esa-sceva/satcom-chunk-collection huggingface dataset:
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 6 Validation of Use Cases
### KPIs and Measurement Methodology
#### 6.1.3 Slice Deployment Time (SDT)  
The overall time required to deliver an active slice over the neutral host infrastructure. In essence, the SDT refers to the time required for the creation and activation of an end-to-end network slice, including the creation and configuration of all the virtual components that are entailed in the slice. This metric takes into account the execution of two main steps in the neutral host platform workflow: the slice creation and activation (see Section 5.2).  
* Slice Creation Time (SCT): refers to the amount of time it takes the Slice Manager to return the results of a submitted slice creation request to an end-user. This operation includes the sequential creation of all the chunks belonging to the slice and the grouping of those chunks. This time is measured from the moment when the creation request of a slice is sent to the Slice Manager, until receiving the confirmation that the slice was created.
* Slice Activation Time (SAT): refers to the amount of time it takes the Slice Manager to return the results of a submitted slice activation request to an end-user. This operation includes the instantiation of the mobile core and the configuration of the corresponding PLMNID in the RAN nodes included in the slice. This time is measured from the moment that the request is sent to the Slice Manager, until receiving the confirmation that the slice is ready to be used. Such confirmation is provided after receiving the acknowledgement from OpenStack about the mobile core instantiation and from the RAN Controller regarding the radio nodes configuration. Note that still additional seconds might be required to complete both operations as well as to finalize the Day1 configurations on the mobile core (based on cloud-init).  
Henceforth, in the scope of the proposed framework, the SDT can be computed according to the following equation:  
\[\text{SDT}=\text{SCT}+\text{SAT} \tag{1}\]  
To compute this KPI, a custom Python script was developed to automate the slice deployment, time measurement and slice removal by sending the required REST API calls to the platform Slice Manager. This script measures and stores the times involved in each operation in a database, simulating user requests (like the ones done via the Dashboard).
## 3 Methodology
### Data Processing  
The left part of Figure 2 shows the three main steps in data processing. In the chosen datasets, Street2 Shop [1] and STL-Fashion [2], the query scene with a bounding box and the ground-truth item \(i\) which corresponds to the bounding box are provided. Given the scene-item pair, first, we do image cropping for the query scene to remove the part containing the ground-truth item. The cropped result is scene \(s\), which is used to calculate the image-level and region-level distances. Secondly, we mask the query scene according to the given bounding box, and then use an object-detection model to detect the items appearing in the masked scene. The detected objects are used to obtain the object-level distance. Thirdly, we build a human mesh from the complete scene to obtain the body-shape information \(b_{s}\). The ground-truth item \(i\), the cropped scene \(s\), the detected items  
Figure 2: Overview of BOCTP through a simplified example. The left part shows the data-processing step, which contains three parts: image cropping, object detection and mesh reconstruction. The right part shows the training step. CNN denotes ResNet-50 pre-trained on the ImageNet dataset and SMPL-X denotes the extracted body-shape parameters. Embedding functions \(g(\Theta,\cdot)\), \(f(\Phi,\cdot)\) and \(h(\Psi,\cdot)\) are used to reduce the feature dimension and extract high-level representations (embedding vectors). In the shown example, the embedding vectors are of size 5, the feature map is of size \(3\times 3\times 2\) and the number of detected item objects is 2.  
in the masked scene, and the body-shape information \(b_{s}\) are used to train the proposed model. Before extracting image features and a building mesh, the images are normalized into intensity levels of \([0,1]\) via linear scaling. In addition, the extracted features are also normalized to \([0,1]\) before training to make sure that the input vectors have same data range.
# Traffic-Aware Optimization of Task Offloading and Content
## 3 System Model and Problem Formulation
### Problem Definition  
In this paper, the aim is to minimize the average time taken to execute and the total energy consumption within each region. This problem is formulated with the consideration of maximum latency and computing power constraints, and can be described as follows:  
\[\begin{array}{l}min\overline{T}^{i}(t),minE^{i}(t)\quad\forall i=1,2,\ldots,I\\ s.t.\quad\left\{\begin{array}{l}\alpha_{i,n}^{i,n}\alpha_{i,n}^{m},\alpha_{i,n}^{c}\in[0,1],\ \alpha_{i,n}^{i}+\alpha_{i,n}^{m}+\alpha_{i,n}^{c}=1\\ s_{m}^{i,n}\in\{0,1\}\\ T_{q}^{i,n}(t)\leq DDL_{q}\\ C_{S_{i}}\leq cs_{m}\\ \sum_{n=1}^{N}\alpha_{i,n}^{m}fm_{i}\leq cc_{m}\end{array}\right.\end{array} \tag{14}\]  
These constraints address task divisibility, binary caching decisions, and timely task completion. They also limit the data cached by tasks to match the edge server cache capacity while ensuring that the combined resource requests of offloaded tasks do not surpass the computing capacity of edge servers.  
It is worth emphasizing that the execution time, energy consumption, and other formulas involved in this article all adhere to scientific principles and follow the foundation work of previous related research. Accurate definitions and thte rationality of these formulas are derived from in-depth research in the cited literature [6; 12; 19; 28], ensuring the scientificity and credibility of our offloading strategies and their application in different scenarios.
###### Abstract
proposed a preventive opportunity maintenance method based on the Weibull distribution to minimize power outage time in railway traction power supply systems [9]. [PERSON] et al. employed Markov processes to analyze the maintenance parameters of power transformers, taking into account various component combinations and multiple maintenance personnel [10]. To address the optimization of periodic imperfect maintenance for power transformers, previous studies have identified common issues [2]:  
1. Limited research on power transformers: The current research on periodic imperfect maintenance for power systems primarily focuses on hydropower stations and wind power, with limited studies on the optimization of periodic imperfect maintenance strategies for power transformers. 2. Limited maintenance history data: Data on power transformers is often scarce, and the same fault mode rarely repeats in the same equipment. This limitation makes it challenging to develop complex periodic maintenance strategies. Additionally, optimized maintenance strategies usually apply to more common and simple maintenance tasks. For example, there is an abundance of data available for common defects such as indicator lights not working or minor oil leakage, while severe defects like fan motor failures have limited or no data. 3. Static maintenance costs and benefits: Previous studies often assume constant costs for maintenance tasks and pay less attention to how these costs change over time. To address these issues, this paper considers the dynamic changes in maintenance costs over time and introduces an imperfect maintenance improvement factor that accounts for these changes with operating time. A maintenance interval optimization model is established with reliability as a constraint. The sequential least squares programming method is employed to solve the model. This paper proposes a transformer maintenance interval optimization method that considers imperfect maintenance and dynamic costs. It dynamically considers the maintenance cycle cost with limited maintenance data and optimizes the maintenance interval for power transformers under different defect patterns. This method, along with the findings of this paper, can be applied to the optimization of maintenance strategies for power transformers owned by power companies of different scales. The structure of this paper is as follows: Section 2 discusses the relevant work on periodic maintenance and imperfect maintenance. Section 3 introduces the dataset used in this research. Section 4 presents the theoretical framework and steps of the transformer maintenance interval optimization method based on imperfect maintenance factors and considering reliability. Section 5 verifies the effectiveness of the method through a comparison of maintenance cases in a power grid in Inner Mongolia and draws relevant conclusions based on the case results.
# Dynamic Range Compression and the Semantic Descriptor Aggressive
## 5 Conclusions  
This paper has shown that professional engineers use the descriptor \"aggressive\" when describing the sound quality of compression techniques that distort the signal. The first listening experiment demonstrated that there is a strong positive correlation between the listeners' scores for distorted and aggressive when rating the same audio stimuli in a controlled listening experiment. It was also shown that compression settings measured to have 0.5% THD and above were rated as both the most distorted and the most aggressive, but there was no significant difference between settings measured to have more than 0.5% THD. Meaning, in this current study, that listeners could not discern any noticeable difference in perceived distortion or aggression amongst audio measured between 0.5% and 1.58% THD. The various time constant settings used in the experiment, which were gleaned from common settings used in the industry, had no significant effect on the perception of distortion or aggressive sonic signatures. Finally, the experiment revealed a strong correlation between settings rated as aggressive and the audio feature roughness, suggesting that this plays a role in the perception of aggressive sounding audio.  
The second listening experiment revealed that compression, which imparts distortion onto the program material, is needed to achieve the most aggressive sound qualities. It appears that fast compression with no distortion (as emulated with the clean software compressor) can affect aggressive sound qualities. Still, the effect is not nearly as significant as using fast-acting compression and distorted artefacts. Both experiments indicated there was no interaction effect between the songs used and the compression settings. Thus, it appears that the songs had little bearing on the results, and the findings from these two experiments should translate to other songs in similar genres.  
Finally, a textual analysis conducted on descriptors gathered during the second experiment highlighted the use of descriptors which relate to distortion. The author plans to carry out a new study which will investigate the lexicon of distortion, looking for the similarities between these terms. The results of this study will afford the academic and professional community with a better understanding of how music producers describe and implement distortion in music production.  
This research received no external funding.  
The author would like to thank [PERSON] for his help and advice on experimental design.  
The authors declare no conflict of interest.  
Figure 8: Long-term average spectrum (LTAS) measurement from the uncompressed audio (green), the clean software compressor (blue) and the 1176 compressor in all-buttons mode (red).
## 2 Materials and Methods
### NIR-HSI Data Acquisition and Extraction  
The images of the samples (\(I_{mw}\)) were captured using the NIR-HSI system based on the arrangement as follows. Two rice varieties (36 seeds of each) were arranged on the 10 \(\times\) 10 grid black square plate, transferred to the moving table controlled by a stepping motor, and scanned line by line using the HSI system. In total, there were 16 scanned plates for 32 rice varieties. The powder was placed into a sample holder plate containing 9 samples of each and adjusted to 5 mm thickness for HSI scanning. The sample arrangement process of powder was carried out by flattering the sample surface using a rod stick without compression. The distance between the sample and the camera was set as 25 cm in order to cover the spatial range of the sample. The spatial and spectral data were obtained when the sample passed the camera field of view (FOV) and were saved in a 3D hypercube containing two spatial dimensions and one spectral dimension. The dark (\(I_{dark}\)) and white (\(I_{white}\)) reference images were acquired to calculate the reflectance value and to correct noise which may come from the environment. The dark reference image (0% reflectance) was collected by covering the camera lens using an opaque cap and turning off the light. In comparison, the white reference (99% reflectance) was acquired by using a white Teflon sheet. The HSI corrected image (\(I_{c}\)) was calculated based on the HSI raw data image (\(I_{mw}\)) using Equation (1), as follows:  
\[I_{c}=\frac{I_{raw}-I_{dark}}{I_{white}-I_{dark}} \tag{1}\]  
The sample spectral information was extracted from the corrected image (\(I_{c}\)) after removing the background by applying the region of interest (ROI) selection step. Both processes, image correction and background removal, were conducted using MATLAB software (The Math Works, Natick, MA, USA, R2019a). To simplify the image correction and spectral extraction process and to explain the data analysis strategy, the flow chart of this process is presented in Figure 2.  
Figure 2: The strategy of hyperspectral data extraction and analysis.
# Design of Adaptive Kalman Consensus Filters (a-KCF)
## 5 An Example  
Consider a linear dynamic system in Equation (1) with \(A=\begin{bmatrix}-2&1\\ 0&-1\end{bmatrix}\); assume that there are three agents distributed in a 2D domain. Each agent embeds a sensing device with measurement capability \(C_{i}\), such as \(C_{1}=\begin{bmatrix}1&0\end{bmatrix},\quad C_{2}=\begin{bmatrix}0&1\end{bmatrix}, \quad C_{3}=\begin{bmatrix}0.5&0\end{bmatrix}\).  
It is clear while that the system itself is not observable by individual agents, it is observable through all of them. In this simulation, we carry out a comparison between the Kalman consensus filtering algorithm proposed by [PERSON] in [17] and our adaptive Kalman consensus filtering algorithm from Theorem 1 on their performance and effectiveness.  
We set up our simulation with the following parameter configuration:  
* Kalman Consensus Filter The process noise distribution matrix is \(B=I_{2}\), and the covariance matrix of the process noise and measurement noise are \(R=1\) and \(Q=0.05^{2}\times I_{2}\), respectively. We assume the initial conditions for the error covariance matrices of the agents to be \[P_{1}(0)=\begin{bmatrix}1&0\\ 0&1\end{bmatrix};\quad P_{2}(0)=\begin{bmatrix}0.5&1\\ 2&0.5\end{bmatrix};\quad P_{3}(0)=\begin{bmatrix}1.5&2\\ 1&1.5\end{bmatrix}.\] We set the initial value of \(\gamma\) as \(1\). Furthermore, we assume that each agent can receive and transmit its own information to the other two; we call this type of communication a full-connectivity topology. Therefore, the associated graph Laplacian has the following expression: \[L=-\begin{bmatrix}2&-1&-1\\ -1&2&-1\\ -1&-1&2\end{bmatrix}.\] (29)
* Adaptive Kalman Consensus Filter For ease of implementation, we only consider the special case illustrated in Section 4 here. The initial guesses for the adaptive gains are \(\gamma_{1}(0)=\gamma_{2}(0)=\gamma_{3}(0)=1\). The consensus weighting matrix \(W_{i}\) for each agent \(i\) is calculated through Equation (7), with the assumption that \(\Omega_{i}=I_{2}\) for all \(i\) and that \(W_{i}=\Pi_{i}C_{i}^{T}C_{i}\).
# Mitigation against DDoS Attacks on an IoT-Based Production Line Using Machine Learning
## 1 Introduction
### Related Works  
The need to use industrial IoT to monitor the production line is described in detail in [8]. It is emphasized that, in order to achieve an intelligent production, the production factors have to have the ability to self-identify, intelligently perceive, make independent decisions, and learn knowledge, which is supported by industrial IoT technology. However, further research is still needed in the area of IoT manufacturing, artificial intelligence algorithms, and machine learning.  
Continuous efforts to increase the productivity of production lines affect security, and the implementation of intelligent manufacturing processes can also affect security in a negative way. The principles, overview, and threats of some cyberattacks on the production line, such as zero-day attacks, false data injection attacks, or denial of service attacks, are described in [9]. In 2017, [PERSON] et al. [10] tested the production line for some vulnerabilities and the main shortcomings identified were the use of default passwords for various services and insufficient SSH encryption.  
The research in [11] shows the link between security and safety of the production line. A cyberattack on a production line security is based on intercepting and modifying messages between control devices, and can lead to intentional misalignment of the production robot and so result in injury to production operators.  
[PERSON] and [PERSON] [12] discussed significant challenges to the security of smart manufacturing systems; DDoS attacks are one of the serious threats to be considered when designing security. [PERSON] et al., in [9], draw attention to the fact that a DDoS attack in such an environment could have catastrophic consequences for production lines. This equipment is critical for efficient and safe operation, as a number of industrial sensors and devices of new-generation control systems have the ability to connect directly to computer networks and the Internet.  
However, specific DDoS attacks and their effects on the production line, are not described thoroughly in the literature. It is assumed that DDoS attacks have a negative impact on the production line and thus on the production process, but there are no documentedreal experiments. If tests of countermeasures against DDoS attacks are verified, they are usually only indirectly performed on simulation models of the production line or existing datasets, such as [10; 13; 14].  
Some components of the production line state that they are able to operate even in the event of network anomalies. However, no further detail or conditions of anomalies are given. Real experiments performed on a running production line show that this is not always the case and seldom when the individual components need to communicate with each other. These facts cannot be revealed during the simulation of the production line and will only be revealed by testing on a real production line.
## 3 Methodology
### Road Region Extraction
#### 3.1.1 Cloud Semantic Segmentation  
In this study, RangeNet++ was applied to segment LiDAR points into multiple categories, including 'truck','vehicle', 'pedestrian', and 'ground'. RangeNet++ is an efficient 3D semantic segmentation architecture that runs in real time. To solve the problem of point-cloud disorder, a range representation method is used that converts each point into image coordinates. The representation method is defined by  
\[\begin{pmatrix}u\\ v\end{pmatrix}=\begin{pmatrix}\frac{1}{2}\big{[}1-arctan(y,x)\pi^{-1}\big{]}&w\\ \big{[}1-(arcsin(zr^{-1})+f_{up})f^{-1}\big{]}&h\end{pmatrix} \tag{1}\]  
where \((u,v)\) represent the image coordinates with height \(h\) and width \(w\), \(f=f_{up}+f_{down}\) is the vertical field of view of the sensor, and \(r=\left\|p_{i}\right\|_{2}\) is the range of each point.  
An illustration of point-cloud semantic segmentation is shown in Figure 3.  
In order to facilitate the observation of different categories, points were marked with different colors according to categories. As shown in Figure 3, 'ground' is green, 'truck' is red,'vehicle' is blue, 'pedestrian' is pink, and 'other' is gray. As the road boundary shape features in the mine scenarios are not prominent, we set it in the 'other' category for semantic segmentation. Due to the mining environment being a closed scenario, object categories are controllable. Thus, semantic segmentation results are stable.  
With the truck moving, one area can be scanned multiple times by LiDAR. However, due to the semantic segmentation noise, this area may be classified into different classes. Inspired by [37], we build a cloud map by stitching clouds with RTK GNSS/INS poses. Then we use statistics to filter noise [37] to improve the accurate and robust of the semantic segmentation.
###### Abstract  
This paper presents the development and validation of a high-resolution photonic and wireless monitoring system for knee-referenced gait cycle analysis. The proposed system integrates a single optical Fiber Bragg Grating (FBG) sensor with a resonance wavelength of 1547.76 nm and electronic modules with inertial and magnetic sensors, achieving a 10 p.m. wavelength resolution and \(1^{\circ}\) angular accuracy. The innovative combination of these components enables a direct correlation between wavelength variations and angular measurements without requiring goniometers or motion capture systems. The system's practicality and versatility were demonstrated through tests with seven healthy individuals of varying physical attributes, showcasing consistent performance across different scenarios. The FBG sensor, embedded in a polymeric foil and attached to an elastic knee band, maintained full sensing capabilities while allowing easy placement on the knee. The wireless modules, positioned above and below the knee, accurately measured the angle formed by the femur and tibia during the gait cycle. The experimental prototype validated the system's effectiveness in providing precise and reliable knee kinematics data for clinical and sports-related applications.  
high-resolution measurement; photonic sensors; fiber Bragg grating; inertial sensors; knee gait cycle monitoring; wireless systems +
Footnote †: journal:  
Article
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 3 Characteristics of DDIS
### Parametric Analysis of DDIS
#### 3.2.2 Influence of DDIS Parameters
The other mechanical parameters are the same as in the earlier parametric analysis. Non-zero values of \(\mu\): 0.10, 0.20, and 0.30, are assumed for the DDIS. As the inertance-mass ratio increases from 0.10 to 0.30, the displacement and acceleration mitigation effects of the DDIS substantially increase. The minimum displacement and acceleration responses coincide with the areas that have the upper boundary of \(\kappa\) and \(\lambda\approx 1.0\). This is ascribed to the almost negligible deformation of the high-stiffness support (i.e., large \(\lambda\)), resulting in the vibration energy being rapidly and directly transferred to and dissipated by the DDE. It can be deduced from the surf plots that the DDIS is not sensitive to change in \(\kappa\) and \(\lambda\), because the surf plots in a blue zone that indicates the low value of the displacement and acceleration responses change slowly when the parameters (\(\kappa\) and \(\lambda\)) vary rapidly. This indicates that a decrease in \(\kappa\) and \(\lambda\) does not considerably reduce the structural performance, including the acceleration and displacement responses. Furthermore, increasing the inertance makes the DDIS more effective for vibration control, in positive association with the increased energy storage provided by a larger inertance. Referring to the results of parametric analysis, it is suggested to design the DDIS with a medium inertance-mass ratio (\(\mu\approx 0.5\)), a large DDE \(\kappa\geq 0.5\), and a tunable spring (\(\lambda\leq 1.0\)).