esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 5 Conclusions  
Existing tower tilt monitoring methods include the lead hammer method, theodolite, and manual inspection, but poles and towers are widely distributed, and several are deployed in inaccessible locations. In addition, transmission pole and tower accidents often occur during natural disasters, making these methods ineffective for monitoring the transmission poles and towers. Given the poor real-time performance of existing monitoring methods of tower tilt, this paper has proposed a monitoring method of tower tilt based on a monocular vision image. A mathematical model is developed by analyzing and calculating the collected information, including image processing and contour extraction. Using the improved monocular vision monitoring method in this paper, the perception accuracy of the tower tilt was improved by 7.5% by establishing the optimal model and experimentally testing its attitude in the laboratory. This is an essential indicator of tower stability and attitude, which can be predicted to a precision of \(\pm\)2 mm with the axial eccentricity distance. A major advantage of this method is that it is real-time, simple to operate, and has a short response time for detecting tower inclination. It reduces the inspection intensity of the staff significantly, and it also makes it possible to gauge the tilting degree of the tower in time, which ensures a safe and stable transmission line operation.  
Conceptualization, X.W. and M.W.; methodology, C.L. and Z.S.; validation, C.L. and L.Z.; data curation, Y.Y. and M.W.; and writing--original draft preparation, Z.S. and C.L. All authors have read and agreed to the published version of the manuscript.  
Jilin Province Science and Technology Development Program Project supported this research. Funding Number: 20220201075 GX.  
Not applicable.  
The data used to support the findings of this study are included in the article.  
The authors declare that they have no competing interest.
## 1 Introduction
Although this method was computationally efficient in the frequency domain, it could not produce the full-resolution salient map and the result was unsatisfying. Achanta [7] presented FT method, which obtained a pixel's saliency by measuring each pixel's color difference from the average color in the image to exploit the spatial frequency content of the image. Finally, HC [8] method constructs the color histogram only using color to obtain the saliency; however, this method does not perform well with complex texture scenes in certain instances. Recently, researchers have increasingly investigated methods of detecting image saliency by simultaneously considering the local and global feature, with some methods achieving better performance. For example, Perazzi _et al._ proposed a conceptually clear and intuitive algorithm (Saliency Filters (SF)) [10] for contrast-based saliency estimation based on the uniqueness and spatial distribution of those elements that combined the two measures in a single high-dimensional Gaussian filtering framework. In this way, they simultaneously addressed both local and global contrast; however, substantially more parameters are needed to produce satisfactory results because the method sometimes falsely identifies the background as the salient region. Yong _et al._[24] proposed Cell Contrast and Statistics (CCS), which utilizes both the color contrast model and space statistical characteristic model to detect saliency based on the constructed image cell. In 2014, Gokhan _et al._ exploited the contributions of the visual properties of saliency in two methods. The first method [25] is based on machine learning and initially extracts biologically plausible visual features from the hierarchical image segments; then, it uses regression trees to learn the relationship between the feature value and visual information. The second method is FASA [6], which obtains a probability of saliency by feeding the statistical model with the spatial positions and sizes of the quantized colors estimated early and then combines the probability of saliency with the global color contrast measure to obtain the final saliency. Chen _et al._[26] addressed multiple background maps in an attempt to generate full-resolution salient maps in linear computation time and with a low probability of falsely masking background as salient regions. However, the method would sometimes fail as a result of the background estimation being invalid. Overall, recent improvements of saliency detection indicate that using the local feature combined with the global feature to accurately detect the salient object/region in the image is the general pursuit of mainstream research. The salient object/region would be clearly separated from cluster scenes with the overall shape or contour and subtle texture details. However, these existing methods have not effectively used the local texture information working together with color information for saliency detection.
## 4 Results  
### Spatial Representativeness Determined by Sill Value  
A paired t-test was performed on the sill value for each set of paired TM and MODIS data. The spatial variation was determined according to the criterion that if the \(p\)-value was zero, it confirmed the null hypothesis--land surface variation at adjacent scales was not different, and the pixel can represent the larger scale scale. For all data pairs, the histogram of spatial variation is shown in Figure 2. The median value was also used as the indicator of effective spatial resolution, as in [PERSON] et al. (2010). In this situation, the median value was 2, which suggests that the variation in the land surface was subtle within the 21 \(\times\) 21 TM scale, and the proper aggregation scale for TM albedo data was 21 \(\times\) 21 pixels. The 21 \(\times\) 21 aggregation scale represented a 630 m spatial scale, which is consistent with the result of ([PERSON] et al., 2011).  
Figure 2: The proper aggregation scale histogram according to the sill value of each scene. The scale is defined with TM pixels. The scene number is the total number of the scenes at each scale where the MODerate Resolution Image Spectroradiometer (MODIS) pixel represents the spatial scale well.  
Table 3 shows the proper aggregation scale for every land cover type of AmeriFlux sites. For most land cover types, the land surface was stable within 21 \(\times\) 21 TM pixels, and for mixed forest and croplands, the proposed effective spatial resolution was 690 m (approximately 23 \(\times\) 23 TM pixels).
## 4 Results
### Performance Analysis by Video Type
#### 4.2.1 Uav  
This group encompasses all images that do not belong to a specific category, representing 60% of the database. As illustrated in Table 4, DETR demonstrates higher accuracy in this category, reflecting its capability to effectively manage standard scenarios. The F1-Confidence of DETR is also significantly high, indicating an effective balance between precision and recall in these scenarios. YOLO, although less accurate, maintains high efficiency, rendering it suitable for applications where speed is critical. The detection results for this category of images are presented in Figure 4.
# Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing
## 1 Introduction
[22] is a local multiple regression technique used to estimate variable relationships that vary spatially and can effectively explore the impact of multiple factors on GPP. Furthermore, studies have indicated varying impacts of air pollutants and meteorological factors on vegetation growth at different stages of urbanization. The Geographically and Temporally Weighted Regression (GTWR) model introduced by [PERSON] et al. [23] takes into account the temporal dimension to address spatiotemporal heterogeneity, which has considerable applicability in this problem. AlthoughGWR and GTWR have been widely used in various studies, they are less frequently utilized in examining the determinants of GPP. In this study, we used [PERSON]'s slope estimation, Mann Kendall (M-K) trend test, GWR, and GTWR methods to investigate the spatiotemporal changes of GPP in Beijing and explore the impact of environmental and human activity factors on GPP using AGPP data from 2000 to 2020 and meteorological and air pollutant data from 2014 to 2020. Additionally, we also employed land use/land cover (LULC) data to analyze the impact of LULC transitions on Annual Gross Primary Productivity (AGPP). Through this comprehensive approach, we aim to optimize the spatial structure and facilitate the city's goal of achieving carbon neutrality.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 6 Sleep Pattern Extraction and Sleep Quality Analysis
### Cohort Analysis  
Dividing patients into groups (precision cohort) with common characteristics is crucial to providing personalized care. Thus, we conducted the same pattern extraction methodology as discussed in Section 6.3.2 across the entire patient population, using the daily sleep profile representation (Section 6.4.1) to identify patient groups with similar sleep patterns. The resulting sleep patterns are depicted in Figure 10. Cluster-0 and cluster-3 are the two most dominating sleep patterns, where cluster-0 represents the healthiest pattern, according to [16]. Cluster-4 and cluster-5 represent two sleep profile patterns with many long naps. Cluster-1 refers to long night sleep patterns. The last cluster represents days where only a few short naps were recorded. Cluster-2 indicates days without any sleep activities detected, which is often due to the user being away from home. These clusters can be considered to be the 'behavior pattern dictionary' of our patient database, and each pattern is referred to as a 'word' in the 'dictionary'. The dictionary can be enriched over time, when new patterns are formed.  
Given the extracted 'dictionary', a user's recent sleep profile can be represented by the appearance frequency of each 'word'. Table 4 lists the 15 bed-sensor users in our database, and the number of days they were in a respective sleep pattern. Thus, a user's sleeping pattern during the monitored period can be represented as a 'bag of words', indicating the frequency of appearance of each 'word'. Based on the resulting user representation, a user segmentation was performed with HDBSCAN, resulting in 4 clusters:  
* Group-A: patients 1, 14, and 15
* Group-B: patients 2, 5, 7, 8, and 12  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sleep Types** & **Metrics** & **Value Ranges** & **Sleep Score** \\ \hline \multirow{8}{*}{Night sleeps} & [7, 8] h & 4 \\  & Night sleep duration & [5, 7) h or (8, 9] h & 3 \\  & (\(D_{NS}\)) & [3, 5) h or (9, 12] h & 2 \\  & [1, 3) h or (12, 15) h & 1 \\ \cline{2-4}  & & 0 & 4 \\  & Number of & 1 & 3 \\  & awakenings and breaks & 2 & 2 \\  & (_if_\(1\leq D_{NS}<15\) h) & 3 & 1 \\ \cline{2-4}  & & [95, 100] \% & 4 \\  & Sleep efficiency & [90, 95] \% & 3 \\  & (_if_\(1\leq D_{NS}<15\) h) & [85, 90] \% & 2 \\  & & [80, 85] \% & 1 \\ \hline \multirow{4}{*}{Day naps} & Number of naps & [0, 1] & 2 \\  & (_if_\(1\leq D_{NS}+D_{DN}<15\) h) & [2, 3] & 1 \\ \cline{1-1} \cline{2-4}  & Nap duration (\(D_{DN}\)) & [0, 50) mins & 2 \\  & (_if_\(1\leq D_{NS}+D_{DN}<15\) h) & [50, 100] mins & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Daily sleep score quantification metrics.  
Figure 9: Daily sleep score based on the metrics defined in Table 3.  
Group-C: patients 4, 6, and 13  
Group-D: patients 9, 10, and 11  
User 3's daily sleep pattern could not be assigned to any of the patient groups. The care manager confirmed that this user was in a unique health condition, which keeps him/her in bed most of the time.
# Local Community Detection Based on Small Cliques
### Related Work  
Numerous local community detection algorithms have been proposed [16; 17; 18; 4; 19]. Many of these algorithms can be grouped as greedy community expansion [4], which also provides the basis of other (more complex) algorithms. It can be described best as greedily adding the node to the community that currently has the maximum value of some function that assigns a value to each node. This greedy community expansion usually stops once the value of every node that could still be added is negative. Often measures based on the ratio of internal edges to edges that are connected to the rest of the graph are used. BME1 [20] additionally uses the distance to the seed node to assign less importance to edges from nodes further away from the seed node. Other algorithms like iGMAC [21] also consider a wider neighborhood of the nodes to be added and are thus computationally more expensive.  
However, there are also algorithms that work with a different strategy, which is not based on greedy community expansion. One of these is PageRank-Nibble, which works by locally approximating PageRank-vectors [22]. It first computes a ranking of the nodes and only then finds a community based on this ranking. Flow-Pro [23] starts a network flow from the seed node and adds nodes to the communitiy when a sufficient part of the flow arrives at them. LEMON is an approach based on local spectral optimization [24]. From these algorithms, we only include PageRank-Nibble in our evaluation as for the other algorithms no efficient implementation is available.  
Many of the above-mentioned algorithms can also start from a set of seed nodes instead of a single seed node. This can be used to identify a community more precisely as discussed in [25]. Starting from a maximum clique as proposed in [9] is a similar idea that helps identifying the community more precisely.  
Triangles are not only an important part of our Triangle Based Community Expansion algorithm and the LTE algorithm [15], but are also used in other community detection algorithms [10; 19; 26]. Triangles have also already been popularized with respect to social networks by the social sciences with the term of triadic closure and are the basis of the clustering coefficient [27; 28].
# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
###### Abstract  
This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results.  
crude oil futures; volatility; GARCH-MIDAS; mixed-frequency data +
Footnote †: journal: Applied Sciences
## 3 Simulation Results and Analysis
### Results and Verification
The results obtained in this study are applied to the literature related to the determination of the driving coil energization method. The comparison of the experimental results obtained from different device models with the results of this study all confirm the relationship between the driving coil inter-stage distance and the selection of the energization method. This validates the accuracy and effectiveness of the results of this study.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
###### Abstract  
The majority of cloud computing consists of servers with different configurations which host several virtual machines (VMs) with changing resource demands. Additionally, co-located VMs are vulnerable to co-resident attacks (CRA) in a networked environment. These two issues may cause uneven resource usage within the server and attacks on the service, leading to performance and security degradation. This paper presents an Agent-based VM migration solution that can balance the burden on commercially diverse servers and avoid potential co-resident attacks by utilizing VM live migrations. The Agent's policies include the following: (i) a heuristic migration optimization policy to select the VMs to be migrated and the matching hosts; (ii) a migration trigger policy to determine whether the host needs to relocate the VMs; (iii) an acceptance policy to decide if the migration request should be accepted; and (iv) a balancer heuristic policy to make the initial VM allocation. The experiments and analyses demonstrate that the Agents can mitigate CRA in a distributed way to mitigate the associated risks while achieving acceptable load balancing performance.  
virtual machine live migration; load balancing; co-resident attacks; cloud computing; multi-agent systems +
Footnote †: journal: Applied  
1 The Key Laboratory on Reliability and Environmental Engineering Technology, Beihang University, Beijing 140191, China 12 School of Reliability and Systems Engineering, Beihang University, Beijing 140191, China 2*Correspondence: [EMAIL_ADDRESS]
## 4 Results
### Comparison of Subjective Satisfaction with and Without AR System Application  
Table 4 presents the findings from the comparative analysis of user subjective satisfaction with and without the AR system. Our analysis revealed that the application of the AR system resulted in statistically significantly higher satisfaction (\(p<0.05\)) across multiple dimensions. Specifically, participants reported greater satisfaction with the AR system in terms of overall experience, interest/fun, motivation, perceived exercise effectiveness, perceived movement accuracy, and the total satisfaction score, when compared to the conditions where the AR system was not used.
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 1 Introduction
Then, regarding relay selection of each node as optimization variable, a network lifetime maximization problem that considers both energy consumption rate and residual energy is formulated with the target of maximizing the minimum lifetime of sensor nodes in the network. Considering that the optimization problem is an NP-hard binary integer problem and intractable, especially in the case where the number of the nodes in WBANs is large, a heuristic iterative solution is designed to rapidly solve the optimization problem with low time complexity. The performance of the proposed scheme is evaluated through numerical simulations. The results indicate that the proposed scheme has better performance in network lifetime compared with other existing methods in a human body scenario. Moreover, the results show that the heuristic solution can effectively reduce the time complexity with only a negligible performance degradation gap from the optimal value. In addition, to comprehensively illustrate the advantages of the proposed scheme, we also conduct simulations based on a more generalized WBAN model specified in the IEEE 802.15.6 standard. The simulation results demonstrate that the proposed scheme also excels in a more general application scenario. At the end of evaluation, we make a brief discussion on the implementation of our scheme in a real WBAN system to evaluate the feasibility of it. The rest of this paper is organized as follows. Section 2 consists of related works. In Section 3, the system model considered in this paper is described. The optimization variables, optimization problem formulation and corresponding constraints are defined in Section 4. The heuristic solution is then introduced in Section 5. Simulation results, analyses and implementation discussion are presented in Section 6. Finally, we conclude the paper and briefly discuss the future works in Section 7.
## 3 Results  
### Study Selection  
Figure 1 illustrates the process of study inclusion in the present systematic review. Initially, a broad search strategy was employed, leading to the identification of a substantial number of records. Subsequently, strict refinement criteria, based on robust methodological standards, were applied, resulting in the selection of 32 studies that met the inclusion criteria and were incorporated into the final synthesis. Additionally, three studies published after the initial cutoff date of January 2023 were included due to their high relevance to the topic under investigation. Consequently, the total number of studies included amounted to 35.  
Each of these studies evaluated key aspects that allowed for a comprehensive comparison of the methodologies employed. To this end, data such as study type, authors and year of publication, methods for assessing joint stiffness (via range of motion [ROM] or other techniques), sample characteristics, therapeutic interventions, outcome measures (including active and passive ROM, patient satisfaction, pain intensity, and complications), main findings, risk of bias (considering study design, blinding, management of participant loss, and control of co-interventions), and treatment duration were extracted and analyzed.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 3 Separation Calculation Model Based on Track Prediction
### Separation Calculation Model  
The separation calculation model comprises two main aspects: the conflict frequency and the collision probability in conflict. Conflict frequency refers to the frequency at which two UAVs come close to each other and fall below the separation, represented by the number of conflicts per flight hour, which is the frequency of separation violations. The conflict frequency depends on traffic density, route structures, and the separation. The larger the separation, the higher the conflict frequency in the same operational airspace. The collision probability in conflict depends on various factors, such as the UAV size, speed, position error, and CDR capability. Under a given separation, smaller UAVs, lower speeds, more accurate positions, and faster CDR responses will lead to a lower collision probability.