esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Models Description  
### Fuel Assembly Design  
#### 3.1.1 BWR GE14 10 \(\times\) 10  
The work presented here considered two different fuel assembly designs: (1) BWR GE14 10 \(\times\) 10; and (2) SVEA-100, for the full core and criticality analysis. The BWR GE14 assembly was set as a reference, while SVEA-100 was used to estimate the effect of the FA design change on the results of the analysis. BWR fuel assemblies are often non-uniformly enriched. To simply the modelling approach, the current study used an averaged radial fuel enrichment. Work presented in [15] showed that averaging fuel enrichment only has a small impact on the k\({}_{\text{eff}}\) and isotopic composition in BWR spent fuel. As a real-life fuel enrichment patterns are rarely available in the open literature for the modern BWR fuel assembly designs, that would be of high interest to investigate their behaviour if the operator provides detailed data.  
The fuel in BWR GE14 assembly has a U-235 content of 4.5 wt.%, and gadolinium rods contain in addition 7 wt.% Gd\({}_{2}\)O\({}_{3}\). The fuel assembly consists of 92 fuel rods in total out of which 14 are the part-length rods dividing the FA into two lattices--dominant and vanished, and 15 are burnable poison rods (Figure 1). The assembly design parameters are explicitly described in the first part of this study [22] with the key data summarised in Table 1.
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 3 History of Whole Organ Engineering
### Autologous Engineered Tissues for Organ Resuscitation  
In 2006, [PERSON] et al., published their results from a study after implanting engineered bladders in human patients with end-stage bladder disease from 1999 to 2018, expanding upon previous animal studies that demonstrated the feasibility of implanting de novo bladder tissue in dogs [36]. Patient cells from bladder biopsy samples were expanded into muscle and urothelial cell cultures and then seeded on either a collagen matrix derived from decellularized bladder submuc or a composite bioscaffold composed of collagen and PGA, with some scaffolds of each type being covered in an omental wrap [37]. The seeded scaffolds were incubated for 3 or 4 days before implantation, wherein the engineered bladder was anastomosed to the native bladder with sutures and fibrin glue [37]. Postoperative analysis was done for up to five years to assess maximum capacity pressure, leakage, and histology of tissue samples. These assessments found that the composite bioscaffold wrapped in omentum had the best result in improving patients' urinary continence and bladder compliance and capacity, which was concluded to be because the PGA provided greater structural durability than collagen alone, and the omental wrap enhanced vascularization of the implants [37]. Though an entire organ was not engineered ex vivo, the engineered bladder tissue was structurally and physiologically interchangeable with any segment of the rest of the bladder and was able to regenerate function of failing organs and improve quality of life for patients.
# Deep Vision Multimodal Learning: Methodology, Benchmark, and Trend
## 2 Learning Architectures
### Multimodal Loss Function  
A reasonable loss function design can effectively help the network to train, but there is currently no comprehensive summary of some special loss functions used in multimodal training. This section lists some loss functions specifically designed for multimodal learning, shown in Figure 2.  
**Cross-modal focal loss**[42]. This is an application of focal loss [43] in the multimodal field. Its core idea is that when one of the channels can correctly classify a sample with high confidence, the loss contribution of the sample to the other branch can be reduced; if one channel can completely correctly classify a sample, then the other branch can no longer penalize the model. It shows that another modality mainly controls the loss value of the current modality.  
**Cross-modal center loss**[44]. This is an application of center loss [45] in the multimodal field, which minimizes the distances of features from objects belonging to the same class across all modalities.  
**Cross-modal cycle-consistency loss**[46]. This is used to enforce the semantic alignment between clips and sentences. It replaces the cross-modal attention units used in [47, 48]. Its basic idea is to consider mapping the original modality to the target modality during cross-modal matching and find the matching target modality information with the highest similarity for specific original modality information. The matched target modal information is inversely mapped back to the original modal. Finally, the distance between the mapped value and the original value is calculated.  
**Contrastive alignment loss**[49]. This loss function adopts InfoNCE [50] with reference to contrastive learning. It enhances the alignment of visual and textual embedded feature representations, ensuring that aligned visual feature representations and linguistic  
Figure 2: Multimodal loss function summary.  
feature representations are relatively close in feature space. This loss function does not act on the position but directly on the feature level to improve the similarity between the corresponding samples.  
**Soft cross-modal contrastive loss**[51]. To capture pairwise similarities, HIB formulates a soft version of the contrastive loss [52] widely used for training deep metric embeddings. Soft cross-modal contrastive loss adopts the probabilistic embedding loss in previous contrastive loss, where the match probabilities are now based on the cross-modal pairs.  
**Multiteacher alignment loss**[53]. This is a loss function used in multimodal knowledge distillation [54] that facilitates the distillation of information from multimodal teachers in a self-supervised manner. It measures the distance of the feature distribution of each modality after the feature extraction stage.  
In conclusion, most of the loss functions for multimodal learning are extended by some previous unimodality loss functions, which are complemented according to the cyclic consistency or alignment between modalities.
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
###### Abstract  
Long-haul optical fiber communication employing digital signal processing (DSP)-based dispersion compensation can be distorted by the phenomenon of equalization-enhanced phase noise (EEPN), due to the reciprocities between the dispersion compensation unit and the local oscillator (LO) laser phase noise (LPN). The impact of EEPN scales increases with the increase of the fiber dispersion, laser linewidths, symbol rates, signal bandwidths, and the order of modulation formats. In this work, the phase noise cancellation (PNC) employing a radio frequency (RF) pilot tone in coherent optical transmission systems has been investigated. A 28-Gsym/s QPSK optical transmission system with a significant EEPN has been implemented, where the carrier phase recovery (CPR) was realized using the one-tap normalized least-mean-square (NLMS) estimation and the differential phase detection (DPD), respectively. It is shown that the RF pilot tone can entirely eliminate the LPN and efficiently suppress the EEPN when it is applied prior to the CPR.  
coherent optical fiber communication; laser phase noise (LPN); carrier phase recovery (CPR); phase noise cancellation (PNC); equalization enhanced phase noise (EEPN); radio frequency (RF) pilot tone +
Footnote †: journal: Applied Physics Letters
# Quality-Aware Resource Model Discovery
## 5 Evaluation
### Design  
The case study was conducted at a manufacturing company in Korea producing a variety of semiconductor products. According to the confidentiality agreements, we cannot present the details on the semiconductor manufacturing process utilized for this case study. In short, we can understand it as an ordinary semiconductor manufacturing process consisting of more than 500 sub-activities such as wafer fabrication, oxidation, photolithography, and etching. In addition, the process had a complicated resource network model because of the lengthy process and too many resources involved.  
To evaluate the effectiveness of our approach in the manufacturing process, we answered the following research questions:  
* Is it able to identify the superior and inferior resource paths using the real-life manufacturing event logs?
* Is the model simplification based on the quality of resources useful for a better understanding?  
We extracted a quality-enhanced manufacturing event log from the manufacturing information systems supporting the execution of the corresponding process. The event log included approximately 15,000 wafers, i.e., process instances. From all of the 500 sub-activities, we collected 118 activities to be analyzed with the help of domain experts. In addition, in this case study, we utilized the quality, i.e., qualified outputs of all the produced wafers as quality measurements, and it had a range from 0 to 1.
## 1 Introduction  
The study of implant placement in the presence of a single target  
## 1 Introduction  
An accurate dental implant position is necessary for an implant placement. Conventionally, dental models and radiographic images are used for the diagnostic wax-up, making a radiographic template, planning the implant position, and fabricating a surgical guide. Nowadays, dental implant treatment planning has become more efficient due to the development of digital technology in dentistry, known as computer-assisted implant surgery [1,2].  
Computer-assisted surgery for an implant placement, CBCT (cone beam computed tomography) data, and scanning surface data of the overall morphology of the oral cavity are used to generate a virtual three-dimensional structure of the oral anatomical status in implant planning software. This model represents hard tissues, soft tissues, and vital structures that would be advantageous for implant treatment planning. The operators use the acquired data with an implant planning software program to diagnose and plan the implant position following the final restoration or prosthesis to avoid vital structures as well as to generate a surgical guide [3, 4, 5]. In order to place the fixtures in the correct positions in accordance with the treatment plan, clinicians use a static guidance system, also known as a rigid surgical implant template/guide, to assist them with the procedure [4].  
Therefore, it is important to have an accurate implant surgical guide. With the advancement of digital technology in dentistry, implant planning software programs have been developed and play an important role in fabricating these surgical drill guides. Thus, the accuracy of surgical drill guides relies heavily on the implant planning software programs being used. However, no evidence has been found regarding how surgical guides can be affected by the accuracy and reproducibility of different software programs. Hence, we aimed to compare the accuracy of static surgical guides prefabricated by three different software programs.
# Blockchain and Smart Contracts for Insurance: Is the Technology Nature Enough?
## 3 Blockchain Applications in Insurance: Selected Use Cases  
As illustrated in the previous section, advantages of blockchain are various. A number of enthusiasts already proposed using this technology in various sectors and contexts, including:  
* Government [35], to record in a transparent way citizens' votes, or politicians' programs (for verifying if promises made have been kept) or to enable autonomous governance systems [36];
* Intellectual property [24], to certify the proof of existence and authorship of a document;
* Internet [25], to reduce censorships, by exploiting the immutability of data stored in the blockchain;
* Finance [37], to transfer money between parties without having to rely on banks;
* Commerce [38], to record goods' characteristics as well as their ownership, especially for luxury goods, thus reducing the market of counterfeit/stolen items;
* Internet of Things (IoT) [39; 40; 41], e.g., by exploiting smart contracts to automatically process data coming from sensors, in order to let intelligent machines interact with each other [42] and autonomously take actions when specific situations occur;
* Education [43], to store information on qualifications acquired by learners, e.g., to reduce job application frauds; in this context, multiple actors (e.g., universities, training institutions, etc.) could write qualifications achieved by a person on the blockchain; human resources staff could then easily obtain information about when and where a given competency was obtained.  
A rather comprehensive overview of applications developed in each of the above sectors can be found in [32; 44]. What should be evident from the above list is that benefits deriving from the adoption of blockchain technology are not limited to a single sector/scenario. Moreover, even within a given sector, blockchain can have different impacts considering the various stakeholders operating in it, their business models, their needs, etc.  
In the following, the attention will be specifically devoted to the insurance sector, where the use of blockchain could positively affect different internal processes (from customer acquisition and management, to frauds prevention, etc.) and could even allow companies to reach new markets [4; 16; 17; 32]. In particular, a selection of use cases that could potentially benefit from blockchain technology will be introduced. For some use cases, prototype implementations have already been developed. In other cases, the use of blockchain has been only analyzed from a theoretical point of view. For each use case, advantages, disadvantages and impact on the insurance domain will be discussed.
## 3 Results
(2012) [35] conducted a pilot study assessing the efficacy of NFT for dyskinesia induced by levodopa in PD patients. Nine participants underwent 24 half-hour sessions targeting increased alpha and low-beta activity (8-15 Hz) and reduced theta (4-8 Hz) and high-beta (23-34 Hz) activity. Although there were no statistically significant changes in motor symptoms (AIMS, UPDRS), quantitative EEG (qEEG) analysis revealed a decrease in high-beta power and an increase in alpha power post-training. [PERSON] et al. (2014) [8] explored the effects of NFT on balance in 16 PD patients, with an average age of 74.23 years in the experimental group. They were assessed for static and dynamic balance before NFT. Participants sat in front of a computer screen while playing video games designed to increase beta1 (12-15 Hz) and reduce theta (4-7 Hz) activity. After eight 30 min sessions over 2.5 weeks, the experimental group showed significant improvement in both static and dynamic balance tests (Biodex, BBS). The control group received random feedback but showed no significant changes. [PERSON] et al. (2021) [36] conducted a single-case feasibility study targeting SMR (12-17 Hz) in a PD patient in the 50s. He underwent NFT both with and without medication. The patient completed NFT over two days, and his motor skills  
et al. (2023) [37] explored the effects of multimodal biofeedback on motor and nonmotor symptoms in PD patients. This study involved 21 participants, divided into three groups: a multimodal (MM) experimental group (feedback from EEG, electrocardiogram (ECG), photoplethysmogram (PPG) and respiratory feedback (RSP)), an EEG-only experimental group and an SHAM control group. Each participant completed five training sessions over 14 days, assessing motor and nonmotor symptoms with various scales (UPDRS II, III; BBS; TUG; HRSD). The results showed that the EEG group showed significant improvement in motor symptoms, while the MM group showed significant improvement in nonmotor symptoms.
###### Abstract
The above investigation results indicated that proper in-time supporting measures consisting of adopting retaining structures and internal supports are able to guarantee the safety and stability of a deep foundation pit with soft surrounding rocks during excavation processes. However, geological conditions are distinct in different areas [18; 19]. Subway station construction in urban areas may also involve dealing with geological conditions such as hard rock strata. For foundation pits with hard surrounding rocks, conventional in-time supporting schemes and sequences for deep foundation pits with soft surrounding rocks are effective enough to control the corresponding displacements during excavation process but may lead to over-support of the foundation pits considering that the surrounding hard rocks have certain strengths and thus the displacement of the surrounding rocks is relatively lower than that of soft surrounding rocks [20; 21]. As a result, conventional in-time supports for foundation pits with hard surrounding rocks could lead to unnecessary costs, increased manpower, and longer construction durations. Therefore, selecting appropriate supporting measurements for foundation pits with hard surrounding rocks is significant in terms of lowering both costs and the length of the construction period. In this investigation, in order to find the most suitable supporting measurements for excavating a deep foundation pit with hard surrounding rocks, a conventional support consisting of a retaining structure and inner supports, which is also usually used for supporting deep foundation pits in soft soils, was adopted. Four different inner supporting schemes were taken into consideration to investigate whether it is necessary to set inner supports, as well as how to set inner supports, for ensuring the safety of deep foundation pits during excavation process. The retaining structure was made of drilled grouting piles. The inner supports included steel bracings and prestressed anchorages. The stability of the foundation pit under the four different supporting conditions was evaluated according to the lateral displacements of the surrounding rocks, settlement of the surrounding ground, the axial forces of the steel bracings, and displacements at the tops of drilled grouting piles, which were determined by means of numerical finite element calculations.
# Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5
## 5 Conclusions  
In response to challenges in concrete image object detection, an enhancement was made to YOLO v5, resulting in the R-YOLO v5 rotational object detection algorithm. This enhancement was carried out through two primary aspects. Firstly, an angular regression variable was introduced, leading to the redefinition of the loss function. Secondly, attention mechanisms, namely PSA-Neck (Positional Self-Attention Neck) and ECA-Layer (Efficient Channel Attention Layer), were seamlessly integrated into the network architecture. This integration took into account the contributions of various node features to the network's performance. Furthermore, the algorithm incorporated skip connections to maintain consistent feature scales across the network.  
The experimental results demonstrate that the R-YOLO v5 detection algorithm achieves an [EMAIL_ADDRESS] of 94.03% for detecting concrete structural cracks, surpassing other algorithms like SASM, S\({}^{2}\)A Net, Re Det, as well as the horizontal bounding box-based YOLO v5 model. Furthermore, R-YOLO v5 exhibits notable advantages in terms of model size (4.17 Mb) and detection speed (0.01 s per image).  
The algorithm proposed in this paper for crack detection offers a higher accuracy and faster detection speed compared to previous research. It can effectively detect concrete bridge cracks in unmanned aerial vehicle (UAV) aerial videos or images, eliminating the need for manual high-altitude inspections and significantly improving detection efficiency. Additionally, the improved strategy for rotating object detection discussed in this study has a certain degree of generality and can provide insights for enhancing other object detection algorithms. However, the current model still has a large computational and parameter load, which increases the training difficulty to some extent. Future work could explore lightweight improvements to the model with the goal of ensuring detection accuracy while enabling deployment on portable devices.  
Methodology, investigation, writing--original draft, visualization and data curation, Y.L.; model experiment, T.Z. and J.X.; writing--review and editing, Y.H.; conceptualization and funding acquisition, Q.P.; provision of study materials, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Cultivation Program for the Guangxi Science and Technology Plan Project of China (No. AA21077011).  
Not applicable.  
The authors declare no conflict of interest.
# Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials
## 4 Numerical Results
### Effect of Multi Interval Parameters  
In practice, the uncertain parameters affecting rotor performance are typically diverse. This subsection investigates the simultaneous effects of seven uncertainty parameters--namely, interval stiffness in supports 1 and 2, interval mass unbalance in Discs 1, 2, and 3, and interval unbalance phase in Discs 2 and 3--on the rotor's steady-state response. The degrees of uncertainty for stiffness and mass unbalance are assumed to be 5%, 10%, and 15%, respectively. Given the significant impact of phase fluctuations on vibration, only the scenario where the phase angle varies within \(\pm 5^{\circ}\) is considered, with the phase of Disc 1 fixed as a reference. Three cases are established based on the degrees of uncertainty for all parameters, and uncertainty analysis is conducted for these scenarios. For clarity, the degrees of uncertainty for all parameters are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Degrees of Uncertainty**} \\ \cline{2-4}  & **Case 1** & **Case 2** & **Case 3** \\ \hline \(k_{1}\) & 5\% & 10\% & 15\% \\ \(k_{2}\) & 5\% & 10\% & 15\% \\ \(u_{1}\) & 5\% & 10\% & 15\% \\ \(u_{2}\) & 5\% & 10\% & 15\% \\ \(u_{3}\) & 5\% & 10\% & 15\% \\ \(\phi_{1}\)When the Chebyshev orthogonal polynomial is of order 3 (\(p=3\)), the relative error \(\varepsilon_{r}\) achieves convergence. Consequently, a third-order Chebyshev orthogonal polynomial is utilized to calculate the range of steady-state response variations of the system under multiple sources of uncertainty and varying parameter intervals. The range of steady-state response for Disc 1, obtained using the proposed method, is illustrated in Figure 11.  
As shown in Figure 11, having multiple uncertainty parameters significantly affects the steady-state response of the rotor. This pattern of influence encompasses the characteristics exhibited by the individual parameters described earlier, including broad resonance bands, peak shifts, and fluctuations in amplitude magnitude.