esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The AAL Prototype CARIMO for Home Care Service Users
### Fitness Program: Body-Related Features of CARIMO  
The \"body-related features\" of CARIMO included four functions to improve or at least maintain physical abilities: (i) fitness exercises, (ii) activity overview, (iii) tip of the week, and (iv) recording of activities [26].  
\"Fitness exercises\" provided health-enhancing physical exercises focusing on mobilization, stabilization, muscle strengthening, and endurance [27]. The 10-min training sessions changed daily, were based on the fitness level of the home care service users, and consisted of twelve exercises each. Each session was intended to be completed by the users on their own or together with a caregiver (care worker, family carer, or volunteer). For enhanced training planning functionalities (e.g., management of training levels and training plans), the SimpliFlow training management platform was integrated. More than 250 exercise videos were created by a sports scientist, with people aged 50-60 demonstrating how to perform the exercises. All exercise instructions were available as both text and audio output.  
The \"activity overview\" enabled home care service users to keep track of their activities on a daily, weekly, and monthly basis. According to their physical achievements, users were awarded a bronze, silver, or golden trophy cup on a daily or weekly basis. Data from the fitness bracelet were automatically recorded and included in this section.  
A \"tip of the week\" was meant to promote the use of the CARIMO tablet and to stimulate the users to keep physically active, either by suggesting simple changes for boosting activities outdoors (e.g., walking around a bench twice before sitting down) or by exercising at home.  
The activities part of the \"activities and notes\" features enabled CARIMO users to manually enter activities into their \"list of activities\" if not already recorded by the activity tracker.
## 3 Review of Friction Related Accidents on a Runway
### Summary  
In this section, an analysis of runway excursion incidents in Australia and the USA was performed and was compared to a similar analysis from China and to other studies based on investigations from other countries. It was concluded that poor friction significantly affects the risk of runway excursions. This conclusion, however, needs further consideration due to the complexity of the analyzed data. It was found that most of the runway excursion events, which happened due to poor braking, occurred during aircraft landing rather than take-off.  
In all of the analyzed incidents, poor braking was the main reason for the runway excursion. In most cases, however, there were other contributing factors, which included:  
* Approach and landing mistakes;
* Unawareness of runway conditions;
* Poor condition of the runway surface, which includes wear, rubber build-up etc.;
* Contamination of a runway with water, snow, ice, dew, etc.;
* Wind gusts, crosswind, and tailwind;
* Incorrect brake application and/or brake or anti-skid system malfunction.  
It was concluded that it is possible to significantly reduce the risk of such accidents using the following recommendations:  
* Regular monitoring of the friction coefficient of the pavement;
* Regular runway maintenance, including rubber removal, regrooving, and resurfacing;
* Constant monitoring of surface and weather conditions to increase the awareness of pilots.  
Findings of other researchers in other countries are similar. Hence, it is important to analyze and improve international standards and regulations that will significantly reduce the risk of runway excursion due to poor braking.  
Figure 12: Total taxiing and take-off time of an aircraft and surface friction [63].
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 4 Experiments and Discussion
### Comparative Experimental Results  
Given the limited availability of open-source projects dedicated to panoramic image odometry, we selected two representative methods as benchmarks against the proposed method. The first is LF-VIO [30], the latest state-of-the-art panoramic odometry method, and the second is VINS-MONO [34], a classic visual odometry algorithm that supports panoramic images. All experiments were conducted on a laptop with an Intel Core I7-8750H processor. Three commonly used performance metrics, the relative pose error in translation (RPEt), the relative pose error in rotation (RPEr), and the absolute trajectory error (ATE), were used to evaluate the VIO's localization accuracy. Table 1 summarizes the experimental results of the three algorithms in the PAL-VIO dataset, and the associated confidence intervals (CIs) at 95% confidence level are given in Table 2. These results indicate that the proposed SCP-VIO demonstrates superior performance in both RPEt and ATM across most sequences. Scenario ID01 and ID06 were analyzed as representative cases and characterized by higher and lower angular velocities, respectively. In the ID01 scenario, SCP-VIO achieved an RMSE of RPEt of 1.70%, representing improvements of 36.8% and 54.3% over LF-VIO and VINS-MONO, which exhibited RPEt RMSE values of 2.69% and 3.72%, respectively. Under lower angular velocity conditions, performance increased across all algorithms, with the RMSE of the RPEt of SCP-VIO decreasing to 1.11% compared to 1.12% and 3.54% for LF-VIO and VINS-MONO. Under both high and low angular velocities of the camera, SCP-VIO maintained the highest accuracy among the three methods. However, its improvement in trajectory estimation accuracy was more pronounced under high-speed conditions. Figure 10 illustrates the comparison of trajectories for scenarios of IDs 01, 04, 05, and 06.
# Patient Safety Culture in Hospital Settings Across Continents: A Systematic Review
## 3 Result
### General Characteristics of the Reviewed Studies
#### 3.3.5 Exploring Perceptions of Patient Safety Culture across Continents  
The studies compiled in Supplementary Materials reveal the widespread adoption of the HSOPSC 1.0 globally. Few studies have implemented the revised version, HSOPSC 2.0, which is currently being evaluated for its validity and adaptation in various hospital settings across countries. Some survey results were discarded, and data conforming to a uniform standard were collected for descriptive exploration, comparing and analyzing variability across different dimensions in study surveys. Therefore, only studies that used HSOPSC 1.0 were included in this section, as well as studies that used the average positive response rate percentage to measure PSC dimensions. On the other hand, some surveys were excluded because they did not cover all dimensions [39; 59; 61; 63; 69; 89], and some utilized a customized version of the HSOPSC instrument, which assessed only some dimensions [60; 89]. Furthermore, some survey results were not included because they did not directly report the number of participants [40; 56; 66], and others that only used the mean and standard deviation (SD) as measurement values were also excluded [36; 52; 71; 74; 81; 86; 92]. Therefore, the remaining survey results were systematically organized by continents, countries, professionals, and the 12 dimensions response results, as shown in Table 4.  
One of the main objectives of this study is to assess the strengths and weaknesses within various dimensions of PSC across the multiple studies included in this study. Regarding the evaluation of the survey results, a dimension was considered strong if 75% or more of the responses were positive, while dimensions showing more weakness had fewer than 50%. The range between these thresholds is considered acceptable, yet it necessitates further enhancement [98]. Standard deviation is calculated as a measure of dispersion around the average score of all dimensions that contribute to the overall PSC score. All 12 dimensions of PSC measures included in the survey are averaged up to create the overall perception of PSC, represented in the right column under the mean section in Table 4. Lower scores refer to areas for improvement, while higher scores indicate a more positive perception [13; 14].
# Detection of Malicious Websites Using Symbolic Classifier
## 2 Materials and Methods
### Over-Sampling Methods
#### 2.4.3 Borderline SMOTE  
According to [32] the general idea of the BorderlineSMOTE algorithm is to detect samples of the minority class on the borderline (between two classes) and use them to generate new synthetic samples which are added to the original dataset. The algorithm is a type of oversampling method and can be described as the extension of the original SMOTE algorithm. To describe the process of synthetic sample generation using the BorderlineSMOTE algorithm the entire dataset will be denoted as \(T\), the minority class samples as \(S\), and the majority class samples as \(M\).  
\[S = \{s_{1},s_{2},\ldots,s_{min}\}, \tag{7}\] \[M = \{m_{1},m_{2},\ldots,m_{maj}\}, \tag{8}\]  
where \(min\) and \(maj\) represent the total number of minority and majority class samples, respectively. The Borderline SMOTE algorithm starts by determining \(m\) nearest neighbors from the entire dataset \(T\) for every sample in the minority class \(N\). With \(m^{\prime}\) the number of majority samples among the \(m\) nearest neighbors will be denoted (\(0\leq m^{\prime}\leq m\)). The next step is to determine how many samples from \(m^{\prime}\) are among \(m\). If all \(m\) nearest neighbors \(s_{i}\) are from the majority class then the \(s_{i}\) is considered as noise and the sample is ignored from further steps. If the \(s_{i}\) sample is surrounded by a large number of majority class samples then the \(s_{i}\) sample is incorrectly classified and is placed into the DANGER set. If the number of majority class samples \(m^{\prime}\) is low then the sample \(s_{i}\) will not participate in further steps. The DANGER set is a subset of minority class S since it consists of samples from borderline data of the minority class S.  
\[DANGER=\{s^{\prime}_{1},s^{\prime}_{2},\ldots,s^{\prime}_{mun}\}, \tag{9}\]  
where \(mun\) it represents a number of borderline minority class samples and \(0\leq s^{\prime}_{mun}\leq s_{min}\). The next step is to calculate for each DANGER set sample k nearest neighbors from S. The final step is to generate \(l\times s^{\prime}_{mun}\) number of synthetic positive examples from data in the DANGER set. The \(l\) is a number between 1 and k. For each \(s^{\prime}\) sample a random number of k nearest neighbors in S is selected. Then the difference \(dif_{j}\) (\(j=1,2,\ldots,l\)) between \(s^{\prime}_{i}\) and its 1 nearest neighbors for S is calculated and multiplied by a random number in the 0 to 1 range. The new synthetic minority sample between \(s^{\prime}_{i}\) and its nearest neighbor is generated using the expression  
\[synth_{j}=p^{\prime}_{i}+r_{j}\cdot dif_{j}\quad j=1,2,\ldots,l. \tag{10}\]  
The process is repeated for every \(s^{\prime}_{i}\) in the DANGER set.
# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 2 Method  
Control-network-aided SLAM is the foundation of the proposed distributed indoor mapping scheme. In the SLAM problem, there exist two different strategies to obtain the position of the robot: feature matching and scan matching. Feature matching refers to positioning by matching significant feature points (such as feature lines or corner points) extracted from LiDAR scan data with feature maps. However, it is relatively difficult for 2D LiDAR scans to extract distinctive features because LiDAR scan data do not contain rich feature information like visual images do [19]. Scan matching refers to positioning by matching two or more consecutive LiDAR scan data with a grid map or point cloud. Scan matching is a widely used method for calculating relative poses in LiDAR-based SLAM, which can be divided into scan-to-scan matching and scan-to-map matching methods. Scan-to-scan matching accumulates error quickly, as it only uses the observation information of two consecutive LiDAR scans. Scan-to-map matching can obtain a more accurate relative pose because it can limit the accumulation of errors. In this paper, we use an optimization-based method to get more accurate mapping results. Firstly, the map generation and update methods utilized in the SLAM process are introduced. Secondly, the IMU-aided scan-to-map matching is described in detail. Thirdly, the control-network-aided SLAM back-end optimization is explained. Finally, the distributed mapping scheme is designed based on control-network-aided SLAM.
## 1 Introduction
Their study showed that the pattern of flexural-shear arrangement is more efficacious than the arrangement of flexural reinforcement in enhancing the rigidity, ultimate flexural bearing capacity, and post-cracking performance of reinforced concrete beams. [PERSON] et al. [24] investigated the reinforcement and damage properties of cracked CFRP-concrete beams using the digital image correlation method and proposed a simple method for evaluating the reinforcement effect of cracked CFRP-concrete beams, which was verified by experimental results. The authors suggested that the reinforcement effect of cracked CFRP-concrete beams should be evaluated during service. [PERSON] et al. [25] investigated the bending behavior of reinforced concrete beams reinforced with CFRP tendons by creating a finite element model that elucidated the damage mode of the beams in terms of displacement evolution and strain development. In addition, the experimental results revealed the influences of the bar connection mode. RC beams strengthened with continuous bars have a greater ultimate load than those strengthened with lapped bars. [PERSON] et al. [26], [PERSON] et al. [27], and [PERSON] et al. [28; 29] derived the calculation equation of the ultimate stress and flexural bearing capacity of externally prestressed CFRP concrete beams under static loading by analyzing the short-term performance of externally prestressed CFRP concrete beams. Additionally, the predicted results exhibited a high level of agreement with the test results. In a study conducted by [PERSON] et al. [30], it was demonstrated that the arrangement of flexural reinforcement resulted in a notable reduction in mid-span deflection and enhanced the overall performance of the beams subjected to uniform loads. The study of [PERSON] et al. [31] demonstrated that when damage occurs at the mid-span, there is a linear drop in the ultimate load as the distance between deviators increases. The majority of previous research has mostly focused on utilizing CFRP reinforcement to enhance flexural or shear resistance individually. There are limited studies on the use of CFRP reinforcement to enhance the flexural and shear capacities of both simply supported and continuous beams. In this paper, a series of tests were designed to verify the effectiveness of external prestressed CFRP tendons to enhance the flexural and shear load-carrying capacity of concrete beams strengthened simultaneously. In this study, a total of eight reinforced concrete beams were fabricated and enhanced with externally prestressed CFRP tendons. The beams consisted of both simply supported and continuous beams. This research employs experiments to comprehensively examine the influence of the reinforcement arrangement of CFRP concrete beams, the prestressing level, and the presence of initial cracks on the flexural and shear performance of the beams. Furthermore, this paper examines the equations used to anticipate the ultimate flexural bearing capacity of concrete beams that are reinforced with externally prestressed CFRP tendons.
## 3 DLSTM Based Indirect RUL Prognosis  
### Polynomial Fitting Based HI  
For Li-ion battery RUL prognosis, it is important to construct an indirect HI which shows the degradation phenomenon, because the battery capacity cannot be directly acquired on-line. In most real-life applications, the battery capacity can be estimated by one of a number of complex methods (electrochemical analysis, Ampere-hours, impedance, and other methods). These methods are time-consuming, and their accuracy is not satisfying. Therefore, using the charge current or discharge voltage to construct an indirect HI is more meaningful for online RUL prognosis.  
It is widely known that a battery's life is closely related to its voltage. Similar to [30], we also adopt the time interval of equal discharge voltage difference (TIEDVD) as an indirect HI in each cycle. As shown in Figure 3, the Li-ion battery discharge time becomes shorter with each cycle. Due to the discrete monitoring time, the TIEDVD HI in the \(\tau th\) cycle can be defined as:  
\[HI(\mathbf{\tau})=\text{max}(T(\mathbf{\tau}))-\text{min}(T(\mathbf{\tau})),\mathbf{\tau}=1,2,...,k \tag{2}\]  
where \(T(\mathbf{\tau})\) is the voltage measurement time aggregation of the \(\tau th\) cycle in the predefined discharge voltage range. The voltage range (Figure 3) contains Vmax (upper limit) and Vmin (lower limit). So, the HI series can be described as:  
\[HI=\{HI(1),HI(2),...,HI(k)\} \tag{3}\]  
However, the literature [30] did not give the specific principle of discharge voltage range selection and the mapping relationship between the constructed HI and Li-ion battery capacity. Hence, in order to solve these problems, this paper utilizes the Spearman correlation coefficient method [31] to analyze and predefine the discharge voltage range. Spearman correlation analysis does not have specific data conditions, in contrast to two other correlation analysis methods (Person and Kendall). Then, the polynomial fitting method is used to analyze the correlation character [32] and establish the mapping relationship. Finally, the indirect HI construction framework can be summarized as follows:  
Step 1: Acquire and preprocess the voltage measurements in each discharge cycle;  
Step 2: Predefine the Vmax and Vmin, then use Equation (2) to calculate the HI of each cycle, and gain the HI series, as shown in Equation (3);  
Step 3: Utilize Spearman method to calculate and analyze the correlation coefficient between the HI series and the Li-ion battery capacity series. If the correlation coefficient meets the demand, perform the next step. Otherwise, repeat Steps 2 and 3;  
Figure 3: Discharge voltages at different cycle numbers and indirect HI extraction.  
Step 4: Use the polynomial fitting method to build the mapping relationship between the HI and the Li-ion battery capacity.
# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
## 2 Fundamentals of Chiral Metasurfaces
### The Optical Response of Chiral Media  
The constitutive relationships that typically illustrate the optical response of a broadly chiral material are as follows:  
\[\overline{\mathrm{D}}=\epsilon_{0}\overline{\mathrm{E}}+\frac{\mathrm{i}}{ \epsilon_{0}}\overline{\mathrm{X}}\mathrm{H}, \tag{1}\]  
\[\overline{\mathrm{B}}=-\frac{\mathrm{i}}{\epsilon_{0}}\overline{\mathrm{X}} \overline{\mathrm{E}}+\mu_{0}\overline{\mathrm{H}}\mathrm{H}, \tag{2}\]  
where \(\epsilon_{0}\) and \(\mu_{0}\) are the permeability and permittivity of the vacuum, respectively. The magnetic field is \(\overline{\mathrm{H}}\), the electric displacement is \(\overline{\mathrm{D}}\), the electric field is \(\overline{\mathrm{E}}\), and the magnetic induction is \(\overline{\mathrm{B}}\). Additionally, \(\overline{\mathrm{H}},\overline{\mathrm{X}}\), and \(\overline{\mathrm{F}}\) are the permeability, chirality tensor, and permittivity, respectively. The chirality tensor quantifies the asymmetrical response to EM fields, elucidating how they generate or interact with OR and CD. This tensor is vital for understanding and predicting chiroptical properties, accelerating the design of objects for applications like optical signal control and chiral sensing. For an isotropic material \(\overline{\mathrm{H}},\overline{\mathrm{X}}\), and \(\overline{\mathrm{F}}\) could be simplified to scalars \(\mu,\chi\), and \(\varepsilon\), which supports different responses to magnetic and electric fields for the RCP and LCP incidents, the RCP and LCP light have different refractive indices(n), which are given by  
\[n_{\pm}=\sqrt{\epsilon\mu}\pm\chi. \tag{3}\]  
According to Equation (3), waves with opposing handedness would accumulate phases differently as they pass through chiral materials. However, the impedances of both circular polarizations are similar:  
\[Z=Z_{0}\sqrt{\frac{\mu}{\epsilon}}, \tag{4}\]  
where \(Z_{0}\) represents impedance in vacuum.  
Impedance represents the resistance an EM wave encounters as it travels through a medium. RCP and LCP waves with the same amplitude were superimposed to form a linearly polarized wave. Equation (3) makes it simple to demonstrate that the different refractive indices (n\({}_{+}\), n\({}_{-}\)) of the LCP and RCP waves cause the linear polarization to rotate by angle \(\varphi\).  
\[\varphi=\frac{(n_{+}-n_{-})\pi d}{\lambda_{0}}\text{ or can also be }\varphi=\frac{[\arg(T_{+})-\arg(T_{-})]}{2}, \tag{5}\]  
where \(\lambda_{0}\) represents the wavelength of the light in vacuum, and d represents the chiral medium's thickness. T\({}_{+}\) and T\({}_{-}\) are the coefficient of transmission for the right and left spin states, respectively. OR involves both physical effects and processes.