esa-sceva/satcom-chunk-collection huggingface dataset:
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method
### Network  
Classification neural networks play a critical role in self-supervised ASD detection by extracting latent features and utilizing classification learning to effectively distinguish between different sound patterns. Self-supervised ASD methods based on classification networks have been demonstrated to outperform traditional approaches, especially in handling complex anomalous patterns [7]. In this study, we propose two classification network architectures to meet the requirements of resource-constrained environments and high-sensitivity detection tasks: the lightweight ASDNet, and SpecMFN, which combines SpecNet and MobileFaceNet, as illustrated in Figure 2.  
ASDNet is optimized for resource-constrained environments, with the goal of reducing model parameters and computational overhead while maintaining robust detection performance, making it well suited for deployment on edge devices. In contrast, SpecMFN integrates a spectral feature processing network (SpecNet) with an efficient classification network (MobileFaceNet), thereby enhancing its ability to detect complex anomalous patterns. Notably, performance evaluations indicate that SpecMFN achieves a higher partial area under the curve (pAUC) than ASDNet, highlighting its superior ability to capture anomalous patterns in regions with low false positive rates (FPR). This makes SpecMFN particularly suitable for applications that require high detection precision. Together, these two architectures provide flexible and efficient solutions tailored to diverse application needs.
## 3 Results
### Circadian Stimulus Autonomy (CSA)
#### 3.3.3 Influence of Orientation (North/South)  
\(CSA1\) values for cubicles with dark interior finishes generally show significant variability, especially in north orientations, where a noticeable decline is observed within a band approximately 0.75 m from the facade. In contrast, for south orientations, \(CSA1\) values are consistently higher, with narrower dispersion across different configurations. \(CSA2\) values for a 15% WWR are spatially similar across most configurations, especially in north orientations with dark coatings. In these cases, the \(CSA2\) begins at low values, often near zero, and peaks at 0.75 to 1.00 m from the window before gradually declining with a shallow slope towards the back of the room.  
The type of orientation also significantly affects \(CSA2\) values. In north orientations, especially for larger window dimensions, the \(CSA2\) exhibits minimal deviation between Seville and Barcelona, with slightly lower values in Barcelona. For south-facing rooms, the presence of a window lintel impacts the area within 0.50 m of the window, causing a distinct decrease in \(CSA2\) levels.  
3.3.4. Influence of Location (Seville/Barcelona)  
While \(CSA\) values show some variance between Seville and Barcelona, the location has a less pronounced impact compared to that of the window size and interior finishes. However, \(CSA2\) values for rooms with bright coatings are consistently elevated above 80% in both Seville and Barcelona for south and north configurations, particularly for centered windows with WWRs between 25% and 45% and upper windows with WWRs between 35% and 45%. Slight differences are noted, with values being marginally higher in Seville than Barcelona.  
3.3.5. Influence of Interior Reflectance (Bright/Dark Coatings)  
The interior finish plays a critical role in influencing \(CSA\) results, particularly \(CSA1\) values. Bright coatings significantly enhance \(CSA1\), with values between 20% and 25% higher compared to those for dark coatings across all orientations and WWRs. For bright coatings, CSA2 values exceed 80% for both south and north configurations, especially in rooms with centered and upper window configurations and WWRs between 25% and 45%. The distribution of \(CSA2\) values for bright interiors is more consistent, while for dark finishes, the variability increases notably, particularly in north-facing orientations.  
Dark coatings generally produce lower \(CSA1\) values, with only 20% to 25% of the values obtained for bright coatings. The effect of dark coatings is particularly pronounced in north orientations, where \(CSA1\) values range from 0% (15% WWR) to 63% (Barcelona, centered, 45% WWR). In contrast, for south-facing configurations with dark coatings, \(CSA1\) values are slightly higher but are still significantly lower than for those with bright coatings.  
Overall, the \(CSA\) results are more influenced by interior reflectance and window size than by location. The orientation further impacts the distribution, with south orientations generally yielding higher \(CSA1\) values and less variability compared to north orientations.
###### Abstract  
In recent years, a growing number of real-time semantic segmentation networks have been developed to improve segmentation accuracy. However, these advancements often come at the cost of increased computational complexity, which limits their inference efficiency, particularly in scenarios such as autonomous driving, where strict real-time performance is essential. Achieving an effective balance between speed and accuracy has thus become a central challenge in this field. To address this issue, we present a lightweight semantic segmentation model tailored for the perception requirements of autonomous vehicles. The architecture follows an encoder-decoder paradigm, which not only preserves the capability for deep feature extraction but also facilitates multi-scale information integration. The encoder leverages a high-efficiency backbone, while the decoder introduces a dynamic fusion mechanism designed to enhance information interaction between different feature branches. Recognizing the limitations of convolutional networks in modeling long-range dependencies and capturing global semantic context, the model incorporates an attention-based feature extraction component. This is further augmented by positional encoding, enabling better awareness of spatial structures and local details. The dynamic fusion mechanism employs an adaptive weighting strategy, adjusting the contribution of each feature channel to reduce redundancy and improve representation quality. To validate the effectiveness of the proposed network, experiments were conducted on a single RTX 3090 GPU. The Dynamic Real-time Integrated Vision Encoder-Segmenter Network (DriveSegNet) achieved a mean Intersection over Union (mIoU) of 76.9% and an inference speed of 70.5 FPS on the Cityscapes test dataset, 74.6% mIoU and 139.8 FPS on the CamVid test dataset, and 35.8% mIoU with 108.4 FPS on the ADE20K dataset. The experimental results demonstrate that the proposed method achieves an excellent balance between inference speed, segmentation accuracy, and model size.  
Article  
1 School of Computer Science and Technology, Changchun University, Changchun 130022, China; [EMAIL_ADDRESS] (J.F.) [EMAIL_ADDRESS] (P.X.) [EMAIL_ADDRESS] (C.B.)  
2 School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100811, China; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
real-time semantic segmentation; autonomous driving perception; lightweight neural network; attention mechanism; multi-scale feature fusion
# Research on Lazy Theta+
## 6 Simulation Experiment and Analysis
### Route Planning Algorithm Comparison Experiment
#### 6.2.3 Route Smoothing Experiment  
1. Simulation  
For a grid level of \(N=3\), the flight direction of the planned route obviously changes. It is convenient to observe the effect of the B-spline smoothing route, so the route planning result of the grid-based Lazy Theta* algorithm under the grid level \(N=3\) is used as the smoothing experiment object. The competitor smoothing methods are the uniform quadratic B-spline, uniform cubic B-spline, equally spaced quadratic B-spline, and equally spaced cubic B-spline. By comparing the maximum altitude and path length after smoothing, a method suitable for smoothing the route is obtained. The equally spaced B-spline results for route smoothing are depicted in Figure 23, in which a green dotted line represents the original path, and the smoothed path is represented by a red curve.  
Figure 14 in Appendix B complements the smooth simulation results of the uniform B-spline. The following is an analysis and comparison of the data obtained from the smooth simulation experiment.  
2 Analysis of results  
The experimental data of the smooth route are presented in Table 4.  
Figure 22: Lazy Theta* data change diagram of grid points at different grid levels. (**a**) Path length variation graph, (**b**) Path cost variation graph, (**c**) Maximum turning angle change diagram, (**d**) Maximum pitch angle change diagram, and (**e**) Running time.  
The data in Table 4 show that the uniform B-spline cannot connect to the route's starting point, while the equally spaced B-spline is a smooth curve connecting to the route's starting point. Therefore, the equally spaced B-spline is more suitable for the smoothing method of the route. Regarding path length, the path length of the equally spaced cubic B-spline is the shortest, which further improves flight efficiency and makes the planning result closer to the real flight path. From the perspective of maximum altitude, three B-splines with equal spacing can reduce altitude changes and make the flight more stable and controllable.  
Overall, the above experimental analysis verifies the equidistant B-spline's effectiveness, proving that the equidistant cubic B-spline is more suitable for route smoothing in complex terrains.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Route Planning Algorithm**} & \multirow{2}{*}{**Smoothing Method**} & **Path Length after** & **Maximum Height after** \\  & & **Smoothing** & **Smoothing** \\ \hline \multirow{4}{*}{Grid point Lazy Theta*} & Original route & 105.02 & 5.00 \\ \cline{2-3}  & Uniform quadratic B-Splines & 100.59 & 4.38 \\ \cline{2-3}  & Uniform cubic B-splines & 98.61 & 4.17 \\ \cline{2-3}  & Equally spaced quadratic B-splines & 96.89 & 4.17 \\ \cline{2-3}  & Equally spaced cubic B-splines & 90.50 & 3.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Route smoothing experimental data.  
Figure 23: Top view and altitude curve after route smoothing. (**a**) Equally spaced quadratic B-spline smoothing, and (**b**) Equally spaced cubic B-spline smoothing.
## Materials and Methods  
### Materials  
Brown algae (_Laminaria digitata_) was provided by Setalg (Pleubian, France). After washing, drying, and grinding, the biomass was sieved to recover the fraction below 250 \(\upmu\)m. Alginate (Manugel GMB) was supplied by DuPont (Landerneau, France; now JRS Rettenmaier). Branched polyethyleneimine (PEI, 50% (_w/w_) in water) and glutaraldehyde (GA, 50% (_w/w_) in water) were purchased from Sigma-Aldrich (Taufkirchen, Germany) Sodium carbonate, and calcium chloride were obtained from Chem-Lab NV (Zedelgem, Belgium). Strontium nitrate (Sr(NO\({}_{3}\))\({}_{2}\)) was used for lab experiments (Sr(NO\({}_{3}\))\({}_{2}\)) (single metal solutions) while strontium chloride (SrCl\({}_{2}\)) was selected for multi-component solution; both of them were purchased from the Damao Chemical Reagent Factory (Tianjin, China) and Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China), respectively. MgCl\({}_{2}\).6H\({}_{2}\)O, AlCl\({}_{3}\).6H\({}_{2}\)O, CaCl\({}_{2}\), NaCl, Anhydrorous K\({}_{2}\)CO\({}_{3}\) were supplied by Guangdong Guangzhou Sci-Tech Co., Ltd. (Guangdong, China). Ethylene glycol diglycidyl ether, Chloroacetonitrile, and hydroxylamine hydrochloride were purchased from Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China).  
The process was developed using _Laminaria digitata_, an emblematic example of abundant brown algae, that was already investigated for metal sorption and widely characterized. The process could be extended to other brown algae containing substantial amounts of alginate (i.e., higher than 25%-30%, _w/w_).
_Applied_  
_sciences_  
_Article_  
**Numerical and Experimental Study on the**  
**Flow-Induced Noise Characteristics of High-Speed**  
**Centrifugal Pumps**  
**[PERSON] 1,2\({}^{\copyright}\), [PERSON] 1, [PERSON] 2,3, [PERSON] 1,2, [PERSON] 1 and [PERSON] 1,*  
1 National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China; [EMAIL_ADDRESS] (Q.S.); [EMAIL_ADDRESS] (C.S.); liaho01@caas (H.L.); [EMAIL_ADDRESS] (K.H.)  
2 Farimal Irrigation research Institute, CAAS Key Laboratory of Water-Saving Agriculture of Henan Province, Xinxiang 453003, China; [EMAIL_ADDRESS]  
3 School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-1395-280-1593  
**Abstract:** The development of low-noise pumps is essential to design quiet fluid delivery systems. Due to the complicated internal flow, the flow-induced noise characteristics of high-speed centrifugal pumps have not been well understood. Taking engine cooling pumps as an example model, experimental measurements are performed in a semi-anechoic room and a CFD/CFA calculation method is proposed to study the fluid-borne noise and radiated noise characteristics. In the speed range of 5000-6750 r/min, both the pump head and the dimensionless radiated noise characteristics conform to similar laws, and the highest efficiency point pump presents the lowest noise level. Consistent with the experimental results, the predicted radiated noise of the model pump presents dipole characteristics at the required flow rate condition. Moreover, the spectrum of fluid borne noise at pump outlet shows broadband characteristics but with obvious discrete peaks, which are not only related to the fluid pressure pulsation characteristics (\(6f_{0}\) and the multiple) at the low-frequency region, but also to the frequency of the structural mode (3000-6000 Hz region). Rotor-stator interaction of the pump flow field between the impeller and volute is the main reason of flow-induced noise; unstable flow also contributes to the broadband components in the noise spectrum.  
**Keywords:** high-speed centrifugal pumps; flow-induced noise; computational fluid dynamics; computational fluid acoustic; pressure pulsation
## 1 Introduction
This review paper aims to comprehensively analyze recent research applying GANs and Diffusion Models to AD detection using neuroimaging. We summarize current methodologies, model architectures, applications, assess their performance metrics, strengths, limitations, and clinical potential. Through these insights from recent studies, we aim to guide researchers and practitioners on ongoing efforts at the intersection of AI innovation and neurodegenerative disease diagnosis research. The rest of this review paper is structured as follows. To introduce the essential background on AD, Sections 2 and 3 define the stages of AD and exploring the most important neuroimaging modalities that are used for AD diagnosis. Section 4 presents a comparative review of the available literature that apply these generative models to AD diagnosis. It reviews different types of methods applied in those reviewed papers, identified their key research challenges and gaps. It also includes an evaluation of various performance metrics, with particular emphasis on experimental results and diagnostic accuracy reported in the reviewed articles. Section 5 discusses the most common datasets used in AD research. Sections 6 and 7 are devoted to describing different types of GANs and Diffusion models, respectively. Finally, Section 8 summarizes and discusses the study's key findings, results, challenges, and potential future directions.
## 2 Methodology and Experiments  
### Fabrication of the Silicon Micro-Pyramid Structures  
To fabricate silicon micro-pyramid structures, we first cleaned the silicon wafer with ethanol and acetone. The silicon wafer was then etched at 85 \({}^{\circ}\)C for 40 min in a mixed solution of NaOH (1.8 wt%), water and isopropyl alcohol (IPA, 5 vol%). During the NaOH etching, the etching solution was stirred using a magnetic stirrer, and the rotational speed of the magnetic stirring was set to 200 rpm. After etching, the wafer was rinsed with deionized water and dried with nitrogen.
# Reconstructing the 26 June 1917 Samoa Tsunami Disaster
## 3 Methods and Data
### Tsunami Modelling  
#### 3.1.1 Model Setup and Configuration  
This tsunami modelling analysis adapts a similar approach used by [PERSON] et al. (2020) [27] to model the inundation of the 2009 event. For generation of the tsunami from the initial earthquake through to propagation and inundation, the BG-Flood software was used. BG Flood (Block-adaptive on Graphics processing unit Flood model) is suited for the simulation of flooding and/or inundation caused by rivers, rain, tides, or tsunamis. It is based on the formulation of Basilisk as well as on the memory structure on the GPU of block uniform quadtree of [PERSON] et al. (2017) [33]. The block uniform quadtree structure enables various resolutions with the same memory size but with different physical sizes [33].  
For tsunami initialization, we used the earthquake parameters for the 1917 event presented by [12] to configure the source model. To start generating the initial earthquake, the following values were needed: fault parameters (strike, dip, rake, slip), the dimensions of the rupture (length and width), the hypocenter of the earthquake (coordinates and depth), and the timing of the rupture. The rupture length was 150 km with a rupture width of 50 km. The earthquake epicentre was located at 15.13\({}^{\circ}\) S and 173.28\({}^{\circ}\) W on 26 June 1917 at 05:49 UTC (i.e., 25 June 1917 at 18:49 local time) at a depth of 10 km. After applying vertical deformation to the fault, the initial water displacement was calculated as a theoretical visco-elastic fault displacement using the formulation of [34].  
Figure 2 displays the water displacement after the 1917 earthquake. For the tsunami modelling, a fault length of 150 km was used to match the runup observations. As the model uses an adaptive grid (more than one resolution), three output NetCDF files were created with resolutions of 10 m, 20 m, and 40 m.
Due to this, it has attracted the attention of scholars [27; 28; 29; 30; 31]. The significant drawback of the early proposed algorithm, including both the numerical and metaheuristic methodologies, is the probability of converging to values which may not be a global optimum but, rather, are stuck at a local optimum. To unravel this issue, an RTO is inspected in this examination for the ideal coordination of OCRs and is contrasted with chaotic firefly algorithms (CFA), firefly algorithms (FA), continuous particle swarm optimization (CPSO), continuous genetic algorithms (CGA), genetic algorithms (GA), and the simplex method (SM). More recently, the authors applied the root tree algorithm (RTO) to the DOCRs and compared them with different metaheuristic algorithms [32]. In this article, the problem of OCRs in a power system is handled by the RTO algorithm. The objective of this problem is to minimize the total operating time taken by primary relays. Primary relays are expected to isolate the faulty lines satisfying the constraints on design variables. RTO is a renowned and trustworthy bio-inspired algorithm for solving linear, nonlinear, and complex constrained optimization problems. To the extent of the author's knowledge, RTO has not yet been executed for the advancement of OCR settings utilizing single- and multi-loop distribution bus systems, which are introduced in this paper. We have actualized RTO to take care of the issue of OCR settings, and the results of our simulation are contrasted with other up-to-date algorithms. The rest of the paper is organized as follows. In Section 2, we elaborate on the mathematical formulation of the problem. The RTO is recalled in Section 3. Parameter settings and statistical and graphical results are discussed in Section 4. Section 5 concludes the present study.
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 3 Background
### Big Data and Kinetic Drives  
Data volume is one of the important dimensions of the Big Data concept. The amount of data currently is huge and keeps growing at a rapid rate. In the Big Data environment, variety is another dimension. There are huge amounts of data sources. For example, in the Internet of Things environment, all kinds of \"things\" are generating data, such as vehicles, appliances, people, mobile devices,  
Figure 3: Comparison of sequential write throughput between the Kinetic Drive and the LevelDB server (_Key_ size = 16 B, one million key-value pairs) [29].  
and industrial sensors. These different types of data sources have been generating data in various and irregular formats (e.g., videos, images, texts, numbers, and web pages).  
To deal with various and irregular data formats, NoSQL databases usually offer more flexible and better performance than traditional SQL databases. As one of the NoSQL databases, the key-value store system provides a simple, but efficient data storage and access scheme. Key-value store systems have been widely used in many Big Data applications, such as Amazon Dynamo [4], Facebook Cassandra [5], and LinkedIn Voldemort [6]. When those applications generate a data record such as a customer's information or a user's profile, it can be stored in a key-value pair format in those decentralized systems.  
Kinetic Drives are specifically designed for key-value store systems to store key-value pair data. As mentioned previously, we can find that Kinetic Drives significantly change the data access mechanism for user-to-drive and drive-to-drive interactions. The performance of the entire large-scale KVS system can be improved by Kinetic Drives due to better parallelism and scalability.  
Kinetic Drives can be widely deployed to replace traditional hard disk drives for key-value store systems. As shown in Figure 1a,b, Kinetic Drives reduce the complexity of data access and storage. With proper metadata management schemes, those large-scale key-value store systems such as Amazon Dynamo [4], Facebook Cassandra [5], and LinkedIn Voldemort [6] can use Kinetic Drives to improve the performance and reduce the overhead of the system. Furthermore, with Kinetic Drives, the Seagate Open Storage Platform [40] can even work with storage in OpenStack (an open standard cloud computing platform) [41], which is widely deployed in the cloud. Kinetic Drives can be the storage devices for key-value pair data, and our design in this paper offers scalable solutions for a global-scale key-value store system.
## 1 Introduction
### Towards Future Internet  
After successfully connecting computers (Internet protocol) and later people (World Wide Web), enabling an Internet of Things is one of the great challenges of the future Internet. According to some estimates, the size of the Internet doubles every 5.32 years, which will lead to an average of 6.58 connected devices per person by 2020 [3]. These 50 billion things [4] connected to the Internet in order to gather information for various and unattended applications that support new markets [5] will create a heavy and dense traffic on the core network. On the other hand, these new and exciting possibilities come with the requirement of a larger addressing space. The IPv4 addressing pool already exhausted [6] the transition to IPv6 with its huge numbering space (\(2^{96}\) times bigger than IPv4's) is urgent [7].  
In the wide field of health informatics, the special case of eHealth relates to the use of the Internet to disseminate health related information [8]. The health-related measures are captured by small and various devices and transmitted to be stored in large databases. Further processing of this data helps to support diagnostics. The overall objective is to improve efficiency and save lives [9].