esa-sceva/satcom-chunk-collection huggingface dataset:
# Prospects of Structural Similarity Index for Medical Image Analysis
## 3 Current Improvement in SSIM
### Three-Component Weighted SSIM  
A three-component weighted SSIM or three-component SSIM (3-SSIM) is an upgraded SSIM used to overcome the issues in blurred and noisy images. This 3-SSIM has the same objective as GSSIM, and thus 3-SSIM uses a similar step in the gradient map comparison of reference and distorted images. In other words, 3-SSIM was inspired by GSSIM [16] and three-component image comparisons segmenting the images into three components, namely edge, smooth, and texture regions [19]. The similarity score can then be calculated utilizing the assigned weights for each region.  
A 3-SSIM was first introduced in 2009 and was the initial research direction for developing the SSIM into a four-component weighted SSIM (4-SSIM), as described later in Section 3.3. There are four steps to compute 3-SSIM. First, the SSIM index is calculated. Second, the images are divided into three regions: edge, smooth, and texture regions. An edge region can be found when the gradient is large, whereas a smooth region is relative to a small gradient. In addition, the texture region is extracted from the two thresholds. Third, non-uniform weights are implemented into the SSIM from the three regions. Fourth, the weighted SSIM is united by extracting the weighted average; hence, a similarity score can be obtained [19]. Figure 6 shows the 3-SSIM procedures used to yield a similarity score.  
Figure 5: MS-GSSIM procedures to quantify the image quality. \(L\) is low-pass filtering and \(2\downarrow\) denotes downsampling by 2.  
Procedures for 3-SSIM also exist for a multiscale method, called 3-MS-SSIM. In general, the 3-MS-SSIM approach is the same as the procedure depicted in Figure 2 but follows a quantifying procedure, as shown in Figure 6. The steps in Figure 6 can be described as follows:  
* Step 1: Compute the SSIM map using Equation (14). Using this SSIM map, we can call up the structure information.
* Step 2: Calculate the gradient magnitude utilizing a Sobel operator over the reference and noised images.
* Step 3: Define the threshold value \(TH_{1}=0.12\times g_{max}\) and \(TH_{2}=0.06\times g_{max}\), where \(g_{max}\) denotes a maximum grayscale level of gradient magnitude when computed over the original image.
* Step 4: Based on step 3, partition the images into edge, smooth, and texture regions using the following rules: If \(p_{o}(i,j)>TH_{1}\) or \(p_{d}(i,j)>TH_{1}\), it is an edge region; if \(p_{o}(i,j)<TH_{2}\) and \(p_{d}(i,j)\leq TH_{1}\), it is a smooth region; and otherwise, if the pixels belong to a texture region but are not edge pixels, it is a texture region. Here, \((i,j)\) denotes the gradient coordinate, \(p_{o}\) is the original image pixel, and \(p_{d}\) denotes a degraded image pixel.  
Figure 7 shows the images for every process in a specific region. Figure 7a depicts the original \"Lena\" image, whereas Figure 7b shows its blurred image, Figure 7c depicts the edge region image, Figure 7d shows the smooth region image, and Figure 7e depicts the texture region image.
# Piecewise Function Hysteretic Model for Cold-Formed Steel Shear Walls with Reinforced End Studs
###### Abstract  
Cold-formed steel (CFS) shear walls with concrete-filled rectangular steel tube (CFRST) columns as end studs can upgrade the performance of mid-rise CFS structures, such as the vertical bearing capacity, anti-overturning ability, shear strength, and fire resistance properties, thereby enhancing the safety of structures. A theoretical hysteretic model is established according to a previous experimental study. This model is described in a simple mathematical form and takes nonlinearity, pinching, strength, and stiffness deterioration into consideration. It was established in two steps: (1) a discrete coordinate method was proposed to determine the load-displacement skeleton curve of the wall, by which governing deformations and their corresponding loads of the hysteretic loops under different loading cases can be obtained; afterwards; (2) a piecewise function was adopted to capture the hysteretic loop relative to each governing deformation, the hysteretic model of the wall was further established, and additional criteria for the dominant parameters of the model were stated. Finally, the hysteretic model was validated by experimental results from other studies. The results show that elastic lateral stiffness \(K_{\mathrm{e}}\) and shear capacity \(F_{\mathrm{P}}\) are key factors determining the load-displacement skeleton curve of the wall; hysteretic characteristics of the wall with reinforced end studs can be fully reflected by piecewise function hysteretic model, moreover, the model has intuitional expressions with clear physical interpretations for each parameter, paving the way for predicting the nonlinear dynamic responses of mid-rise CFS structures.  
CFS structures; CFS shear wall with reinforced end studs; concrete-filled rectangular steel tube columns; hysteretic behavior; hysteretic model +
Footnote †: journal: Applied Sciences
## 3 Results
### Correlation Tests  
We tested our results for statistical correlation to lithology, historical evolution extent of the city, season, and month of incidents by the chi-square method. None of all tested correlations were  
Figure 6: Samples of collapsed buildings that have an NHS spatiotemporal pattern before their collapse. The main map shows the location of the four collapsed buildings on a true color Sentinel-2 image acquired on 24 October 2018: **(a)** shows the spatiotemporal analysis for a 200 m buffer of a whole building collapse on 15 August 2016, **(b)** is the result of a whole collapsed building on 1 May 2016, **(c)** illustrates the analysis result of a partial building collapse on 14 July 2016, and **(d)** shows the result of a whole building collapse on 22 January 2017.  
statistically significant with the two spatiotemporal patterns of our results, as shown in Table 4. However, the only potential correlation was obtained in the lithology test.  
All incidents that occurred within the stabilized sand dune soil had new hot spots in the vertical motion before the building collapse, while five of six collapsed buildings in the reclaimed sand area in the old city also had new hot spots of vertical motion before the incident. The sandy nature of those two soil types may illustrate a correlation that could be helpful in predicting building collapse. Nevertheless, this potential correlation needs further investigation for better validation.
## 2 Materials and Methods
### Methods
#### 2.3.3 Standardized Multivariate Linear Regression (SMLR) Model  
The SMLR model combined several driving factors to explain the dynamics of GPP. To separate the relative contribution of each driving factor, the fraction in variation partitioning of the multiple standardized regression was applied. The SMLR model is expressed as follows:  
\[\frac{GPP-\overline{GPP}}{\sigma_{GPP}}=\beta_{0}+\beta_{1}\frac{ED_{1}- \overline{ED_{1}}}{\sigma_{1}}+\ldots+\beta_{n}\frac{ED_{n}-\overline{ED_{n}} }{\sigma_{n}}, \tag{3}\]  
where GPP represents a time series of accumulated GPP in the growing season (AGG); \(ED_{i}\) (\(i=1\), \(2\),......, \(n\); \(n\) is the total of driving factors) is a time series of a certain influencing factor covering the whole study period at the pixel scale, including daily sunshine duration in summer, daily average temperature in summer, daily precipitation in summer, growing season length (GSL), and FPAR; \(\overline{GPP}\) and \(\overline{ED_{1}}\) are the multi-year mean values of AGG and influencing factors, respectively. \(\delta_{GPP}\) and \(\delta_{ED}\) are standard deviations of AGG and influencing factors. Each time series was normalized by subtracting its mean value and then dividing it by its standard deviation. Thus, any unit change in each variable has the same statistical meaning and the effects of changes in different factors can becompared. \(\beta_{1}\), \(\beta_{2}\)\(\dots\)\(\beta_{n}\) are regression coefficients that represent the importance of each driving factors to the GPP. Factors with a large coefficient were assumed to contribute more to the variation in GPP, and the largest coefficient was identified as the dominant factor to GPP in our study area during the period of 1982-2015. Also, we used the Variance Inflation Factor (VIF) to examine the multicollinearity of our driving factors before conducting the SMLR model. Significant multicollinearity exists when the VIF \(\geq\) 10. Each SMLR model and their coefficients were verified by the _F_-test and _t_-test, respectively.
## 3 Results
### Cycle Modelling
#### 3.1.1 Cooled Cycle Comparison  
In an attempt to compare the performance of an uncooled cycle (the target cycle of this work) to a cooled one, like-for-like working conditions are applied. Conditions of the optimised cycle model from the work of [PERSON] et al. are adopted [10] which are compared against the data from IEA GHG report [9]. Optimisation work, to achieve maximum cycle efficiency target, was carried out by the authors of [10] after performing a sensitivity analysis on the most relevant cycle-affecting parameters. The thermal energy of the feedstock is kept the same as in the cited works by setting the natural gas inlet flow rate to 16.52 kg/s. For the sake of comparison, the uncooled turbine is assumed to be capable of operating at a full-size (\(\approx\)700-800 MW) power scale.  
The comparison between a number of cycle performance results is viewed in Table 2. Generally there is good agreement in the results with some discrepancies that can be attributed to the differences in minor quantitative assumptions, and because of using two compression stages instead of four [10] which lowers the power required to drive the compressors. The absence of turbine cooling gives a higher turbine outlet temperature which implies a lower temperature drop across the turbine contributing to a lower turbine specific work. However, the higher combustion flow rate, required to reach the desired temperature of the cycle, balances the drop in turbine specific work. Overall, the cycle with the uncooled turbine gives a superior cycle efficiency compared to the cooled case, a 1.54% efficiency point gain. The heat transfer associated to preheating the turbine cooling flow within the regenerator (which also contributes to expansion losses) is removed and thus the detrimental efficiency penalty affiliated with this process is eliminated causing a beneficial impact on the cycle efficiency.  
In fact it is suggested that while turbine cooling allows an increase in turbine inlet temperature (TIT) which enhances cycle performance, it also increases energy losses in turbines and thus leads to a lower aerodynamic efficiency [28] that in turn would have a great negative impact on the net cycle efficiency [29]. [PERSON] et al. [10] noted that the maximum net cycle efficiency is achieved at a relatively low turbine inlet temperature (lower than the maximum simulated TIT value) because of the substantial cooling mass flows required if a high combustor outlet temperature is implemented.
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 5 Experiment and Evaluation  
### Dataset Description  
We use data from a well-known telecommunications operator in China to evaluate the performance of our model. Specifically, the dataset covers monitoring data from March 2021 to December 2023, spanning 138 resource pools, 31,924 servers, and 392 PB of storage. The data have been aggregated by resource pools and include daily records of storage resource usage. Due to variations in business operations and geographical factors, the demand patterns for cloud resources exhibit significant differences across different resource pools.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 4 Robotic Maintenance Technology in Other Industries
### Mobile Manipulators for Inspection and Repair Tasks
#### 4.2.3 Maintenance in Aerospace  
The aerospace industry deals with aircraft and spacecraft for space. Workspaces and environments are different in the aerospace industry, which requires motion and navigation capability in the air with or without gravitational force. For example, ground, aerial, or underwater navigation systems are not usable in space because of the lack of gravitational force. Hence, researchers developed free-flying or free-floating mobility features for mobile space manipulators [101; 102]. The dual-arm free-flying space robotic system (DAFSAR), shown in Figure 14a, can estimate the target object's pose, self-dock onto the host, and perform robotic maintenance.  
To increase the repair capability and real-time on-orbit assessment, researchers from the United States Naval Academy designed a mobile manipulator called Autonomous Mobile On-orbit Diagnostic System (AMODS), which was a combination of multiple CubeSat (Cube shaped Satellite) with 7 DoFs manipulator [103]. This robotic system consisted of two satellite systems, one for repair (RSat) and one for navigation (BRICSat), as shown in Figure 14b.  
The control system of a space mobile manipulator requires special attention as a free-floating mobile base, disturbance from the host spacecraft, and end-effector motion can affect the pose of the whole robot. A coordinated control system considering the attitude of the base robot and the motion of the dual arm manipulator system was investigated [104]. Alternatively, [PERSON] et al. discussed an impedance-based control system by designing the end-effector motion as a mass damper-spring system for target grabbing [105]. Another impedance-based control system was investigated for the docking of the robot onto the spacecraft [106]. Teleoperation or virtual reality (VR) controllers were used for control system for space mobile manipulators [107; 108].
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Symbolic Representation of Transition Systems  
The model checking formulation provided in the previous subsection relies on an explicit representation of states and transitions. Such an explicit representation is not adequate to handle large state spaces. In _symbolic model checking_, the (explicit) transition system is converted into a more concise, implicit representation based on propositional formulas, called _symbolic representation_. Using a symbolic representation, model checking algorithms can traverse the state space of the system more efficiently, by manipulating sets of states and transitions directly, instead of being forced to operate on one state or transition at a time. In addition, since the symbolic representation relies on propositional formulas, symbolic model checking algorithms can leverage well-developed automatic techniques for manipulating such formulas such as BDDs or SAT solvers.  
In this Subsection we show how to _encode_ a transition system into its symbolic representation and how to formulate the invariant verification problem on such a representation. Given a transition system \(\mathcal{TS}=\langle S,S_{0},R,AP,L\rangle\), to obtain a symbolic representation of \(\mathcal{TS}\), we must define how to encode each of its constituents into propositional formulas.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 3 Related Work
### IoT Applications Based on Edge-Cloud Framework
The result of this work shows that systems with less computing capacity, consumption and hardware expenses may be deployed in a much more cost-effective manner by dividing computation loads from the cloud into the edge. [PERSON] et al. (2018) presented a water conservation edge computing framework by employing easy computing methods. The proposed work helps decision-makers raise people's awareness of water efficiency in a scientifically easy method. Based on numerous parameters such as the number of people, average family income, the profession of the members, and previous water needs, the proposed model leverages edge computing at the home nodes of the network to estimate the actual consumption of a specific home in real-time. Results reveal that the proposed approach operates better in a self-synthesised dataset than in other similar networks. The approach framework converges effectively and provides greater precision in spatial and temporal terms. There are also other applications on health care and video streaming. [PERSON] et al. (2018) provided a fog-based computing-based framework that presents an implementation of an Internet of Things health monitoring system as a proof of concept. The proposed framework improves, for example, energy economy, performance, reliability, interoperability, and other diverse properties of IoT architecture utilised in health applications. A fog computing-based health IoT system and its services are described in more detail in the proposed framework, which is conducted from a variety of viewpoints. A medical case study named early alert cases to monitor patients suffering from acute diseases was applied to the fog-assisted system. All data flow operations from the data collection at sensor nodes to the cloud and end-users are part of our whole system demonstration. [PERSON] et al. (2018) proposed a framework for mobile users to employ fog computing as a content delivery system. The fog nodes are distributed close to mobile users. The users request the fog servers and answer each request, whether they are downloaded from the fog server or from the cloud if the request is just initialised. The findings show that fog computing gives minimal response time compared to the cloud. The studies also examine mobile users' power consumption in different modes.
## 2 System Overview
### Flywheel Energy Storage System (FESS)  
The proposed FESS consists of three main components, namely the motor/generator (M/G) integrated with the flywheel, and two two-level three-phase power converters, devoted to M/G-side and grid-side power control purposes, respectively. The two converters are coupled through a DC-link, which decouples M/G and grid powers to each other through a capacitor module.  
Regarding the M/G, the IRLA-SPM shown in Figure 2 has been designed ad hoc for this application [32; 33]. An inner-rotor configuration has been selected to achieve high rotating speed by limiting peripheral speed appropriately. However, the surface-mounted configuration still requires the use of a carbon-fiber sleeve to restrain the PMs on the rotor surface. In this regard, the most distinctive feature of the proposed configuration consists of integrating the flywheel within the sleeve, by increasing its thickness in accordance with both PM containment and energy requirement. This leads to a very thick sleeve (approximately 36 cm) due to the relatively high amount of energy to store (approximately 8 kWh), and, thus, to a large-airgap machine. However, a very large airgap is not a critical issue as magnetic flux density should be reasonably low due to the high-speed range (up to 18 krpm), also leading to almost sinusoidal magnetic flux density distribution. The latter and the use of ferrite PMs enables core losses reduction, which is fundamental at high speed operation. The use of ferrite PMs also limits costs and availability issues of high-energy density PMs, enabling higher operating temperatures as well.  
Figure 1: Overview of the FESS integrated within a household electric system.  
The proposed FESS is equipped with an integrated energy management and control system. In particular, the EMS defines the most suitable FESS reference power in accordance with PV production and load demand. However, user's preferences are considered as well, for example setting the desired turn on and off times during the day, which are represented by the on/off signal depicted in Figure 1. Consequently, different operating modes are foreseen, as detailed in the following.
## 2 Materials and Methods
### Measurements
#### 2.4.4 PAPE Protocol  
No preloads were applied during the baseline protocol. On the third day, the participants performed three repetitions, with 30 s rest intervals, at 85% of their one-repetition maximum (1 RM) on the Smith machine back squat after a proper warm-up [21]. On the fourth day, they executed three repetitions, again with 30 s rest intervals, at 90% of their 1 RM on the Smith machine back squat following a warm-up. The movements were performed with the feet positioned shoulder-width apart and were controlled. The participants were instructed to keep their feet on the ground and squat until their knees formed a 90\({}^{\circ}\) angle. The location where the athletes reached the predetermined angle was marked with a resistance band and the movement was completed by engaging the hamstring muscle group [28]. The selection of these loads was based on previous studies [19; 29], which demonstrate that contractions of maximal or near-maximal intensity (>80% of dynamic or isometric MVC) are widely considered optimal for eliciting PAPE. Furthermore, the use of similar percentages of 1 RM was intended to ensure consistent movement velocities across all participants. This approach reflects the understanding that the effect of the preloading protocol depends not only on the load magnitude but also on the velocity of the movement [30].  
Figure 1: The experimental design of this study. _Legend._ BS85%: Smith machine back squat 85% of 1 RM; BS90%: Smith machine back squat 90% of 1 RM; PAPE: post-activation performance enhancement; min: minute; RAST: running anaerobic sprint test.