esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
In general, the damage indexes proved to be very effective in detecting not only the presence of damage, but also its extent. Importantly, the results of this study highlight the potential benefits of applying damage indexes in advanced monitoring and assessment systems, particularly in digital twin systems and sensor-based applications. Damage indexes can improve real-time assessment of seismic damage and decision making after seismic events, which is an important aspect of engineering practice. In particular, seismic design should consider structural safety together with optimization of available resources and prioritization of actions. The use of damage indexes to quickly identify damage and vulnerabilities enables more informed decisions leading to a more efficient use of resources in post-earthquake rehabilitation. In addition, the use of damage indexes could promote a more integrated and systematic approach to resource allocation for seismic rehabilitation measures by moving from assessing the vulnerability of individual buildings to a damage index ranking of the different buildings in the urban context. Conceptualization, L.D., M.G., S.L. and A.B. ; methodology, L.D. and A.B. ; software, L.D. ; validation, L.D., M.G., S.L. and A.B. ; formal analysis, L.D. ; writing--original draft preparation, M.G. ; writing--review and editing, L.D., M.G., S.L. and A.B. ; visualization, L.D., M.G., S.L. and A.B. ; supervision, A.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The studies presented here were partially carried out as part of the activities envisaged by the Agreement between the Italian Department of Civil Protection (DPC) and the ReLUIS Consortium. The contents of this paper represent the authors' ideas and do not necessarily correspond to the official opinion and policies of DPC. The authors declare no conflicts of interest. Figure 17: Damage indexes for the existing RC frame building in the infilled frame configuration (EX_MRF_IF). Note: the green, yellow, orange and red refer to the UPD, LS, CP and GC performance levels, respectively. Figure 13: Damage indexes for the code-conforming MRF building in the bare frame configuration (CC_MRF_BF). Note: the green, yellow, orange and red refer to the UPD, LS, CP and GC performance levels, respectively. Figure 4: Damage indexes for the code-conforming MRF building in the infilled frame configuration (CC_MRF_IF). Note: the green, yellow, orange and red refer to the UPD, LS, CP and GC performance levels, respectively. Figure A5: Damage indexes for the code-conforming SW building in the bare frame configuration (CC_W_BF). Note: the green, yellow, orange and red refer to the UPD, LS, CP and GC performance levels, respectively. Figure 6: Damage indexes for the code-conforming SW building in the infilled frame configuration (CC_W_IF). Note: the green, yellow, orange and red refer to the UPD, LS, CP and GC performance levels, respectively.
# Using Modeling to Select the Type of Microwave Field Emitter for Dense-Layer Grain Dryers
## 5 Conclusions  
The characteristics obtained as a result of modeling (standing wave ratio, radiation efficiency and radiated power) for the types of microwave field emitters allowed for their evaluation and formulation of the following conclusions:  
Application of horn-type waveguides in convective-microwave processing plants makes it possible to manufacture rather convenient processing equipment for processing grain. In the operating frequency range, their SWR is 1.3, and their radiation efficiency is 78 dB. However, such waveguides do not provide sufficient uniformity of microwave field distribution at their output affecting the efficiency of grain processing.  
The use of rectangular 55 \(\times\) 110 waveguides with a slot radiator makes it possible to ensure a more uniform radiation of electromagnetic field in the entire spectrum of wave types. Their SWR is equal to one, and the radiation efficiency amounts to 94.43 dB. At the same time, their application requires further design development related to the issues of supplying the heat-carrier to the processing area.  
Semicircular waveguides with a slot radiator feature SWR equal to one over the frequency range from 2 GHz to 3 GHz. Their radiation efficiency is 94.28 dB. The electromagnetic field distribution homogeneity of such waveguides is better compared to rectangular waveguides. Additionally, the problem of supplying the heat-carrier to the area of grain processing can be solved more easily for circular waveguides.  
Based on the above, the authors have given preference to semicircular waveguides. Further research will be conducted with these types of emitters.  
Conceptualization, A.N.V.; methodology, D.B.; software, validation, formal analysis, A.A.V., A.N.V. and D.B.; investigation, A.A.V.; resources, data curation, A.N.V.; writing--original draft preparation, writing--review and editing, visualization, A.A.V.; supervision, project administration, A.N.V.; and funding acquisition, A.A.V. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Federal Scientific Agroengineering Center VIM (RF state assignment No FGUN-2022-0004).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The research results are presented in the report on research work of the direction \"Energy supply of agroindustrial complex\" of FGBNU FNAC VIM for the 1 st quarter of 2023.  
The authors declare no conflict of interest.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 4 Results
### Analysis of Estimated Drifts in TOC Products
#### 4.3.6 Statistical Analysis of NDVI Drifts  
The same statistical analysis on the observed drifts was carried out for the temporal series of NDVI over the same ensemble of evaluation sites. The estimated drift for TOC and BRDF-normalized NDVI are presented in Figure 16 for all observations and restraining the analysis to near-nadir acquisitions (VZA \(<\) 10\({}^{\circ}\)). Overall, there is no clear evidence of the impact of the orbital decay for any angular conditions or land cover class. The deviation of the median from the 0 value is well within the statistical dispersion of the estimated drifts, demonstrating that the orbital drift effect on NDVI time series is less evident than for surface reflectances data, even for near-nadir observations.  
Figure 15: The figure shows the boxplots of estimated drifts in the four spectral bands when all angular observations are merged. The statistics are aggregated per land cover class: Crop (CR), Tree (TR), Shrubland (SH), Grassland (GR), Sparse vegetation (SP), Bare soils (BA). Estimated drifts for TOC (red) and BRDF-normalized (blue) reflectances are presented for the bands: (**a**) Blue, (**b**) Red, (**c**) NIR, (**d**) SWIR. The parameters of the boxplot chart are those used and described in Figure 12.
_applied_  
_sciences_  
_Article_  
**Computational Fluid Dynamics Investigation of Hydrodynamic Forces and Moments Acting on Stern Rudder Plane**  
Configurations of a Submarine**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{2}\)\({}^{\copyright}\) and [PERSON] \({}^{1,\star}\)\({}^{\copyright}\)**  
\({}^{1}\) Department of Transportation Mechanical Engineering, The University of Danang--University of Science and Technology, Da Nang 50000, Vietnam; [EMAIL_ADDRESS]  
\({}^{2}\) Department of Naval Architecture and Marine Engineering, Changwon National University,  
Gyeongnam 51140, Republic of Korea; [EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract** This study presents the predicted hydrodynamic characteristics of different rudder plane configurations on the stern of a full-scale submarine in deep water, which are obtained using the Reynolds-Averaged Navier-Stokes method in Ansys Fluent Solver. First, the results obtained for the X-rudder plane configuration are verified according to previous numerical and experimental results in order to assess the accuracy of the simulation procedure. The X-rudder plane, Y-rudder plane, and Cross-rudder plane configurations in deep water with deflection angles ranging from \(-21\) degrees to \(+21\) degrees are then simulated. Next, the hydrodynamic forces and moments of the Cross-plane, X-plane, and Y-plane rudder configurations obtained through simulation are analyzed using Taylor's expansion to estimate the hydrodynamic coefficients. The obtained results demonstrate that the X-force of the X-plane rudder configuration is larger than the corresponding forces acting on the Cross-plane rudder and Y-plane rudder configurations. Meanwhile, the Y-force and Z-force of the X-plane rudder configuration are significantly greater than the corresponding forces of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the \(y\)- and \(z\)-axes. However, the roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than that under the X-plane rudder configuration.  
**Keywords** submarine; stern rudder plane configuration; rudder force and moment; RANS-based simulation
# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 5 Conclusions  
This paper presents a lightweight drone-based algorithm for insulator defect detection. Due to the limited number and poor balance of samples in the CPLID insulator dataset, data augmentation techniques such as brightness variation and noise addition were applied to enrich sample types and quantity. Additionally, the dataset was enhanced by introducing color-changing insulator labels alongside existing labels for normal and defective insulators, thereby diversifying label types. Considering the limited hardware conditions of mobile devices, this study introduced depthwise separable convolution and used the MobileNetv3 model as the Backbone network to replace the feature extraction part of YOLOv8s for lightweight improvement. Furthermore, given the relatively small proportion of insulators in the images, a lightweight attention mechanism, CBAM, was integrated into the feature extraction module of YOLOv8s.  
The experimental results demonstrate that the proposed LDIDD achieved a 46.6% reduction in computational complexity and a reduction of about 2% in [EMAIL_ADDRESS] compared to the base YOLOv8s, while maintaining a stable mAP of around 98.3%. The findings confirmed that the proposed method effectively achieves both a lightweight design and high recognition accuracy in detecting insulator defects within UAV inspection images. Furthermore, it is suitable for deployment on the mobile devices of UAVs, offering a viable alternative to traditional manual inspection and meeting the real-time requirements necessary for insulator detection on mobile platforms.  
Future work will focus on adding more types of labels and refining the identification accuracy for each type of defect. Also, research could focus on optimizing the method's real-time processing capabilities and enhancing its adaptability across diverse environments and tasks, especially in more intricate transmission line inspection scenarios. Additionally, exploring the integration of the model with edge computing technologies or expanding its application to smart grids and smart cities is also a promising area.  
Conceptualization, Y.L., X.L., and Z.W.; methodology, Z.W.; software, Y.L.; validation, X.L. and R.Q.; formal analysis, Y.C. and A.P.; investigation, Z.W.; resources, X.H.; data curation, R.Q.; writing--original draft preparation, X.L. and A.P.; writing--review and editing, Y.L. and A.P.; visualization, Y.C.; supervision, Z.W.; project administration, Y.L.; funding acquisition, Y.L. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Project of Inner Mongolia Power Co., Ltd., under Grant No. LX01234742, and was partly supported by Key Foundation of Zhejiang Provincial Natural Science of China, under Grant No. LZ22F010005 and LTGY24F010002.
# Efficient Discretization of the Laplacian: Application to Moving Boundary Problems
## 2 Materials and Methods
### Problem Statement and Mathematical Background
In any case, we collect the coefficients in (4) or (5) into a matrix \(D_{h,\text{FD}}\), which is called the finite difference discretization matrix. Here, the entry \(D_{h,\text{FD}}[j,k]\) is the coefficient of \(u(x_{k})\) in the approximation of \(\Delta u(x_{j})\), either in (4) or in (5). In this way, \(D_{h,\text{FD}}\) becomes a sparse matrix with either at most 5 or 9 non-zero elements per row; see Figure 2. In the case of regular geometries and homogeneous boundary conditions, \(D_{h,\text{FD}}\) is a positive definite (or semidefinite) symmetric one. To compare the above cases, in Figure 2, we also give the position of the non-zero elements and the total number of non-zeros (denoted with nz) in the matrices \(D_{h,\text{FD}}\) corresponding to the discretizations in (4) and (5), respectively. The conventional way to find optimal parameters in (2) and (3) is to use Taylor expansion and determine the coefficients to obtain the desired approximation order. In a general case, this approach completely fails since then the accuracy in (4) and (5) are not available and for lower-order accuracy, multiple solutions exist. In this way, we somehow have to optimize the coefficients in (2) and (3). In a previous study [12], instead of pointwise optimization procedures, a neural network was utilized, which \"learns\" this process and can quickly compute the corresponding coefficients. That study focused on the five-point approximation in (4). With the matrix \(D_{h,\text{FD}}\) at hand, the final form of the FD discretization of the problem in (1) is the linear system  
\[D_{h,\text{FD}}\mathbf{u}_{h,\text{FD}}=\mathbf{f}_{h}, \tag{6}\]  
where \(\mathbf{f}_{h}\) denotes the discretization of the source term \(f\) with \(\mathbf{f}_{h}[j]=f(\mathbf{x}_{j})\). The solution \(\mathbf{u}_{h,\text{FD}}\) is then called a finite difference approximation of \(u\) in (1).
# Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
## 1 Introduction  
The explosion of the Internet of Things (IoT) has led to an abundance of data streams that organizations use to make rapid, data-driven decisions. Whether managing energy consumption in a smart building, monitoring patient vitals in telehealth, or tracking metrics on a factory floor, dashboards have emerged as vital interfaces for visualizing and analyzing large volumes of sensor data in real time [1]. However, most of these IoT dashboards are designed with primarily visual paradigms (color-coded charts, graphs, and interactive graphical controls) often with minimal textual description. This design bias leaves behind a substantial user base that relies on screen readers and other assistive mechanisms for digital access [2]. According to the World Health Organization, more than 2.2 billion people globally experience some form of near- or distance-vision impairment [3]. Supporting this, WebAIM's 2024 screen-reader survey found that 68% of blind respondents interact with dashboards at least once per week [4]. Yet, a recent audit of 87 public COVID-19 dashboards revealed that fewer than 20% provided even basic text alternatives such as alt-text or downloadable tables [5], highlighting a persistent accessibility gap in data-rich IoT interfaces. As a result, visually impaired users continue to face major obstacles in interpreting real-time sensor data when it is presented solely through charts or images without meaningful textual equivalents.  
These accessibility shortfalls are not merely minor usability issues; they translate into critical gaps in equal access. In many jurisdictions, web accessibility for digital services is a legal requirement. For instance, in the U.S., Section 508 of the Rehabilitation Act mandates that federal electronic and information technology be accessible to people with disabilities [6], and in the EU, the EN 301 549 standard [7] outlines similar requirements for ICT products and services. Despite these regulations, most IoT dashboards remain only superficially compliant. Simply providing a short text alternative (alt text) for an image or chart is often insufficient to convey the complex, dynamic information in real-time data streams. A static alt tag like \"Line chart of sensor data\" does not enable a blind user to understand trends, compare values, or detect anomalies from the data. As a result, critical information related to quality of life, workplace safety, or medical outcomes may not be fully accessible to those who need it.  
At the same time, recent developments in artificial intelligence, particularly Large Language Models (LLMs) [8], have opened new possibilities for enhancing accessibility. LLMs can generate context-aware, extended textual descriptions or summaries of data that go beyond what a human might write manually for an alt text [9]. Merging these AI capabilities with a rigorous adherence to web accessibility standards is a promising yet underexplored approach in the context of IoT dashboards.
# An Abstraction Layer Exploiting Voice Assistant Technologies for Effective Human--Robot Interaction
## 5 Use Case Evaluation
### Use Case Architecture Evaluation  
We initially developed our use case on a MacOS with 8 GB RAM and 2,2 GHz Dual-Core Intel Core i7 using Choregraphe 2.1.4 and connected to a hot-spot. Within the same hot-spot, the NAO robot was also connected. Two Amazon EC2 instances were employed to host the SSSCs components. Python was the adopted programming language (for HRICs). Javascript, Python, and Java were adopted for the presented SSSCs. Google Assistant SDK was the voice assistant we employed.  
_The software architecture of a program or computing system is the structure or structures of the system, which comprise software components, the externally visible properties of those components, and the relationships among them_[50].  
Three different methods for software architecture evaluations have been developed at the Software Engineering Institute: ATAM (Architecture Tradeoff Analysis Method) [51], SAAM (Software Architecture Analysis Method) [52], and ARID (Active Reviews for Intermediate Designs) [53]. All the others derive from them [54].  
Because our use case is at an early development stage we chose SAAM for its evaluation. SAAM appeared in 1994 intending to analyze system qualities early in the life cycle of the software architecture. This allows the developers different architectural options. Some pros that SAAM provides are: (i) potential problems can be identified in the early phase of the development; (ii) the documentation can be improved; (iii) the understanding related to software architecture problems can be enhanced; (iv) it relates different involved stakeholders (architect, maintainer, developer, end-user); (v) through the identification of scenarios it provides feedback for its improvement.  
SAAM focuses on modifiability in its various forms (such as portability, subsetability, and variability) and functionality.  
Modifiability allows changes to be made to a system quickly and cost-effectively.  
Portability is the ability of the system to run under different computing environments.  
Subsetability is the ability to support the production of a subset of the system or incremental development.  
Variability represents how well the architecture can be expanded or modified to produce new architectures that differ in specific ways.  
Functionality is the ability of the system to do the work for which it was intended.  
The steps of SAAM that we performed within our analysis are the following:  
Identify stakeholders;  
Develop, prioritize and classify scenarios;  
Perform scenario evaluation;  
Reveal scenario interactions;  
Generate overall evaluation.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 3 Framework Design
### MDP Problem Formulation
#### 3.2.1 State Space  
The agent creates and improves the policy \(\pi\) for selecting an action in the action space considering the input state. Therefore, it is important to decide which observation to use as the state to be used as an input. Because the input required for policy learning is insufficient when the state space is small, the optimal policy cannot be learned. When the size of the state space is large, the deviation greatly increases in the policy-learning process, which can lead to a local optimum, and the load according to the amount of computation increases. So as to quickly reach the global optimum and reduce the amount of computation, the trade-off according to the state space should be considered, and a state suitable for the optimization goal should be selected. The proposed scheme's optimization goal is to create a policy to improve the QoE value, and to do so, the state must use the observations related to the adaptive streaming QoE metric. We configured the state for time step \(t\) as shown in Equation (2).  
\[\mathcal{S}=\left\{\overset{\rightarrow}{x}_{t},\overset{\rightarrow}{d}_{t} \overset{\rightarrow}{n}_{t},b_{t},f_{t},s_{t}\right\} \tag{2}\]  
\(\overset{\rightarrow}{x}_{t}\) is a vector of the throughput of previously requested segments, \(\overset{\rightarrow}{d}_{t}\) is a vector of download times for previously requested segments, \(\overset{\rightarrow}{n}_{t}\) is a vector of the size of the next available video segment, \(b_{t}\) is the client's current remaining buffer, \(f_{t}\) is the predicted buffer occupancy, and \(s_{t}\) is the size of the last requested segment.  
Unlike Pensieve, we use the predicted future buffer state as the state rather than the number of remaining segments. Since general RL-based adaptive streaming aims to maximize the expected cumulative reward, QoE can be improved when the remaining number of segments is used as the state. In particular, Pensieve tends to request more high-quality segments at the end of video streaming. This means that there is a situation in which a higher quality segment can be requested, but a lower quality segment is requested to maximize the expected cumulative reward. Therefore, the predicted buffer occupancy is used as the state to request the highest quality segment that does not cause playback interruption within the available bandwidth. The predicted buffer occupancy is calculated using Equation (3), where \(\tau\) means the playback length of the segment.  
\[f_{t}=b_{t}-d_{t}+\tau \tag{3}\]