esa-sceva/satcom-chunk-collection huggingface dataset:
# A Literature Review of Parameter-Based Models for Walkability Evaluation
## 3 Results and Discussion  
From the pool of analyzed papers, there are a certain number of articles that use the same evaluation instrument. In that sense, they are considered as one observed instrument. The dimension of acquired instruments can be seen in Figure 5.  
According to the dimension of the observed tools, their domains and aspects, their level of significance, and their level of reliability, there are several tools identified: the Pedestrian Health-Oriented Audit (PHOA) [27]; Study of Environmental and Individual Determinants of Physical Activity (SEID) [28]; Neighborhood Environment Walkability Scale (NEWS) [29; 30; 31; 32; 33; 34; 35]; Microscale Audit of Pedestrian Streetscapes (MAPS) [36; 37; 38]; Neighborhood Brief Observation Tool (NBOT) [39; 40]; Irvine-Minnesota Inventory Form (IMI) [41; 42; 43; 44]; SPOTLIGHT-Virtual Audit Tool (S-VAT) [45]; Collaborative Research of AGEing in EU (COURAGE) [46]; Active Neighborhood Checklist (ANC) [47; 48; 49; 50]; Systematic Pedestrian and Cycling Environmental Scan (SPACES) [51; 52; 53]; Environmental Profile of a Community Health (EPOCH) [54]; Path Walkability Assessment (PWA) [25]; Walkability Assessment Checklist (WAC) [55]; Senior Walking Environment Audit Tool (SWEAT) [56; 57; 58; 59]; International Physical Activity Questionnaire (IPAQ) [60; 61; 62; 63; 64; 65; 66]; Path Walkability Indicators (PWI) [67]; International Physical Activity & Environment Network (IPEN) [68; 69]; Road Safety Audit (RSA) [70]; Pedestrian Level of Service (PLOS) [71; 72]; Gross Sidewalk Walkability Index (GSWI) [73]; Pedestrian Environment Data Scan (PEDS) [74; 75]; Path Environment Audit Tool (PEAT) [76]; Active Accessibility (AA) [77]; Field Survey Instrument (FSI) [78]; Physical Activity Resource Assessment (PARA) [79]; Analytic Hierarchy Process (AHP) [80]; Utah Household Travel Index (UHTS) [81]; Pedestrian Environment Quality Index (PEQI) [82]; Parks, Activity and Recreations among Kids (PARK) [83]; China Urban Built Environment Data Scan Tool (CUBEST) [84]; Pedestrian Environment Review System (PERS) [85]; Virtual--Systematic Tool for Evaluating Pedestrian Streetscapes (V-STEPS) [86]; Audit of Physical Activity Resources for Seniors (APARS) [87]; Physical Activity Neighborhood Environment Scale (PANES) [88; 89; 90; 91; 92; 93]; Graduate Ready for Activity Daily (GRAD) [94; 95; 96]; Environmental Assessment of Public Recreation Spaces (EAPRS) [97; 98]; Walk Score Index (WSI) [99; 100; 101; 102; 103]; School Audit Tool (SAT) [104]; Public Space Quality Index (PSQI) [105; 106; 107]; Walking Suitability Index for Territory (T-WSI) [108; 109; 110].  
Figure 5: The dimension of observed audit tools survey forms, questionnaires or instruments.
# Optimal Design of a Residential Photovoltaic Renewable System in South Korea
## 2 Optimization Formulation  
The objective function to be minimized in this residential PV design optimization is the total cost (\(C_{T}\)), which consists of the electric bill from grid (\(C_{Electric}\)), the PV-related construction cost (\(C_{Cst}\)), and the PV-related maintenance cost (\(C_{Mtn}\)), as shown in Equation (1) [6]:  
\[\text{Minimize}C_{T}=C_{Electric}+C_{Cst}+C_{Mtn} \tag{1}\]  
where the annual electric bill (\(C_{Electric}\)) is the sum of the monthly bills, and each monthly bill (\(C_{Electric}^{m}\)) is calculated based on the monthly grid-supplied amount (\(D_{Electric}^{m}-PV_{Electric}^{m}\)) when monthly residential demand (\(D_{Electric}^{m}\)) is greater than the monthly PV generation amount (\(PV_{Electric}^{m}\)), as in Equation (2):  
\[C_{Electric}=\sum_{m=1}^{12}C_{Electric}^{m}(D_{Electric}^{m}-PV_{Electric}^{m}) \tag{2}\]  
For the monthly bill (\(C_{Electric}^{m}\)), Korea adopts a six-stage progressive electric rate system, which charges a higher rate for higher electricity usage, as shown in Table 1.  
For example, if one household consumes 50 kWh for a certain month, the monthly electric bill will be 3125 KRW (=370 + 50 \(\times\) 55.1); if it consumes 150 kWh, the monthly electric bill will be 12,020 KRW (=820 + 100 \(\times\) 55.1 + 50 \(\times\) 113.8). Thus, if we draw a monthly electric bill from 0 to 600 kWh, we obtain a stepped piecewise linear function, as shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Range** & **Base Rate (KRW)** & **Progressive Rate (KRW/kWh)** \\ \hline Up to 100 kWh & 370 & 55.1 \\
101\(\sim\)200 kWh & 820 & 113.8 \\
201\(\sim\)300 kWh & 1430 & 168.3 \\
301\(\sim\)400 kWh & 3420 & 248.6 \\
401\(\sim\)500 kWh & 6410 & 366.4 \\ More than 500 kWh & 11,750 & 643.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Korean progressive electric rate (US$1 \(\approx\) 1100 KRW).  
For the PV-related construction cost (\(C_{Cst}\)), in order to fairly consider this one-time cost alongside other annual costs (\(C_{Electric}^{m}\) and \(C_{Mtn}\)), a capital recovery factor [8], which is the ratio of a constant annual return amount to the initial construction cost (\(C_{Icc}\)) for a given length of time, is introduced as in Equation (3):  
\[C_{Cst}=\frac{r{\left(1+r\right)}^{n}}{\left(1+r\right)^{n}-1}C_{Icc} \tag{3}\]  
where \(r\) is the interest rate (6.5% in this study) and n is number of system operation years (or the number of annual returns received; 25 years in this study).  
The decision variables in this residential PV design optimization are the size of the PV panel (or module; \(S^{PV}\)) and the tilt angle of the PV panel (\(A^{PV}\); horizontal line is 0\({}^{\circ}\)). These two decision variables have value ranges as constraints:  
\[0\leq S^{PV}\leq 3(\text{kW}) \tag{4}\]  
\[15^{\circ}\leq A^{PV}\leq 60^{\circ} \tag{5}\]
# Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area
## 5 Travel Demand Description
### Travel Demand Parameters
#### 5.3.2 Activity-Gap for Kindergarten  
As cited previously, some activities require a gap-value. In this study, only the _kindergarten_ activity is considered by this value; due the limited information about this activity (in \(ENTD\)), \(Gap_{kindergarten}\) is equal to four hours. To integrate this components into the synthetic generator, a defined module \(gap\) is created, it contains for each entry (activity), its gap-value.  
Figure 11: Agent activity-plans: _agent_1_ has two plans, both of which have the same sequence of activities, the sequence of used transport modes, _home_ and _work_ locations; however, they differ in the location of _leisure_ (_l32_ for the first plan and _LaVallee_14_ for the second plan).  
Figure 12: Activity-plan: the first plan of the _agent_1_ is detailed, the sequence of activities is \(home\to work\to home\to leisure\to home\). The locations of activities are: \(h2549(home)\), \(7 clk(work)\) and \(l23(leisure)\). The transport modes are: _car_ for trips \(home\leftrightarrow work\) and \(walk\) for trips \(home\leftrightarrow leisure\).  
5.3.3. Tolerance on Activity-Duration and Travel Time  
In this section, we focus on the static tolerance coefficients for both duration and travel time. These parameters are defined independently from the survey data, also as the selected duration and travel speed (which is used to calculate the travel time). Tolerance represents the proportion of time (freedom) that is given to a traveler: to spend more or less time in an activity (the duration delay) or to go faster or slower (travel time delay). The initial static coefficients of duration vary from 0 for work to 20 percent for restaurant. In our experiment, coefficient \(TTDCof\) is arbitrarily set to 20% for each mode.  
To explain well the meaning of these coefficients, we consider the case of work-activity; the typical duration is set to (4 h 45 min = 285 min), the tolerance coefficient equals to 10%, which means that the tolerated duration is equal to 28.5 min.  
For both home and kindergarten activities, the duration delay set to 0. For the remaining activities, the tolerance coefficient has a converse relationship with the duration of activity. The travel tolerance coefficient is set to the same value for all transport modes.  
Two tolerance (delay) modules are then defined: activity-delay and mode-delay.
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 3 Methodology
### Detecting Dynamic Objects and Generating Masks with YOLOv11  
To effectively detect dynamic objects and achieve high-precision pose estimation in dynamic environments, this paper employs the advanced YOLOv11 [34] deep learning model for semantic segmentation to identify potential dynamic objects. As a cutting-edge technology in the field of instance segmentation, YOLOv11 provides pixel-level instance segmentation and instance labeling of images. These capabilities are fundamental for enhancing our system's ability to handle dynamic contexts.  
Throughout the processing workflow, the semantic segmentation functionality of YOLOv11 is of primary importance within this system. It achieves pixel-level segmentation of each frame, allowing for accurate identification of different object regions. YOLOv11 interprets the input RGB raw images to recognize possible dynamic entities (e.g., people, bicycles, cars, motorcycles, buses) and items that might be moved (books, chairs, etc.). Dynamic objects such as these encompass the most common elements found in most environments. By combining all objects, a mask reflecting all dynamic regions in a single image is obtained, providing precise spatial localization for the subsequent feature point filtering.  
To further enhance the semantic mask's coverage accuracy over object boundaries, in this paper we apply a range of morphological dilation (e.g., 5 pixels) to the masks  
Figure 1: System Overview. This figure illustrates the main components and workflow of the proposed system. The core modules of the SLAM process are indicated by solid lines, while the dashed lines highlight the key aspects of the proposed research involving dynamic feature point processing and pose optimization. This design ensures that the proposed system can effectively discern and eliminate unstable feature points when facing dynamic environments, enhancing both overall localization and mapping quality.  
generated by YOLOv11. This effectively expands the boundaries of dynamic objects and compensates for local discontinuities and gaps caused by instance segmentation errors. This process strengthens the continuity and integrity of the mask around the target edge areas, addressing the common issue of \"truncation\" or \"under-labeling\" in instance segmentation at object edges. This treatment ensures that dynamic targets are not incorrectly classified as static regions due to incomplete boundaries during the geometric processing stage. The edge regions of dynamic objects often contain many inconsistent motion feature points or feature discrepancies caused by occlusion, especially in monocular SLAM systems. If these points are not fully captured, they may lead to mismatches and greatly impact the accuracy of pose estimation. Therefore, the morphological dilation step aids in improving the reliability of feature point matching as well as the overall robustness of the system in complex dynamic scenes.
# Effective Mechanical Properties of a Composite Material Reinforced by Oil Shale Ash Particles
## 3 Mechanical Properties of the Epoxy-Resin-OSA Composite
### Analytical Calculation for the Epoxy-Resin-OSA Composite
#### 3.2.1 Rule of Mixtures  
The rule of mixtures is a simple linear model for predicting the mechanical properties of composite materials based on the properties and volume fractions of the constituents. The rule of mixtures uses the following equation:  
\[E_{c}=E_{p}\cdot V_{p}+E_{m}\cdot V_{m}, \tag{1}\]  
where  
* \(E_{c}\)--elastic modulus of the composite (epoxy-resin-OSA);
* \(E_{p}\)--elastic modulus of the filler (OSA);
* \(E_{m}\)--elastic modulus of the matrix (epoxy resin);
* \(V_{p}\)--filler volume fraction (OSA) from Table 8;
* \(V_{m}\)--matrix volume fraction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Elastic Modulus \(E_{c}\)** & **Standard** & **Standard** \\  & **(GPa)** & **Deviation** & **Deviation in \%** \\ \hline
**0F 0\%** & 3.24 & \(4.95\times 10^{7}\) & 1.53 \\
**10F 10\%** & 3.28 & \(2.08\times 10^{8}\) & 6.35 \\
**20F 20\%** & 3.62 & \(3.54\times 10^{8}\) & 9.80 \\
**30F 30\%** & 5.07 & \(5.55\times 10^{8}\) & 10.9 \\
**40F 40\%** & 5.74 & \(6.85\times 10^{8}\) & 11.9 \\
**50F 50\%** & 6.25 & \(7.17\times 10^{8}\) & 11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Modulus of elasticity found experimentally.  
Now, to use this equation, we need to use the volume fraction instead of the weight fraction. The volume fractions are given in Table 6. Knowing the weight fraction of OSA, the volume fraction of OSA can be found using Equation (2):  
\[V_{p}=\frac{W_{p}\cdot p_{c}}{\rho_{p}}, \tag{2}\]  
where  
* \(\rho_{c}\)--density of the composite (epoxy-resin-OSA);
* \(\rho_{p}\)--density of the filler (OSA particles);
* \(W_{p}\)--filler (OSA particles) weight fraction.  
The results obtained using the rule of mixtures are shown in Table 9.
Previous studies have utilized MCA to assess gait abnormalities in AIS patients, revealing alterations such as reduced pelvic rotation, asymmetrical step lengths, and compensatory mechanisms to maintain balance [15; 16]. For instance, AIS patients exhibit significant differences in lower limb kinematics compared to healthy controls, highlighting the impact of spinal deformity on gait [17; 18]. Postoperatively, some studies have reported improvements in gait parameters, while others have noted persistent abnormalities despite surgical correction [17; 18]. However, the application of MCA in evaluating postoperative outcomes remains underexplored, and existing studies often have small sample sizes and varying methodologies [18; 19]. For several reasons, a comprehensive systematic review of the current evidence on the use of MCA in surgically treated AIS patients is warranted. First, it can synthesize available data to provide a clearer understanding of postoperative functional outcomes, which is essential for optimizing rehabilitation protocols and enhancing surgical techniques [20]. Second, identifying gaps in the literature can guide future research efforts to address unresolved questions. Finally, given the global prevalence of AIS and the widespread adoption of surgical interventions, this review can offer valuable insights for an international audience. It can facilitate the exchange of knowledge across different healthcare systems and cultural contexts, ultimately contributing to improved patient care worldwide [21; 22]. This systematic review aims to evaluate the current evidence on motion capture analysis in surgically treated adolescent idiopathic scoliosis patients. By synthesizing available data, we seek to determine how MCA has been utilized to assess postoperative functional outcomes, identify patterns and discrepancies in findings, and highlight areas for future research.
## 3 Results and Discussion
### Single-Line Formation Test
In a fixed laser power (\(F\) = 1-4), the width and depth of the melting zone were reduced at faster scan rates. This may have been due to the Marangoni convection in the melted pool [18; 19]. The degree of convection is related to the total amount of energy supplied to the powder layer [20]. The flow of the molten metal is caused by local differences in the surface tension on the liquid surface, which is affected by the differences in temperature between different regions of the melt pool [19]. The final shape and size of the solidified bead is determined by those of the melting pool [19]. Therefore, a small bead is created when the scan rate is fast and the laser power is low. When the laser power was set as 100 and 150 W (\(F\) = 5 and 8), the melting zone was unstable. When the laser power and scan rate were 200 W and 128.6 mm/s, respectively (\(F\) = 4), an optimal bead profile, with optimal height, depth, and width, was formed. According to [PERSON] et al. [2], laser power and laser scan rate also affect the powder adhesion thickness. This was not investigated in this single-line formation test but would be worth further investigation.
# An Analysis of MPTCP Congestion Control
## 5 Multi-Path TCP  
MPTCP can improve throughput by allowing devices to aggregate the bandwidth of their available network interfaces when transferring data. For instance, some large data centres use a dual-homed topology to transfer massive flows between servers and end-points. This helps data centres to increase speed when migrating virtual machines, large images/videos backup and replicating content [20; 21; 22].  
Multiple routes can also provide mobility for users. Each end-host can handover traffic from one network interface to another without disrupting the connection. For instance, Apple users (e.g., iPhone or iPads) are able to shift from local Wi-Fi to 4G or vice versa when downloading an important file without losing connectivity [1; 22].  
MPTCP can also improve network resilience by using available redundant paths in the connection. For instance, when an ongoing session fails, the end-host can use alternative routes while waiting to re-connect on the original path [23]. Similarly, MPTCP path diversity allows users to utilise less congested paths in the case of severe congestion in the primary path [24].  
MPTCP in the Networking Stack  
With regards to the protocol stack, all MPTCP operations occur at the transport layer. MPTCP interaction with the application layer is identical to TCP. The application layer communicates with MPTCP through a TCP stream socket which is connection-oriented, reliable and bi-directional. MPTCP divides the data received from the application layer into multiple subflows. At the network layer, each MPTCP subflow appears as a regular TCP flow bound to a local IP address [3] (see Figure 4).  
Figure 4: MPTCP in the Networking Stack.
## 3 Determination Methods of Shear Modulus
### Autocorrelation Function Method  
At small strains, the stress-strain measurements tend to be distorted by ambient noise because of the low signal-to-noise ratios, as well as sensor accuracy. This leads to the presence of the white noise component in the time histories of dynamic strain and dynamic stress. To eliminate these adverse effects, [PERSON] et al. [30] proposed a new method for the calculation of dynamic shear modulus based on the theory of correlation function. The correlation function quantifies the similarity of two waveforms at different times \(\tau\). For the waveform \(x(t)\) with a time length of \(t_{0}\), the autocorrelation function \(R_{x}(\tau)\) can be written:  
\[R_{x}(\tau)=\frac{1}{t_{0}}\int_{0}^{t_{0}}x(t)x(t+\tau)dt \tag{6}\]  
Substituting Equation (6) into Equation (1), the autocorrelation function of an ideal stress waveform is:  
\[R_{\sigma}(\tau)=\frac{1}{NT}\int_{0}^{NT-\tau}\sigma_{a}^{2}sin(wt)sin(wt+w \tau)dt=\frac{\sigma_{a}^{2}}{2 NT\omega}sin(w\tau)+\frac{(NT-\tau)\sigma_{a}^{ 2}}{2 NT}cos(w\tau) \tag{7}\]  
where \(N\) and \(T\) are the number and period of cyclic loading, respectively. The extreme values of Equation (7) can be determined based on the first-order derivative \(\overset{\bullet}{R_{\sigma}}(\tau)\). Thus:  
\[\left\{\begin{aligned} R_{\sigma,\text{max}}(\tau)=\frac{NT-\tau}{2 NT}\sigma_{a}^{2}\tau=0,T,\dots NT\\ R_{\sigma,min}(\tau)=-\frac{NT-\tau}{2 NT}\sigma_{a}^{2}\tau=0.5T,1.5T,\dots(N-0.5)T\end{aligned}\right. \tag{8}\]  
Figure 4: Symmetrical (**a**) and asymmetrical (**b**) hysteresis loops.  
The amplitude of dynamic stress can then be derived at \(\tau=0\):  
\[\sigma_{a}=\sqrt{R_{\sigma,\max}(0)-R_{\sigma,\min}(0)} \tag{9}\]  
Similarly,  
\[\epsilon_{a}=\sqrt{R_{\epsilon,\max}(0)-R_{\epsilon,\min}(0)} \tag{10}\]  
To provide a better understanding of this method, the autocorrelation functions of stress and of strain time histories, including \(R_{\sigma,\max}(0)\), \(R_{\sigma,\min}(0)\), \(R_{\epsilon,\max}(0)\), and \(R_{\epsilon,\min}(0)\) are illustrated in Figure 5.
## 4 Antibacterial Nanotheranostics
### Photodynamic Therapy (PDT)
[227] have conjugated amine-terminated carbon dots (CDs-NH\({}_{2}\), size 6 nm) with the carboxy group of ampicillin (AMP) via a crosslinker forming amine-functionalized CDs (CDs-AMP, 41 nm) as a carrier for the immobilization and delivery of AMP cargo. CDs-AMP demonstrated better antibacterial activities than free AMP against _E. coli_. This enhanced antibacterial activity was demonstrated by a decrease (by 40%) in the MIC of immobilized AMP due to the greater exposure of _E. coli_ cells to larger molecules of the immobilized AMP that retained its activities even after two weeks of storage (at 4 \({}^{\circ}\)C in aqueous solution). CDs-AMP generated ROS (O\({}_{2}\)\({}^{\bullet-}\)) under visible light illumination. The generated O\({}_{2}\)\({}^{\bullet-}\) increased with greater exposure time and visible light lamp intensity and enhanced the antibacterial activity of the conjugate, disrupting the integrity of _E. coli_ membranes. Inspired by the exciting properties of electrospun nanofibers and the antibacterial efficiencies of PDT (without administering antibiotics), [PERSON] et al. [228] have recently combined both systems. They have encapsulated methylene blue (PS) within a biodegradable electrospun poly(\(\varepsilon\)-caprolactone) (PCL) nanofibrous scaffold for controlling implant infections. The fibrous scaffold was activated (via visible light) on-demand, reducing the viability of _E. coli_ (as a model organism for this combined system) that increased with the time of light exposure (as depicted in Figure 7) due to the production of more ROS. It would be a fruitful area for further work to investigate the combined antibacterial effects of these two systems and different nanothernastic systems, specifically on MRSA. In the same vein, the fascinating theranostic properties of NPs and nanofibers mentioned above could be combined with PDT, creating a novel theranostic system, including PDT. Figure 7: Combined antibacterial electrospun nanofibers and photodynamic therapy (PDT) systems. Scanning electron microscope images of electrospun poly(\(\varepsilon\)-caprolactone) (PCL) scaffolds without a photosensitizer (PS). (ND) (**A**) and electrospun PCL scaffolds loading a PS (methylene blue, MB) (**B**) at the magnifications of 1000\(\times\) and with their specific surface areas (SSAs) determined by Brunauer, Emmett, and Teller analysis. Average log reduction of _Escherichia coli_ on PS-loaded PCL scaffolds after 30 (**C**), 60 (**D**), and 120 min (**E**) of light exposure. Scaffold denoted PCL-MB2 was electrospun from PCL loaded with a doubled concentration of MB (PS). Black bars denote results obtained after incubation in the dark. Striped bars denote light measurements. Results are represented as means \(\pm\) SD. The asterisk (*) denotes a significant difference compared with the control (PCL-ND) (_p_ < 0.05, Student’s _t_-test). Reprinted with permission from [228]. Copyright, 2019, American Chemical Society.