HP-5 MS (30 m \(\times\) 0.25 mm inner diameter, film thickness 0.25 \(\upmu\)m). The gas-chromatographic parameters were set as follows: inlet temperature, 260 \({}^{\circ}\)C; injection mode, split (split ratio 40/1); flow rate of the helium carrier gas (99.995% purity), 1.0 mL/min; oven temperature starting from 40 \({}^{\circ}\)C, after 5 min raised to 200 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, and kept at this final temperature for 30 min. Mass spectrometry parameters were set as follows: EI energy, 70 eV; source temperature, 230 \({}^{\circ}\)C; quadrupole temperature, 150 \({}^{\circ}\)C; and mass scan was carried out in the 50-350 \(m/z\) range. All the analyses were performed in triplicate. Mass and chromatographic data were crossed to support the analytes identification. The experimental EI spectra were compared with those collected in both commercial (FFNSC 3) and free online databases (NIST 11, Flavor2). Kovats index (KI) was then used as the further parameter to confirm the MS-based identification of the analytes. KIs were measured using a mixture of n-alkanes (C7-C40) with the same chromatographic setup, and then compared with values reported in the mentioned database and/or with data reported in the literature. The quantitative analysis was performed automatically, integrating the GC/MS peak areas and calibrated by correction factors that relied on an internal standard.