esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work  
BH has emerged as a promising technique for enhancing the flexibility and efficiency of satellite communication systems. Its performance benefits have been extensively validated in the literature. Specifically, [11] quantifies the throughput improvement of BH over fixed multi-beam illumination at the link-level, while [12] further substantiates these gains at the system-level by explicitly accounting for inter-beam interference. Building upon these foundational studies, [13] characterizes BH-based resource management as a multidimensional optimization problem involving joint time-slot scheduling and frequency-power allocation.  
In the domain of beam scheduling, with the growing density of satellite constellations, especially in LEO, the simultaneous coverage of overlapping ground areas by multiple satellites from different orbits has introduced new challenges for beam coordination and interference mitigation [14]. To support dynamic and uneven traffic demands while minimizing inter-beam interference within a satellite system, coordinated beam steering and resource scheduling have attracted growing research interest [15]. For instance, [16] proposes a two-stage approach that jointly minimizes the number of active beams and balances user load under a fixed power constraint, using graph-theoretic beam clustering and K-means-based user grouping. Similarly, [17] formulates beam placement as a multi-objective optimization problem and introduces a genetic algorithm to obtain Pareto-efficient trade-offs between beam count and spectrum usage. In [18], the authors further extend this line of work by jointly optimizing beam locations and transmitting power using both iterative and deep learning-based solutions, significantly improving computational efficiency.  
In parallel, research on frequency and power allocation has progressed along a complementary track. The authors of [19] treat spectrum assignment and power control as separate optimization subproblems. In [20], the authors propose a greedy algorithm for frequency and power allocation in multi-beam LEO systems. The authors of [21] introduce an automatic power control (APC) mechanism to mitigate co-linear interference in the uplink and downlink between LEO and GEO systems, thereby effectively reducing inter-system interference. The authors of [2] presented a joint beam management and power allocation framework focused on maximizing GEO system throughput while ensuring continuity for LEO links. Furthermore, [22] presents a frequency and power allocation strategy tailored for multi-beam systems, designed to meet non-uniform user distribution and traffic demands under constrained power budgets, while exploring the trade-off between total system capacity and user fairness. In [23], the authors optimize subchannel and power allocation in a multi-beam satellite communication system to meet traffic requirements, while simultaneously minimizing power consumption and bandwidth overhead.
## 5 Operational Descriptions of the Future Distribution Networks with the Sundom Smart Grid
### Energy Management Use Cases
#### 5.1.2 Energy Management Use Cases of the Microgrid Phase  
The microgrid is considered to be formed within the MV distribution network area. The energy management HL-UCs of the microgrid phase are similar to the self-sufficient phase. In the microgrid phase, customer types of the previous evolution phase exist (consumers, responsive consumers, prosumers) and a new customer role, the partners. Partners own the previous customer type characteristics, but they also have controllable dynamic loads, and they are active in AS programs through their resources. The dynamic loads are the EV-BESS of the vehicle-to-grid (V2G) and vehicle-to-home (V2H) type of the EVs. The V2H type can supply electricity to a small microgrid such as a house and act as a power source during a power outage (or mobile outdoors). EV aggregators can provide services to the base and the peak power markets, the AS markets (reserve market), and offer additional storage and back-up power [87]. Hence, participation in the AS markets is a regular operation. The EV client types in the suburban area are assumed to be type 3 or 4, according to [87]. Type 3 EV owners provide the EV-BESS as a controllable load, in which charging location and time are known. Type 4 EV owners provide the EV-BESS as a controllable resource, V2G, assuming that the aggregator has contracted a specified amount of battery state-of-charge (SOC). The EV owner defines the charging volume, the available period of charging, the minimum battery SOC level for the next hours (or days). The EV aggregator gathers real-time information about the status, frequency, total capacity, and voltage of the controllable area. The micro and the small-scale generation units are connected to the utility network by the devices and contracts, which allow the two-way power flow with payment of the energy surplus. The common CHP unit could be deployed to provide various ASs such as intraday balancing services, improve power quality, and provide black start services. The CHP unit could be used for DR purposes by controlling the power output (and the water temperature) by the energy management system (EMS) based on the DR optimization problem. In the future, the CHP unit could be coupled to a heat storage system, whereas it could provide flexibility in a generation such as is presented in [88]. Moreover, a higher-level control system is required, responsible for the regional DSM or DR, the energy and power balance management, and the protection management both in the grid-connected and the islanded operation modes. In this paper, the microgrid management system (MMS), including a microgrid energy management system (MEMS) and a microgrid protection management system (MPMS), is considered, of which functions are analyzed in [43]. The HL-UCs for energy management of the LV and the MV distribution networks in the microgrid phase are described in Table 4. Only the new events are presented compared to the previous, the self-sufficient phase.
# A Review on Ultrafast Laser Enabled Excellent Superhydrophobic Anti-Icing Performances
## 4 Advances and Challenges of ULSASs
### Passive Superhydrophobic Anti-Icing Surfaces
#### 4.1.1 Delayed Icing/Frosting
Although much effort has been expended on developing diverse laser-induced micro-nanostructures for anti-frosting, which type of structures performed better was still unclear. In 2023, we classified all micro-nanostructures into two types: open-cell and closed-cell structures [118]. By comparing the condensation frosting processes on the two structures, it was found that more small condensate droplets emerged on the closed-cell structures since the microframes provided more nucleation sites (Figure 13). The delayed frosting time decreased as the area ratio of closed structures increased, indicating open structures performed better than closed structures. This conclusion could greatly guide the structural design of ULSASs for better anti-frosting properties. Although much progress has been made in delaying icing/frosting of ULSASs, the icing/frosting cannot still be completely avoided. Especially in some extremely low-temperature and high-humidity environments, numerous droplets initiate and adhere to the micro-nano valleys, and then rapidly freeze [145,146]. How to inhibit the icing/frosting  
Figure 13: Delayed frosting phenomena on the ULSASs with different microstructures. (**a**,**b**) The frosting processes on the closed-cell microstructures and open-cell microstructures. (**c**) Schematics describing the differences of condensates nucleating and growing on different microstructures. (**d**) The densities and diameters of condensate droplets on the ULSASs with different microstructures. (**e**) Delayed frosting times versus different microstructures. (reproduced with permission from [118]. Copyright (2023) Royal Society of Chemistry). on ULSASs in extremely freezing environments is a great challenge, which needs to be further explored in structures, chemistry, and other areas.
## 3 Proposed Scheme
### NDN Router  
The FIB, PIT, and content store (buffer memory) are the three main data structures found in every NDN router. The PIT records every interest and associates it with the network interfaces that receive pertinent requests. Data is then returned through the reverse request path utilizing this state. Since NDN supports on-path caching, a content object may be cached so that it may be sent in response to later requests for the same item (CS). The size, as well as popularity distribution for the object population, define the memory need for a specific traffic reduction [28].
## 4 Results and Discussion
### Comparisons  
On one hand, we compare GDUI to an unconditional diffusion model and a diffusion model trained on datasets with ground-truth labels. On the other hand, we also compare GDUI to other state-of-the-art methods. Due to the potential impact of different compute budgets on fair comparisons, we utilize the same compute budget for all compared models. The comparison results on the STL-10 dataset are shown in the Table 1. We observe significant improvements of GDUI over unconditional diffusion model in evaluation metrics, such as 37.9% increase in FID score, 19.3% increase in IS score, and 25.4% increase in precision. More notably, GDUI outperforms diffusion model trained on ground-truth labels in all evaluation metrics except for the Recall score, where it performs slightly worse. Furthermore, GDUI surpasses the comparison models in all evaluation metrics except for the Recall score. We believe that GDUI achieves this effect by aligning images with similar features better with label information. We explicitly evaluate changes in sample quality under different training iterations as shown in Figure 2. The results indicate that GDUI outperforms the unconditional diffusion model on all evaluation metrics except for Recall, with a significant margin observed in most cases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Diffusion Method** & **FID\(\downarrow\)** & **sFID\(\downarrow\)** & **IS\(\uparrow\)** & **Precision\(\uparrow\)** & **Recall\(\uparrow\)** \\ \hline Unconditional & 26.81 & 48.74 & 9.76 & 0.59 & **0.45** \\ Ground-truth & 17.20 & 48.55 & 11.40 & 0.72 & 0.41 \\ guidance & 21.39 & 49.53 & 10.71 & 0.65 & 0.39 \\ DDGAN [55] & 21.79 & 49.12 & 10.34 & 0.69 & 0.40 \\ TransGAN [56] & 18.28 & 50.30 & 11.34 & 0.64 & 0.42 \\
**GDUI** & **16.66** & **48.44** & **11.64** & **0.74** & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of GDUI on the whole merged STL-10 dataset, combining the train and test split datasets. The best results are highlighted in **bold**.  
Compared to the diffusion model based on ground-truth labels, GDUI maintains an advantage in FID, IS, sFID, and precision metrics on most training stages, especially as the number of iterations increases. However, GDUI still lags behind the diffusion model based on ground-truth labels by 2% in Recall.  
Qualitative results on STL-10 are shown in Figure 3. It can be observed that the perceptual quality of GDUI-generated samples is higher than those generated by diffusion models based on unconditional and ground-truth labels. For example, GDUI generates images with relatively clear structures of cars and better shapes of horses compared to other diffusion models.
## 2 Materials and Methods
### Materials
#### 2.1.2 Reagents and Standards  
L-amino acid standards, norvaline, sarcosine, sodium dihydrogen phosphate (NaH\({}_{2}\)PO\({}_{4}\)), hydrochloric acid, sodium hydroxide, guanidine hydrochloride, 2_A_-dinitrophenylhydrazine, iron (II) chloride, iron (III) chloride, ammonium thiocyanate, _ortho_-phthalaldehyde, 9-fluorenyl methoxycarbonyl chloride, iodoacetic acid, 3-mercaptoethanolpropionic acid, boric acid (H\({}_{3}\)BO\({}_{3}\)), trichloroacetic acid, and MS-grade ammonium acetate were from Sigma-Aldrich (Darmstadt, Germany). Standards for fatty acid analysis included Supelco 37 Component FAME mix (Supelco, St. Louis, MO, USA), 68D (Nu-Check-Prep, Elysian, MN, USA), and GLC-490 (NuCheck-Prep, Elysian, MN, USA). Standards for the lipid class analysis were oleic acid, oleoyl monoacylglycerolglycerol, dioleoyl diacylglycerolglycerol, triolein, dioleoyl phosphatidylcholine, cholesterol oleate and ethyl docosahexaenoicenoic acid (Larodan, Solna, Sweden). All solvents used were at least HPLC grade. Water used was ultra-pure water (ELGA LabWater, High Wycombe, UK).
# Reduced-Order Model Based on Neural Network of Roll Bending
## 1 Introduction
This allowed them to considerably reduce the thickness error at the head end of the strip by predicting the rolling force; their predictions were very close to the real rolling force. An artificial NN model developed by [PERSON] et al. [28] can predict the thermal expansion of the work roll crown. They used a full three-dimensional model based on the finite difference method under a transient condition to create a data set for NN training. The NNmodel is accurate enough, and its computation time is less than 0.1 s, making it possible to use it for real-time control. A hybrid model with the combination of a predictive artificial NN and a genetic algorithm developed by [PERSON] et al. [29] allows minimization of flatness defects in strips. The genetic algorithm uses an NN as the internal loop for the calculations to optimize several parameters that affect the flatness of the strip. [PERSON] et al. [30] optimized the cold rolling schedule using a genetic algorithm. Comparison of the optimized schedule with the existing one was presented, analyzed, and showed satisfactory results. Despite the rapid development, evolution, and renewal of ROM in recent years, the rolling process remains somewhat away from such general development. The most complete review of soft computing in rolling can be found in [22]. The publication [31] is the closest to the topic presented in the paper. However, in this publication, the NN is mostly trained on data that do not originate from the FEM and has another objective. An analysis of the publication shows that ROM has become an important tool for improving real-time control of rolling processes. The author participates in the development of a real-time control system that offers the real-time design of the rolling schedule and aids in controlling the process in reverse and continuous mills. This system contains several modules, including the module to control the convexity and flatness of the final product. The module contains models of roll bending, roll wear, and roll thermal profiles. The system also contains models of flow stress, rolling force and torque, microstructure evolution, and others. The objective of this paper is to present the roll bending models and the way in which the transformation of the full-order FEM model into an ROM was performed.
# An Interactive Personalized Garment Design Recommendation System Using Intelligent Techniques
## 3 General Scheme of the Design Knowledge Base Construction  
### Construction of the Garment Relational Model  
In the garment relational model, we quantitatively define the similarity degree between \(r\left(CP,GP^{k}\right)\) and the consumer profile \(\left(CP\right)\) and the garment profile \(\left(GP^{k}\right)\). During the recommendation, the system selects the most relevant garment profile from the garment database and then adapts its design to meet the consumer's personalized requirements.  
Formally, the \(r\left(CP,GP^{k}\right)\) is defined as a linear combination of the similarity degrees of style \(r\left(SR,SC^{k}\right)\), color \(r\left(CR,CC^{k}\right)\), fabric \(r\left(MR,MC^{k}\right)\), and garment fit \(r\left(FR,FC^{k}\right)\) between the garment profile and the consumer profile (See Equation (1)), i.e.,  
\[r\left(CP,GP^{k}\right)=\alpha_{1}\times r\left(SR,SC^{k}\right)+\alpha_{2} \times r\left(CR,CC^{k}\right)+\alpha_{3}\times r\left(MR,MC^{k}\right)+\alpha_ {4}\times r\left(FR,FC^{k}\right) \tag{1}\]  
where \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), and \(\alpha_{4}\) refer to the weights of style, color, fabric, and garment fit, respectively. These weights, varying between 0 and 1, are selected by the user (designer or consumer) according to the importance of each component in its specific scenario.  
Let \(\rho\) be a predefined threshold for evaluating the similarity degree \(r\left(CP,GP^{k}\right)\) between the consumer profile \(\left(CP\right)\) and the garment profile \(\left(GP^{k}\right)\). Thus, we define the recommendation rules as follows:  
1. Calculate a list of \(r\left(CP,GP^{k}\right)\) for all the garment profiles in \(G\) and sort them in descending order;
2. Recommend the garment profiles with \(r\left(CP,GP^{k}\right)\geq\rho\) to the consumer or designer;
3. If \(r\left(CP,GP^{k}\right)\) of all the garment profiles \(GP\) is less than \(\rho\), it means that all the existing garments cannot meet the requirements. Next, the self-adaptation mechanism will be activated based on the garment adaptation model.
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 3 Definitions
### Set of Feasible Regenerator Combinations (FRC)  
In a typical translucent network, some nodes in the network may not have installed regenerators. In addition, even in nodes with installed regenerators, they may all be busy (i.e., in use) on the arrival of a connection request. Thus, a possible regenerator combination (as defined in Section 3.2) is referred to as feasible only if all of its nodes have at least one regenerator installed and it is free on the arrival of a request. We may define a subset \(D_{ij}\subseteq C_{ij}\) that considers only the feasible regenerator combinations in \(C_{ij}\). \(D_{ij}\) is formed by \(M_{ij}^{\prime}\leq M_{ij}\) feasible regenerator combinations in \(C_{ij}\), described as  
\[\begin{split} D_{ij}=&[(n_{1}^{(1)},\ldots,n_{K_{ ij}}^{(1)})\ldots(n_{1}^{(M_{ij})},\ldots,n_{K_{ij}}^{(M_{ij}^{\prime})})]\\ =&\{d_{ij}^{(1)},\ldots d_{ij}^{(M_{ij}^{\prime}) }\},\end{split} \tag{2}\]  
in which \(d_{ij}^{(m)}\) is one of the feasible regenerator combinations. If \(H_{ij}^{\prime}\) is the number of intermediate nodes in \(\pi_{ij}\) with at least one installed and free regenerator (note that \(H_{ij}^{\prime}\leq H_{ij}-2\)), we have that \(M_{ij}^{\prime}\leq\binom{H_{ij}^{\prime}}{K_{ij}}\).
# Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph
## 4 Experiments  
### Datasets Introduction  
We used three datasets of movie (MovieLens-20M), music (Last.FM) and restaurant (Dianping-Food) in the experiment. The datasets is as follows:  
* MovieLens-20M is a widely used dataset for movie recommendation and contains approximately 20 million clear ratings (from 1 to 5) on the MovieLens website. The corresponding KG contains 102,569 entities, 499,474 edges and 32 relation types.
* Last.FM contains listening records for 2000 users in the online music system. The corresponding KG contains 9366 entities, 15,518 edges and 60 relation types.
* Dianping-Food comes from Dianping.com, which contains more than 10 million user and restaurant interaction records (including clicks, purchases, etc.), with approximately 2 million users and 1362 restaurants participating. The corresponding KG contains 28,115 entities, 160,519 edges and seven relation types.  
The knowledge graph corresponding to the datasets is derived from the pre-processing knowledge graph disclosed by KGCN [18] and KGNN-LS [30]. These works use Microsoft Satori to build KG for movie and music datasets. The knowledge graph of Dianping-Food comes from the brain of Meituan Brain which is the internal knowledge graph built by Meituan for dining and entertainment. In data preprocessing, we removed the items that are not in the knowledge graph. Before NACF starts training, we initialize all entities and relations as trainable and random vectors. They are constantly adjusted during training until convergence.  
Since the MovieLens-20M and Last.FM are rating feedback, we convert them to click-through rate feedback. For movie, there are 6783276 records with 4-5 points and 6,718,346 records with 1-3 points. So we choose four as the click-through rate to achieve a better samples distribution. The ratings of the music are too sparse (from 1 to 352,698) to be used as a criterion for evaluating user interest. Thus, we set all the music that the user clicked as positive samples. To avoid a large gap between the number of positive and negative samples, we used negative sampling during training to randomly select negative samples from items that were not interacted by users in the datasets until the numbers of positive and negative samples were the same.  
The statistics of these three data sets are shown in Table 2.
###### Abstract  
Under cyclic and repetitive loads, fatigue cracks can be further propagated to a crucial level by accumulation, causing detrimental effects to structural integrity and potentially resulting in catastrophic consequences. Therefore, there is a demand to develop a reliable technique to monitor fatigue cracks quantitatively at an early stage. The objective of this paper is to characterize the propagation of fatigue cracks using the damage index (DI) calculated by various acoustic features of ultrasonic guided waves. A hybrid DI scheme for monitoring fatigue crack propagation is proposed using the linear fusion of damage indices (DIs) and differential fusion of DIs. An experiment is conducted on an SMA490 BW steel plate-like structure to verify the proposed hybrid DIs scheme. The experimental results show that the hybrid DIs from various acoustic features can be used to quantitatively characterize the propagation of fatigue cracks, respectively. It is found that the fused DIs calculated by the acoustic features in the frequency domain have an improved reliable manner over those of the time domain. It is also clear that the linear and differential amplitude fusion DIs in the frequency domain are more promising to indicate the propagation of fatigue cracks quantitatively than other fused ones.  
monitoring of fatigue crack; damage index; ultrasonic guided waves; sensor network; structural health monitoring +
Footnote †: journal: Applied Mechanics and Physics
## 2 Materials and Methods
### Description of Data Evaluation
Third, the transferability of the classification algorithm to different pinions is tested. Consequently, pinons 2 and 3, along with pinons 5 and 6, are grouped collectively due to their shared damage conditions. One of the pinons from each group is utilized to train the classification algorithm, while the other one is employed for testing purposes. The evaluation of the predictions made by the random forest classifier entails the utilization of prediction accuracy as a metric for assessing the overall correctness of the predictions across all test data. Additionally, a confusion matrix is employed to facilitate a more detailed and nuanced evaluation of the predictions. The confusion matrix is a statistical tool used to analyze the discrepancy between the true class value and the predicted class value of the random forest classifier. The main diagonal indicates the proper classified classes; deviations from this diagonal suggest erroneous classifications. The accuracy is a measure used to describe how often a machine learning algorithm predicts the correct condition. It can be calculated by dividing the number of correct predictions by the total number of predictions, as expressed in the following equation:  
\[\text{Accuracy}=\frac{\text{Correct predictions}}{\text{All predictions}} \tag{1}\]  
This contribution utilizes accuracy to quantify the classification performance of different sensors under varying operating conditions. It functions as a metric for comparing the effectiveness of the gear wheel sensor, pole ring sensor, and acceleration sensor in classifying gear damages. Higher accuracy values indicate a stronger classification capability.