esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment 1: Test of Convergent Validity of the Biofeedback System
### Discussion  
In order to examine the concurrent validity of the presented biofeedback system regarding the two valid tools of anxiety detection ([PERSON]'s measurements and the response scores to the state part of the STAI questionnaire), we applied Spearman's Rho correlation analysis to the data received from each participant individually. More specifically, each student responded to the STAI questionnaire during specific phases of the psychometric procedure. Each student's answers were kept in a database under the participant's code. During the time windows of the students' responses, their biosignals provided by the biofeedback and (\(100-\) (Neurosky anxiety value)) were kept in the database under their relevant codes. The correlation analysis for every participant showed that the majority (12/15, 80%) presented significant positive or negative correlations between the STAI assessment score or the (\(100-\) (Neurosky anxiety level)) provided by the Neurosky eSense algorithm and the biosignal measurements provided by the biofeedback device. Thus, as the anxiety detected by the STAI score or the Neurosky EEG increases,  
* the galvanic skin response correspondingly increases. This is confirmed by [PERSON] [46,47] and [PERSON] and [PERSON] [48],
* the heart rate correspondingly increases. This is in line with [PERSON] et al. [49], and [PERSON] and [PERSON] [50],
* the skin temperature correspondingly decreases. This is confirmed by [PERSON] [51].  
More specifically, the moderate correlations that we found between the biosignal values provided by the biofeedback device, on the one hand, and both the STAI and Neurosky Mindset, on the other hand, were in the predicted direction. The Neurosky Mindset is a well-known commercial tool which is used in several EEG research projects dealing with brain activity in various control tasks [52, 53, 54] or more specifically in learning processes [55, 56, 57]. The findings showed that the biofeedback device's measurements of the emotionality component of anxiety displayed a satisfactory convergent and divergent validity.
## 4 Results
### Required Hardware Resources  
To evaluate the hardware resources required by each MPPT algorithm, a synthesis compilation was performed. The device used was an xc7z045 ffg900-2 FPGA from a ZC706 board, and default synthesis configuration was applied. These results are presented in Table 5 as a percentage of occupation in the ZC706 FPGA.  
The ZC706 FPGA was divided into two major sections: the processing system (PS) and programable logic (PL). The PS section is related to the ARM processor and is not the focus of the present study. The PL section contained all the implemented algorithms. This section contained 54,650 slices, 900 DSP, and some other dedicated hardware. Each slice contained eight registers, four LUTs, two F7 muxes, and one F8 mux. In total this FPGA contained 218,600 LUTs, 437,200 registers, 109,300 F7 muxes, and 54,650 F8 muxes. The registers are not presented in Table 5 because of their irrelevance in relation to the other components.  
Figure 18: HIL results of power produced from panel when slow variations occurred.  
In terms of the hardware resources required for implementation, the cost of the FPGA could be determined for each of the algorithms under consideration. The comparison was conducted using a Xilinx 7 series FPGA, with specific hardware requirements varying between algorithms. Specifically, P&O, INC, and IC-INC require the XC7S6 FPGA from the Spartan family, while FL requires the XC7S25 FPGA. The ANN (G, T) algorithm can be implemented using the XC7S75 FPGA, whereas ANN (IC-INC) requires an Artix XC7A200T FPGA.  
Considering only the hardware costs associated with implementing the algorithms, it can be estimated that P&O, INC, and IC-INC would cost approximately EUR 20 when implemented in a microcontroller. On the other hand, FL is estimated to cost twice as much, and ANN (G, T) would require five times more resources for microcontroller implementation. Finally, ANN (IC-INC) is estimated to require 15 times more resources than P&O, INC, and IC-INC in terms of hardware costs.  
It is important to note that P&O, INC, and IC-INC can also be implemented in a microcontroller that costs only a few euros. In this case, the hardware resources available would be replaced by CPU usage, with the calculations performed sequentially; however, the cost would be substantially reduced. While FPGA implementation may be necessary for certain applications that require higher performance and greater processing power, such as ANNs, microcontroller implementation represents a more cost-effective solution for many other applications.
# Approaches and Challenges in Internet of Robotic Things
## 3 Related Work
### Robot Navigation Techniques
The Manhattan distance between two points {p\({}_{1}\)(x\({}_{1}\), y\({}_{1}\)) and p\({}_{2}\)(x\({}_{2}\), y\({}_{2}\)) is given in Equation (6), while the Euclidean distance between two points and octile distances are represented in Equations (7) and (8), respectively. \[h_{1}(p_{1},\ p_{2})=\bar{\mathbb{C}}(|x_{1}-x_{2}|,\ |y_{1}-y_{2}|),\] (6) \[h_{2}(p_{1},\ p_{2})=\bar{\mathbb{C}}\sqrt{(x1-x2)^{2}+(y1-y2)^{2}},\] (7) \[h_{3}(p_{1},\ p_{2})=\bar{\mathbb{C}}(|x_{1}-x_{2}|+|y_{1}-y_{2}|)+( \mathcal{D}-2\bar{\mathbb{C}})Min(|x_{1}-x_{2}|,|y_{1}-y_{2}|),\] (8) where C and D are constants. * Robot navigation using bumper event The bumper event is used to remove obstacles from the robot's navigation. The bumper event algorithm is applied to the TurtleBot in the gazebo simulator. Bumper and state fields comprise the robot. The bumper sensor coupled with a TurtleBot is used to manage the hurdles. The robot is moved and turned using two different ROS velocities (linear and angular). C++ code is used to implement the algorithm. Because it reduces complexity, this approach is very beneficial in unfamiliar contexts. As a drawback, the algorithm does not give collision-free navigation; hence, the camera gets priority over the bumper sensor for collision-free navigation [23; 60; 72]. * Vision-based navigation The robot's gaze direction can be chosen from a variety of directions according to the inclination of angles. More gaze directions necessitate more computational time. For vision-based navigation, an assessment function M is used to calculate the corresponding connection of feature lines between two images as defined in Equation (9) [70; 72; 73; 74; 75]. \[\text{M}=\alpha\ \sum_{i=1}^{\text{N1}}\text{Di}+\beta\ \sum_{i=1}^{\text{N1}}\text{ Li}+\gamma\ \sum_{j=0}^{\text{N2}}\text{Pj},\] (9) where L\({}_{i}\) is the absolute variance between two location intervals. D\({}_{i}\) is the absolute horizontal variance value of feature lines i in the first image and the parallel candidate image. \(\alpha\), \(\beta\), and \(\gamma\) are the weights for each term; \(\alpha+\beta+\gamma=1\). Pj is the penalty value when a feature line does not have a communicator in the second image. N1 is the number of feature lines, with contenders in the second image. N2 is the number of feature lines that do not have a communicator in the second image.
## 3 Results
### Shading Effects on Crop Yields
The control treatment (0% of shadow) showed a significantly lower production of green fruits (0.36 kg\(\cdot\)m\({}^{-2}\)) in regards to the shadow treatments (15%, 30% and 50%) where green fruits were 0.91, 1.52 and 1.32 kg\(\cdot\)m\({}^{-2}\) respectively. The excessive shading percentage in the 50% shadow treatment produced a significant reduction in the number of produced fruits by plants in regards to the control treatment, (0%) and the 15% shadow treatment. The number of commercial fruits did not show significant differences between the control treatment (0%) and 15% and 30% treatments. However, a reduction of the number of fruits associated to the shadow percentage could be observed (Figure 14). Higher yields of commercial fruits were produced in the control treatment (0% shade) with 128 fruits\(\cdot\)m\({}^{-2}\), followed by the 15% shadow treatment (119 fruits\(\cdot\)m\({}^{-2}\)), and the 30% shadow treatment (103 fruits\(\cdot\)m\({}^{-2}\)). In the 50% shadow treatment the number of fruits (86 fruits\(\cdot\)m\({}^{-2}\)) were the lowest, however no significant differences with 15% and 30% treatments were found. Figure 13: Shadow effects on the green fruits yield (kg\(\cdot\)m\({}^{-2}\); total, commercial and non-commercial) harvested in the last harvesting period (239 d.a.t). Results comes from the analysis of the variance that is performed according to the Yij model = \(\mu+\alpha i+\beta j+\epsilon ij\). The nomenclature n.s., *, ** and *** indicates not significant or significant for \(p\leq\) 0.05, 0.01 and 0.001, respectively. Numerical values for each column followed by different letters denote statistical significance for \(p<\) 0.05 according to the corrected minimum significant differences (LSD) test.
# Behavior of Two-Chord Steel-Concrete Composite Columns under Axial Compression
## 4 The Design Strength of TCCFST Members
### Discussion of the Results  
The applicability of the three design codes in the analysis of the axial load-bearing strength of the TCCFST column was examined. Comparisons of the calculated (\(N_{u,(code)}\)) and the experimental ultimate forces (\(N_{u,ex.}\)) are presented in Table 5 as absolute force values and \(N_{u,(code)}/N_{u,ex.}\) ratios. Code AISC 360-16 gives the most conservative predictions mainly because of the relatively high resistance factor. Additionally, the code does not consider the enhanced concrete compressive strength induced by the steel tube. The average ratio of the forces \(N_{u,A\!ISC}/N_{u,ex.}\) is 0.61. Eurocode 4 also yields conservative predictions, with the mean value of \(N_{u,EC\!4}/N_{u,ex.}=0.80\). It has to be noted that, although Eurocode 4 considers the enhanced material properties, because of the relative slenderness of \(\overline{\lambda}>0.5\), those properties are not used in calculations. Code Dbj/T13-51 gives the best forecasts of the results, with the average \(N_{u,D\!B\!I}/N_{u,ex.}\) ratio of 1.06. The advantage of the Dbj/T13-51 code arises from the enhanced concrete compressive strength due to the confining effect provided by the steel tube.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(N_{u,e}\) & \(N_{u,EC\!4}\) & \(N_{u,EC\!4}\) & \(N_{u,EC\!4}/N_{u,ex.}\) & \(N_{u,A\!ISC}\) & \(N_{u,A\!ISC}/N_{u,ex.}\) & \(N_{u,D\!B\!I}/N_{u,ex.}\) & \(N_{u,D\!B\!I}/N_{u,ex.}\) & \(N_{u,F\!EM}  
The sectional capacities determined by the experimental and design code analysis are comparatively presented in Figure 16. Eurocode 4 and AISC 360-16 both gave conservative predictions, which were well over the 10% limits, while the DBJ/T13-51 code gave a very good prediction.  
The bearing capacity and deformational behavior of CFST columns greatly depend on the value of the relative strength ratio, \(\xi\), which was also noted by [PERSON] et al. [33] and [PERSON] et al. [35]. The improved concrete compressive strength in the CFST member is less obvious as the concrete compressive strength increases because high-strength concrete exhibits stiffer behavior than regular-strength concrete (Figure 14).  
In the presented analysis, it can be seen that the mentioned design codes can be used to determine the compressive ultimate axial force of the TCCFST column. The summation of the concrete and steel strengths is used in the design codes to calculate the plastic sectional resistance of the TCCFST columns. This approach provides a reasonable estimation of the column's load-bearing capacity, among which the code DBJ/T13-51-2010 gives the best predictions. An additional investigation is needed to predict the ductile response of steel-concrete composite members, which is often misjudged. Further analysis could include the improved strength of the concrete under different values of the confinement factor provided by the steel tube and the analysis of TCCFST columns made from steel tubes with different cross-section classes.
###### Abstract  
Rapid urbanization has transformed cityscapes worldwide, yet vertical urban growth (VUG) receives less attention than horizontal expansion. This study mapped and analyzed VUG patterns in Wuhan, China, from 2012 to 2020 based on a Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) dataset derived from a long time series of 375 COSMO-SkyMed SAR images. The methodology involved full-stack processing (analyzing all 375 images for a stable reference), sub-stack processing (independently processing sequential image subsets to track temporal changes), and post-processing to extract persistent scatterer (PS) candidates, estimate building heights, and analyze temporal changes. Validation was conducted through drone surveys and ground measurements in the Hanyang district. Results revealed substantial vertical expansion in central districts, with Hanyang experiencing a 66-fold increase in areas with buildings exceeding 90 m in height, while Hongshan district saw a 34-fold increase. Peripheral districts instead displayed more modest growth. Time series analysis and 3D visualization captured VUG temporal dynamics, identifying specific rapidly transforming urban sectors within Hanyang. Although the study is focused on one city with accuracy assessed on a spatially confined sample of more than 500 buildings, the findings suggest that PSInSAR height estimates from high-resolution SAR imagery can complement global settlement datasets (e.g., Global Human Settlement Layer, GHSL) in order to achieve better accuracy for individual building heights. Validation generally confirmed the accuracy of PSInSAR-derived height estimates, though challenges remain with noise and the distribution of PS. The location of PS along the building instead of the building rooftops can affect height estimation precision.  
PSInSAR; COSMO-SkyMed; vertical urban growth; height estimation; Wuhan; GHSL +
Footnote †: journal: Remote Sensing  
1, 2020  
1915 Article  
A][PERSON] 1,A][PERSON] 1,A][PERSON] 2,A][PERSON] 3  
Footnote 1: [[https://doi.org/10.3390/rs1711915](https://doi.org/10.3390/rs1711915)]([https://doi.org/10.3390/rs1711915](https://doi.org/10.3390/rs1711915))
## 2 Background and Related Works
Furthermore, reinforcement learning has been introduced to capture spatiotemporal dependencies and enhance solution robustness under varying conditions [27; 28]. Nevertheless, limited efforts have been made to integrate soft time window penalties directly into a comprehensive scheduling objective, especially when cost, time, and environmental goals must be balanced concurrently. This creates a need for models that reflect more realistic time flexibility while supporting effective trade-offs among multiple performance indicators. Environmental sustainability has become increasingly central to intermodal transport optimization, with carbon emissions frequently incorporated as key objectives or constraints. Mixed integer programming has been employed to evaluate carbon pricing strategies and their effects on emissions and profitability [29; 30]. Robust optimization frameworks have addressed uncertainties in demand and policy, improving the stability of low-carbon solutions [31; 32]. Several models incorporate service-level considerations, including customer satisfaction, alongside economic and environmental factors [33; 34]. Carbon tax and cap-and-trade mechanisms significantly influence mode selection and cost effectiveness under varying policy scenarios [35; 36; 37]. In addition, regional constraints such as emission caps and congestion controls have been integrated into multimodal planning models to assess system-wide impacts [38]. Despite these efforts, carbon pricing is often treated as an external parameter, rather than an internalized variable within the objective function. This limits the ability of existing models to support cost-sensitive and adaptive low-carbon decision-making across transport modes. Although previous research has primarily focused on cost reduction and network efficiency in intermodal scheduling, limited efforts have been made to integrate multiple performance objectives within a unified framework. In particular, the joint consideration of cost, time, and carbon emissions under soft time window and carbon tax constraints remains insufficiently addressed. To bridge this gap, this study incorporates time window penalty costs and carbon tax expenses into the total cost and proposes a multi-objective optimization model that simultaneously accounts for economic, temporal, and environmental goals. The model is solved using the IGWOHHO algorithm, which effectively balances global exploration and local exploitation. A case study is conducted to validate the proposed model and analyze the impact of carbon tax policies on intermodal scheduling decisions.
## 4 Results and Discussion
### Station Performance Analysis  
The comprehensive analysis of station performance confirmed the identification of bottlenecks and revealed operational patterns:
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 3 Proposed Power Control for SC-GFDMA
### Appropriate Value of \(\gamma\)  
The performance of the proposed power control strategy depends on the value of the common ratio \(\gamma\) because it determines whether our power control criterion (47) is satisfied. With \(\zeta_{u_{k},m}=a_{k}\), the inequality (47) can be rewritten as  
\[\left(1-f\left(\Gamma_{u_{3},m}^{(1)}\right)\right)\zeta_{u_{3},m} \geq f\left(\Gamma_{u_{1},m}^{(2)}\right)\zeta_{u_{1},m}\] \[\qquad\Longleftrightarrow\left(1-f\left(\Gamma_{u_{3},m}^{(1)} \right)\right)a_{U_{m}}\cdot\gamma^{U_{m}-3}\geq f\left(\Gamma_{u_{1},m}^{(2) }\right)a_{U_{m}}\cdot\gamma^{U_{m}-1} \tag{55}\] \[\qquad\Longleftrightarrow\left(1-f\left(\Gamma_{u_{3},m}^{(1)} \right)\right)\geq f\left(\Gamma_{u_{1},m}^{(2)}\right)\gamma^{2}, \tag{56}\]  
where we consider the case of \(k=1\) for the estimation of \(\gamma\) because the best condition for the first iteration can reduce the error propagation in the following detections. We can reduce the transmit power by making the value of \(\gamma\) as small as possible; this corresponds to accommodating more users in the same chunk, which further corresponds to an increased spectral efficiency (22). Thus, the proposed method selects the smallest value of \(\gamma\) that satisfies the inequality at the right-hand side (RHS) of (56). As \(\Gamma_{u_{3},m}^{(1)}\) is also given by a function of \(\gamma\), and as \(f\) is a numerically estimated function in SNR-variance density evolution, the inequality in (56) cannot be explicitly solved with respect to \(\gamma\). Thus, the minimum value of \(\gamma\), which is denoted as \(\gamma_{sol}\), is obtained through a simple numerical quantization in this study. Figures 4 and 5 illustrate the procedure by depicting the behaviors of \(1-f(\Gamma_{u_{3},m}^{(1)})\) and \(f(\Gamma_{u_{1},m}^{(2)})\gamma^{2}\) as functions of \(\gamma\). Both \(1-f(\Gamma_{u_{3},m}^{(1)})\) and \(f(\Gamma_{u_{1},m}^{(2)})\gamma^{2}\) are nonlinear functions of \(\gamma\); \(1-f(\Gamma_{u_{3},m}^{(1)})\) peaks at a relatively low \(\gamma\) whereas \(f(\Gamma_{u_{1},m}^{(2)})\gamma^{2}\) increases to an asymptote at \(1\) as \(\gamma\) increases. Thus, simple numerical quantization can determine the minimum solution of the inequality at the RHS of (56); we only require a lookup table for the function \(f(\cdot)\) in SNR-variance density evolution to determine \(\gamma_{sol}\) offline. Table 2 summarizes the values of \(\gamma_{sol}\), which vary depending on the MCS. The pre-calculated \(\gamma_{sol}\) is substituted into the proposed algorithm to increase the spectral efficiency and reduce the total transmit power in each chunk.
## 2 Channel Model  
A typical RIS-aided parallel MIMO VLC system is considered in the paper. For a parallel MIMO VLC channel, the number of transmitters is the same as the receivers. As shown in Figure 1, we assume that there are \(N\) transmitters, \(N\) receivers, and \(K\) RIS elements in this paper. Normally, the number of RIS elements is much larger than the number of the transmitters in RIS-aided VLC systems (i.e., \(K>N\)). The Cartesian coordinates have been considered in the figure. Owing to the features of the parallel MIMO VLC channel, a point-to-point (P2P) channel is formed by matching the transmitters and the receivers one by one. The LoS channel and the nonline-of-sight (NLoS) channel are displayed in the figure. The model of RIS-aided parallel MIMO VLC is first discussed in our work and derived according to the Lambertian model. We will introduce the details of this model in this section.  
Before introducing the channel model further, we need to specify the geometric parameters determining the channel gain. It is supposed that the coordinates of the \(n\)-th light-emitting diode (LED), the \(n\)-th photodiode (PD), and the \(k\)-th RIS element are \((x_{S_{n}},y_{S_{n}},z_{S_{n}})\), \((x_{D_{n}},y_{D_{n}},z_{D_{n}})\) and \((x_{k},y_{k},z_{k})\). The distance between the \(n\)-th transmitter and the \(n\)-th receiver is denoted by \(d^{D_{n}}_{S_{n}}\). Meanwhile, \(d^{R_{k}}_{S_{n}}\) and \(d^{R_{k}}_{D_{n}}\) denote the distance of the \(n\)-th LED to the \(k\)-th RIS element and the \(k\)-th RIS element to the \(n\)-th PD, respectively.  
All of the distance calculated by the distance formula between two points. Without loss of generality, the normal vector of the transmitter is assumed to be downward towards the floor, and meanwhile, the normal vector of the receiver is upward towards the ceiling. Consequently, the angle of incidence \(\Phi_{D_{n}}^{S_{n}}\) and irradiance \(\Theta_{S_{n}}^{D_{n}}\) of the \(n\)-th LoS channel are expressed as  
\[\Theta_{S_{n}}^{D_{n}}=\Phi_{D_{n}}^{S_{n}}=\arccos(\frac{z_{S_{n}}-z_{D_{n}}}{ d_{S_{n}}^{D_{n}}}), \tag{1}\]  
The angle of the irradiance of the NLoS link correlated with the \(k\)-th RIS element and the \(n\)-th LED is given by  
\[\theta_{S_{n}}^{R_{k}}=\arccos(\frac{z_{S_{n}}-z_{k}}{d_{S_{n}}^{R_{k}}}). \tag{2}\]  
The angle of the incidence of the NLoS channel connected to the \(n\)-th PD, and the \(k\)-th RIS element is formulated as  
\[\phi_{D_{n}}^{R_{k}}=\arccos(\frac{z_{k}-z_{D_{n}}}{d_{D_{n}}^{R_{k}}}). \tag{3}\]  
All the notations introduced above are labeled in Figure 1.
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System
### Development of Cloud Management Platform
#### 3.2.1 Function Design  
The functions of the cloud management platform are as follows: (i) User login: The users of this platform are bridge maintenance management units, and the number of users is customized by the backend. Users can remotely log in from any computer with internet access using their account and password; (ii) Participant account management: As the target participants of this system are daily commuters and the participation is voluntary, the system needs to assign accounts for those who are interested in this work. When participants are no longer involved, their account can be deleted to release the corresponding resources; (iii) Crack information management: Crack information includes images, GPS coordinates, and the corresponding text descriptions. Crack information management allows users to download images, coordinates, and texts individually or in batches for use; (iv) Crack information search: As crack collection is a long-term project and many images can be uploaded daily, crack information search allows users to search crack images by time and can directly display the images on the map, enabling users to quickly obtain the crack positions.