esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Previous works
In frequency domain zero-padding, the number of zeros are padded in the middle of Fourier Transform of cross-correlation \(i(n)\). Notice that zero-padding in frequency domain increases the discrete frequency by a certain factor which eventually results in time-domain interpolation in cross-correlation signal \(i(n)\). Zero-padding in frequency domain is a useful tool to improve the peak location accuracy, but it increases the computational complexity [14] and storage requirement associated with inverse Fast Fourier Transform (FFT) operations [15]. Notwithstanding the above mentioned shortcomings, cross-correlation is a still useful tool for time delay estimation as shown that cross-correlation with no pre-filtering is an optimal maximum likelihood estimator to estimate the time delay between two similar signals if the noise processes  
Figure 3: Output of white light interferometer ( \(I_{S}\) in Equation 1 or \(I_{R}\) in Equation 2). \((n)\), \(w_{S}\left(n\right)\) of signal \(i_{R}\left(n\right)\), \(i_{S}\left(n\right)\) are white noises and at least one of signals has high signal-to-noise ratio greater than 10 dB [16]. The outcome of the time delay estimation depends on the combined performance of coarse estimation (zero order fringe peak identification) and sub-sample resolution estimation of time delay. In this article, a new signal processing algorithm which can accurately identify the zero order fringe peak of cross-correlation \(i\left(n\right)\) of two fringe scan output signals of a WLI is proposed. This algorithm still uses a cross-correlation technique taking advantage of simple implementation. But this algorithm combines the hypothesis test as a coarse estimation to reduce the possibility of mis-identification of peak with fine tuning algorithm as a sub-sample resolution peak estimation to overcome the shortcomings of quadratic interpolation or frequency domain zero-padding. The proposed signal processing algorithm uses a software approach, which is potentially inexpensive, simple and fast. And, this proposed signal processing algorithm has a low peak mis-identification rate of 3 x 10\({}^{-4}\) at 31 dB _SNR_ and has a high precision fine tuning capability down to 5 x 10\({}^{-4}\)  fringe as will be shown from the computer simulation results.
## 4 Results and Discussion  
### Digital Twins in RGCE  
The RGCE can be considered as one comprehensive digital twin of the global internet, consisting of several interconnected digital twins. As a basis, there is the RGCE's internet, consisting of several realistic functions and services: for example, BGP routing and realistic structure with public IP addresses, or global PKI infrastructure for certificates. The RGCE includes a wide range of internet public services, such as news sites, social media, and discussion forums. The other layer of the digital twins in the RGCE are the specific organization environments. There is a wide range of organization environments implemented for the RGCE: financial organization, electricity companies, and healthcare-related organizations, to name a few examples. As in the real world, there are interrelated connections between those different elements and, for example, a cyber attack against an electricity company may cause cascade effects to other elements. Similarly, attacks against some crucial software may cause effects on another organization, or if the other organization offers services to another, those might be interrupted because of the cyber attack, which allows training for co-operation between organizations [16].  
In this study, the digital twin of the food-production value chain is modeled. It consists of several organizational elements connected digitally which are dependent on those previously implemented services. These parts of the food production value chain are, for example, smart farming, food-production companies, and retail chains. For example, if there is a cyber attack against a refrigeration unit of the food-production organization, it affects other elements, and that same unit with the same vulnerability can also be used in retail.  
During the implementation of those digital twins, there is a lot of co-operation between the real actors of the industry sectors. By that co-operation, the requirements for the implementation can be gathered to ensure that the implemented digital twins are, from the cyber security training and exercise point of view, as realistic as possible.  
The schematic idea behind every digital twin in the RGCE is presented in Figure 6. The requirements describing processes and structures related to the domain are passed from the real world. The feedback to the real world after an exercise concerns organizational practices and processes.  
Figure 6: Digital representations in RGCE.
## 1 Introduction
To ensure that the analysis accurately reflects real world conditions, the dataset used in this study was derived from \"Starlink-on-the-Road-Dataset\", previously used in [11]. Since Starlink is not yet operational in our study area, we opted for this dataset because it provides a reliable basis for evaluating the effectiveness of our approach in a dynamic satellite environment. The results of this study confirm that the analysis of standard deviations and score variation rates reveals higher stability of SD-TOPSIS compared to the other algorithms under study. SD-TOPSIS stands out for its ability to maintain stable performance even when faced with fluctuations in performance criteria and ensures better connection stability. The remainder of this paper is organized as follows: Section 2 reviews related works on heterogeneous satellite networks and handover management strategies using TOPSIS and its variants. Section 3 outlines the TOPSIS variants under study in the context of LEO networks and the methodology used. Section 4 discusses the simulation results and Section 5 concludes this work and provides insights for future research.
## 1 Introduction  
### Research Background and Motivation  
Globally, an aging population provides a good indicator of how health services have progressed in both developed and developing countries. However, to better provide these benefits, a series of challenges must first be met. One extremely important challenge is to train much needed healthcare professionals who specialize in providing care to seniors who often suffer from a variety of chronic diseases associated with aging, and design environments that incorporate wireless technologies and communications systems adapted to the needs of the geriatric community [1]. Projections show that between 2000 and 2050 the number of people above the age of 60 will increase from 11% to 22% worldwide, meaning that persons in this age group will number approximately 2 billion [2].  
Aging presents a series of challenges for the entire world population, primarily because seniors slowly lose their ability to be self-sufficient due to chronic diseases, physical and/or mental disabilities, or the general frailty that characterizes the aging process [2]. Any of these conditions represent factors that limit the elderly or endanger their lives, even within the confines of their homes. Consequently, 24-hour-a-day monitoring of the elderly can improve attention provided for chronic or acute health concerns, accidents such as falls, as well as a series of other conditions that can detrimentally affect the elderly. For example, falls represent the second most common cause of death by accident among the aged, making persons over the age of 60 the most vulnerable population group. Additionally, non-fatal falls by the elderly can severely compromise quality of life and/or represent considerable medical expenditures (_i.e._, in Finland $3,611 dollars per injury, in Australia $1,049 dollars per injury) [3].  
Providing remote healthcare monitoring and services presents a series of important challenges; therefore, it is important to generate remote monitoring strategies to provide primary healthcare services and mechanisms that allow seniors to receive long-term assistance. To better meet the needs of the aging population, research has significantly advanced both the theory and application of e-Health technologies; largely because their application can reduce costs generated by patient monitoring and provide a variety of advanced services [4]. Importantly, studies show that the elderly generally accept e-Health technologies and consider them beneficial [5].
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
### Scheduler Module Technical Implementation
#### 3.2.2 Constraint Framework  
The optimization employs a three-level hierarchical scoring system: hard constraints ensuring feasibility (temporal bounds, resource limits, task precedence), medium constraints reflecting business objectives (task assignment rewards, prioritization), and soft constraints optimizing operational efficiency (early scheduling, resource state optimization, latency minimization). The mathematical formulation includes both general constraints and 3 GPP-specific procedural constraints to ensure compliance with telecommunication standards.
# An Efficient Multi-Lane SpaceFibre Core for Spacecraft Data-Handling Networks
## 2 SpaceFibre Standard
Moreover, PAD control words are inserted to a data frame if the number of words in a data frame is not a multiple of \(M\), where \(M\) is the number of data-sending lanes. A data word from a broadcast frame or an idle frame is replicated and sent over all data-sending lanes. To simplify the description in this study, a replicated row (RR) is introduced, which is composed of replicated words (RWs) with the identical value. Figure 1: Data frame format for a multi-lane link. An alignment process is employed by the Multi-Lane layer for data coherency. ACTIVE control words are sent by each end of a multi-lane link to exchange the information regarding active lanes. ALIGN control words are used to align a data-receiving row. An ACTIVE word and an ALIGN word are repeatedly sent if the far-end needs to align data-receiving lanes. The alignment process is controlled by the alignment finite-state machine (FSM), dictated by the SpaceFibre protocol. The Lane layer provides character synchronization, clock compensation, 8B/10B encoding, and 8B/10B decoding. An 8B/10B decoder introduces disparity check and detects invalid codes to find bit-flip errors. Moreover, the layer is applied to lane initialization and error detection. Each lane of a multi-lane link contains its own lane layer. The Physical layer is responsible for serializing the symbols provided by the Lane layer and for transmitting the serial data signal over copper cables or optical fiber cables. Meanwhile, the physical layer receives the data signals from physical mediums and de-serializes the signals.
# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 1 Introduction
It has certain translation invariance, rotation invariance, and illumination invariance, which can better adapt to target deformation, scale changes, and occlusion. It is very important to design an effective re-detection method to improve the robustness of visual tracking in the case of tracking failure. In this work, we employ the EdgeBox [15] to achieve object online re-detection and use the predefined threshold as the activation condition for re-detection. However, it did not perform very well for all the video sequences. To this end, we proposed a self-adaptive activation method to stimulate the re-detection component. We compared the size of the response map peak and its corresponding peak-to-side lobe ratio (PSR) [5] score generated by DCFs. By this method, the detector can be well awakened when the condition is satisfying (Section 3.3.1). For the model updating, most existed tracking algorithms often update the tracking model at a fixed interval or frame-by-frame [16; 17; 18; 6; 12]. These approaches have some obvious disadvantages. If objects go through complex appearance changes for instance occlusion and disappear in the current frame, these situations bring will fault background information. The wrong information is delivered to subsequent frames and decrease the performance of tracking after accumulating for a long time. The end result is tracking drift. Hence, we propose an effective model updating method similar to re-detection method. We compare the size of the response map peak and its corresponding _PSR_ score generated by DCFs. By this method, the tracking models can be updated in time to improve tracking robustness and avoid tracking drift effectively due to noisy problems (Section 3.4). The main contributions of this work are as follows:  
1. The hierarchical features of CNNs are used as feature representation to handle large appearance variations, and we learn a spatial-temporal context correlation filter on each CNN layer as a discriminative classifier. We use multi-level correlation response maps for fusion to infer the target location. For scale estimation, we train DCF based on scale pyramid representation and estimate the desired object scale from the best score frame. 2. We employ the EdgeBox to redetect when tracking failure occurred and proposed a novel re-detection activation method. For model updating, we propose a novel model update method to solve the model noisy problems. 3. We extensively validate our method on benchmark datasets with large-scale sequences and extensive experimental results demonstrated that the proposed tracking algorithm is superior to the state-of-the-art methods in terms of accuracy and robustness.
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 4 Case Study, Results, and Discussions
### Industry Data  
The data collection stage was initiated throughout a meeting with a multi-functional team of the company, including specialists from the following areas: manufacturing, engineering, product engineering, purchasing, quality, materials, logistics, production, and supplier development. In this event, the problem was discussed together with the business development team. The objective was grouping parts by similarity for future cost comparison, following the steps above:  
Step 1--Load the complete SAP system database: at this step, the industry material planners identified all items by part number and checked by stock movements over the last three months. Note that just recently purchased parts with interest in cost analysis are included on the initial data set;  
Step 2--Select continuous quantitative data: at this step, product engineering, manufacturing engineering, and accounting departments were invited to discuss the attributes of each part number downloaded. Therefore, clarifications on the nature of the data could be discussed for further classification considering quantitative and qualitative aspects. Observe that this case study only considers quantitative data;  
Step 3--Eliminate components with missing data: some selected parts were incomplete. We highlight that most of the clustering does not work well with incomplete data. Therefore, part numbers with an incomplete data set were suppressed;  
Step 4--Consider NCM (Mercosur Common Nomenclature) as the lower limit of the number of groups to be created: clustering techniques' challenge is determining the number of clusters in advance. Based on this premise, several discussions with manufacturing and product engineering teams were carried out to identify the potential interval of dataset labels. The conclusion was that the NCM could be a good reference as lower limit (200) and engineering class number as the upper limit (621).  
The first loading of systemic information raised 4267 items with 14 dimensions, among which the multi-functional team selected the following dimensions: Weight, Length, Width, Height, Volume, and Density. At the end of the 3 rd step, 2765 parts were included in the database, which presented six dimensions for each sample.  
As guided by the multi-functional team in the 4 th and 5 th steps, 200 different NCM codes and 621 different engineering class codes were used as a reference for defining the number of groups. With this background, it was expected that this study could show an ideal number of groups between 200 and 621.  
For this reason, a search of the number of clusters was made with the following pre-established quantities: 60, 160, 260, 360, 460, 560, 660, 760, and 860, as most of the addressed algorithms requires the number of groups a priori. Values of 200 and 621 were purposely not included because manual analysis found inconsistencies in both cases.
# Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement
## 5 Experiments and Analysis
### Ablation Analysis of Our Method  
We conduct ablation studies to verify the importance of the methods in Sections 3 and 4, including the importance of correcting errors caused by noise, the effect of motion distribution accuracy, and the contribution of Convolution module and Gaussian prior.  
Importance of correcting errors caused by noise: To enhance the phase difference of the MUSIC algorithm, we introduce time-of-flight, but the resulting STO and SFO at the same time introduce phase errors in the received signals of different packets. As shown in Figure 5, the left side is the CSI map of the original sampling, and the right side is the result after using a multivariate linear combination. It can be seen that the phase offset has been aligned.  
Effect of motion distribution estimation: The human action velocity distribution is used as the input of the network, so the accuracy of the action velocity distribution estimation directly affects the effect of action recognition, which in turn depends on the accuracy of the human body position estimation. To verify the effect of Section 3, we add joint parameters and human body dynamic signal amplification steps for comparison experiments.  
As shown in Figure 6, it can be seen that the accuracy rate has been significantly improved after introducing our method.  
Contribution of dynamic Convolution module and Gaussian prior: By using the weight function to adjust the convolution kernel parameters corresponding to different actions dynamically, the model extracts action features more accurately. To consider the characteristics of time series, Gaussian priors are added. As shown in Figure 7a, the features of similar actions are confused, and the features of the same action are scattered and easily identified as other action features. Figure 7b shows the classification performance of the model using the Convolution module and Gaussian prior. We can find that the feature difference between different actions increases, and at the same time the action features that are opposite in time can be better distinguished.
## 3 Algorithmic Mathematical Analysis
### Algorithm Validity Analysis  
It is assumed that the direct distance \(a_{ij}\) between any two star nodes satisfies \(a_{ij}\sim N\big{(}\mu,\,\sigma^{2}\big{)}\).  
It is easy to know that the flooding and backtracking algorithm compares all possible paths between two star nodes and finds the shortest path among them. If there is more than one shortest path, the path with the least number of inter-star nodes will be selected. Therefore, the flooding and backtracking algorithm is definitely optimal [23].  
For greedy algorithms, the average expected route path distance is:  
\[G_{path}(N)=\frac{N}{2}\cdot\mu\cdot\epsilon\cdot\frac{1}{\sigma^{2}} \tag{13}\]  
\(\epsilon\) is an undetermined parameter. For greedy algorithms, in principle, the larger \(\sigma^{2}\) is, the smaller its expected distance is. The average expected path distance is proportional to \(\mu\) and inversely proportional to \(\sigma^{2}\).  
For the balanced partition routing algorithm:  
\[\left\{\begin{array}{c}a_{ij}\sim N\big{(}\mu_{1},\,\sigma_{1}^{2}\big{)}, \,i,\,j\subseteq\varphi\\ a_{ij}\sim N\big{(}\mu_{2},\,\sigma_{2}^{2}\big{)},\,i\subseteq\varphi,\,j \
subseteq\varphi\\ \mu_{1}<\mu<\mu_{2},\,\sigma_{1}^{2}<\sigma^{2}<\sigma_{2}^{2}\end{array}\right.\]  
\[C_{path}(N)=\frac{\frac{N}{3}-1}{N-1}\cdot\mu_{1}\cdot\epsilon_{1}\cdot\frac{ 1}{\sigma_{1}^{2}}+\frac{\frac{2N}{3}}{N-1}\cdot\left(2\mu_{1}\cdot\epsilon_ {1}\cdot\frac{1}{\sigma_{1}^{2}}+\mu_{2}\cdot\epsilon_{2}\cdot\frac{1}{\sigma _{2}^{2}}\right)<C_{pathe} \tag{14}\]  
Among them:  
\[C_{pathe}=2\mu_{1}\cdot\epsilon_{1}\cdot\frac{1}{\sigma_{1}^{2}}+\mu_{2}\cdot \epsilon_{2}\cdot\frac{1}{\sigma_{2}^{2}} \tag{15}\]  
Further, it can be concluded that:  
\[G_{path}(N)-C_{pathe}=\frac{N}{2}\cdot\mu\cdot\epsilon\cdot\frac{1}{\sigma^{ 2}}-\left(2\mu_{1}\cdot\epsilon_{1}\cdot\frac{1}{\sigma_{1}^{2}}+\mu_{2}\cdot \epsilon_{2}\cdot\frac{1}{\sigma_{2}^{2}}\right) \tag{16}\]The greater the value of N, the more likely the greedy algorithm is to be less efficient than the balanced partition routing algorithm. When the number of inter-star nodes is small, specific variance and average step size should be considered for specific analysis.
## 3 Methodology
### Performance Metrics  
The performance of proposed approach is measured by using below three major parameters:  
1. Cache hit rate: As shown in Equation (2), it is the ratio of the requests satisfied by the caching node to the total number of requests. \[HitRatio=\frac{\sum Hits}{\sum(Hits+Missed)}\] (2)
2. Average response: It is the average time required to forward the requested content to the requester node in a network. It is used to minimize the hop count, to decrease the response or waiting time of a request.
3. Energy saving rate: Energy saving rate represents the amount of energy saved by any caching policy in comparison to a non caching in a system.