esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Evaluating the RT Thresholds  
To answer our first research question about producing viable RT thresholds for detecting rapid guessing, we compared the results obtained from the random search and genetic algorithm methods with those from the baseline model based on the original response dataset (i.e., potential random guesses are ignored). To answer the second research question on the consistency of RT thresholds obtained from different threshold-setting procedures, we compared the results of the random search and genetic algorithm methods with those obtained from the NT10, NT15, NT20, and NT25 methods.  
To make a model-level evaluation, we used several model fit indices such as Akaike's Information Criterion (AIC), sample-size-adjusted BIC (SABIC), Bayesian Information Criterion (BIC), Hannan-Quinn criterion (HQ), and log-likelihood value. In this step, we also conducted a small simulation study to highlight the importance of purposeful threshold selection. For each item, we identified the largest number of flagged responses across the NT, random search, and genetic algorithm methods, removed the same number of randomly selected responses from the response dataset, and then obtained model fit indices. This procedure was repeated 500 times, and the average fit indices were reported. We anticipated that removing responses randomly (without considering RT values) would yield worse results than the threshold-setting methods, especially in terms of reliability.  
To compare the results from different threshold-setting methods, we analyzed item difficulty values across the different threshold-setting methods, correlated the ability estimates obtained using different IRT models (i.e., baseline models, NT-based methods, random search, and genetic algorithm), and compared the proportion-correct scores for rapid guessers using the random search and genetic algorithms. In addition, we also evaluated the efficiency of the random search and genetic algorithms regarding the computational time required by each method to find an optimal solution for each item.
# Optimized Combination of Local Beams for Wireless Sensor Networks
## 2 System Model
Afterward, without the prior knowledge of \(\Omega_{i}\), each node \(d_{i}\) steers the beam toward \(\theta_{i}=\varphi_{j}\) and retransmits the signal mixed with the closed-loop phase offset \(\Gamma_{i,j}=\rho_{i}\left(\varphi_{j}\right)\)[22], where the two beam-alignment status can be extracted from a reference position system such as the Global Positioning System (GPS) [36; 37]. Then, without considering mutual coupling effects among the nodes, the combined array factor is given as  
\[\begin{array}{rcl}\mathcal{AF}_{t}\left(\phi\right)&=&\sum\limits_{i=1}^{I_{ c}}\mathcal{AF}_{i}\left[\phi\ |\ \varphi_{j}\right]e^{jk\Gamma_{i,j}}\\ &=&\sum\limits_{i=1}^{K}\sum\limits_{n=0}^{N-1}e^{-jk\left\{\rho_{i}(\phi)- \rho_{i}(\varphi_{j})-nd[\cos(\phi-\Omega_{i})-\cos\varphi_{j}]\right\}}\\ &=&\sum\limits_{i=1}^{K}\sum\limits_{n=0}^{N-1}e^{jk\left\{2\sin\left(\frac{ \varphi_{j}+\varphi}{2}-\psi_{i}\right)\sin\left(\frac{\varphiand the power pattern is also represented as  
\[\mathcal{P}\left(\varphi\right)=\left|\mathcal{AF}_{t}\left(\varphi\right)\right|^ {2}. \tag{4}\]  
Without the loss of generality, the directions of the desired and undesired APs are assumed to be \(\varphi_{1}=0\), \(\varphi_{2}=\pi/3\), \(\varphi_{3}=2\pi/3\), and \(\varphi_{4}=\pi\) henceforth. To verify the feasiblity of combining the multiple beams, the power levels observed in those directions are illustrated in Figures 2 and 3. As seen in Figure 2, the higher value of \(l_{c}\) (i.e., increasing the number of the nodes) significantly contributes to the improvement of \(\mathcal{P}\left(\varphi_{1}\right)\) due to the increased node density over the given area. However, it is also verified that \(N\) (i.e., the elements of the ULAs) is irrelevant to \(\mathcal{P}\left(\varphi_{1}\right)\). This phenomenon can be explained by the fact that the beams are randomly headed, and consequentially part of the beams cannot illuminate the desired AP. In addition, because of the previously mentioned reasons that the beams are randomly positioned and headed, the higher value of \(l_{c}\) and \(N\) increases the average power level of the sidelobes in Figure 3.  
Figure 3: Simulation results of the system model with various \(N\) and \(l_{C}\) - Average power levels of the sidelobes, \(\sum_{j=2}^{4}\mathcal{P}\left(\varphi_{j}\right)/3\), over the undesired directions of \(\varphi_{2}=\pi/3\), \(\varphi_{3}=2\pi/3\), and \(\varphi_{4}=\pi\). Figure 2: Simulation results of the system model with various \(N\) and \(l_{C}\) - Power levels, \(\mathcal{P}\left(\varphi_{1}\right)\), in the desired direction of \(\varphi_{1}=0\).
## 4 Materials and Methods
### Solid-Phase Microextraction (SPME)  
To describe the chemical profile of the headspace from two EOs and of soil samples, a SPME device from Supelco (Bellefonte, PA, USA) was used for the sampling. The soil (\(\sim\)1 g) and the EOs (\(\sim\)2 mL) were individually placed into a 15 mL glass vial with PTFE-coated silicone septum. The chosen fiber was coated with 50/30\(\upmu\)m DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane). Before sampling, the fiber was conditioned at 270 \({}^{\circ}\)C for 20 min. First, the samples were equilibrated for 30 min at 50 \({}^{\circ}\)C prior to analysis. Subsequently, the fiber was exposed to the equilibrated headspace for 10 and 30 min to capture the volatile components from EOs and soil samples, respectively. Later, the fiber was inserted in a GC injector maintained at 250 \({}^{\circ}\)C for the desorption of collected components.
## 4 Prototype Fabrication and Measured Results  
Figure 16a,b depicts the prototype of the portable wideband directional antenna scheme for digital terrestrial television reception without and with ABS radome. Experiments with the antenna prototype (75 \(\Omega\) input impedance) were carried out in an anechoic chamber using a 50 \(\Omega\) vector network analyzer (Agilent E5061B) [34]. The impedance transformer (TME ZT-205) was used to match a 75 \(\Omega\) antenna with a 50 \(\Omega\) measurement system.  
Figure 17 compares the simulated and measured impedance bandwidth (\(\left\lvert\,S_{11}\,\right\rvert\leq-10\) dB) of the portable wideband directional antenna scheme. The measured impedance bandwidth  
Figure 16: Prototype of the portable wideband directional antenna scheme: (**a**) without radome, (**b**) with radome.  
Figure 15: Simulated impedance bandwidth (\(\left\lvert\,S_{11}\,\right\rvert\leq-10\) dB) under variable parameters: (**a**) balun height (\(H_{\text{balun}}\)), (**b**) distance from center to center of the balun (\(D_{\text{balun}}\)).  
was 75.93%, covering the 424-943 MHz frequency band, with the measured antenna gain of 2.69-4.84 dBi (Figure 18).  
As shown in Figure 19, the radiation pattern of the proposed portable wideband horizontally polarized directional antenna scheme was of unidirectionality. The measured xz- and yz-plane cross-polarizations were below \(-20\) dB and \(-40\) dB, respectively, with the corresponding HPBW of \(150^{\circ}\), \(159^{\circ}\), and \(160^{\circ}\) and \(102^{\circ}\), \(78^{\circ}\), and \(102^{\circ}\) at 470 MHz, 680 MHz, and 890 MHz, respectively. The discrepancy between the simulated and measured cross-polarization could be attributed to the use of self-trapping screws in the antenna prototype fabrication, as shown in Figure 16a. The measured xz- and yz-plane back lobe levels were below \(-9\) dB. The experiments also revealed that the ABS radome had negligible impacts on the impedance bandwidth and the antenna gain, vis-a-vis the antenna scheme without the ABS radome. Table 2 tabulates the simulated and measured performance of the proposed portable wideband directional antenna scheme. Table 3 shows a comparison between previous works and the proposed portable wideband directional antenna scheme.  
Figure 17: Simulated and measured impedance bandwidths of the portable wideband directional antenna scheme.  
Figure 18: Simulated and measured gains of the portable wideband directional antenna scheme.  
Figure 19: Simulated and measured xz- and yz-plane radiation patterns of the portable wideband directional antenna scheme: (**a**) 470 MHz, (**b**) 680 MHz, and (**c**) 890 MHz.
## 4 Conclusions  
This study evaluated the reduction efficiency of each water quality improvement scenario using the SWAT-EFDC linkage model for the Ganwol estuarine reservoir, which is experiencing water quality problems because of a continuous inflow of point and nonpoint source pollution from the upstream basin. In particular, because of the embankment located in the middle of the reservoir, it is necessary to analyze whether the effect of improving the water quality can be spread throughout the reservoir because of the location of a levee in the middle before applying the water quality management plan. Therefore, in this study, scenarios for reducing the external inflow load and internal load were applied, and the effects of the water quality improvement were analyzed by considering the levee's influence.  
If we look at the water quality changes of each location according to the effect of the water quality management plan, dredging improved the reservoir's overall water quality by increasing its internal water quality. However, the PSTWs' effluent management had a significant effect in improving the water quality in terms of reducing the inflow of pollutants, but the efficiency of Zone B was relatively lower than that of Zone A because of the levee's influence. Therefore, in the current condition where the water quality in Zone A is worse than that in Zone B, it is appropriate to improve the water quality in the short term by improving the water quality discharged from the PSTWs. However, to improve the water quality of the entire reservoir in the long term, increasing the internal resilience by dredging can be considered to offset the levee's impact.  
This study evaluated water quality management scenarios using the SWAT-EFDC linkage model, which differs from other studies using the HSPF-EFDC linkage model; the difference is in the consideration of the nonpoint sources of pollution and farming mechanisms of agricultural land located around the reservoir. In addition, this study is meaningful because it comprehensively reviews the effects of embankments and water quality improvement scenarios and measures to improve resilience for improving water quality in reservoirs. In addition to evaluating the reservoir's resilience, additional studies are needed to consider a management plan for improving water quality and increasing the resilience of the overall watershed and analyze its impact on the reservoir.  
Conceptualization, S.H. and M.-S.K.; writing--original draft preparation, S.H.; simulation of the linkage model, S.H., S.-M.J. and K.K.; writing--review and editing, H.K. and J.-H.S.; supervising the research, M.-S.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government. (MSIT) (202020R1A2C2003808). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 3 Results and Discussion
### Powder Feed Rate  
An increase in the powder mass flow (PMF) only resulted in a higher layer thickness (Figure 6a).  
The slight deviation of the wall thickness is within the area of uncertainty and is, consequently, not significant. The reason for increasing layer thickness was a combination of increased volume input of filler material per unit length of weld seam and lower part temperature. Figure 6b shows an example of the temperature development of the fourth layer during the production of layers 5-25 (represented by the welding time) at a welding current of \(I_{\text{s}}=105\) A and a welding speed of \(v_{\text{s}}=36\) cm/min. The peak height can be set equal to the energy input into the depth of the component. The lower component temperature at higher PMF (parameters remaining the same) can be justified with formula 2 in combination with formula 3. Where \(Q_{i}\) is the required amount of heat in \(J\), \(m_{i}\) is the supplied powder mass in \(g\), \(m_{p\_j}\) is the powder federate in g/min and \(\Delta T_{i}\) is the temperature difference in \(K\). The variables heat capacity \(c\) and welding time \(t_{si}\) can be neglected due to material constancy or equal construction times.  
\[Q_{i}=m_{i}\cdot c_{i}\cdot\Delta T_{i} \tag{2}\]  
\[m_{i}=\dot{m}_{P\_i}\cdot t_{S_{i}} \tag{3}\]  
\[\frac{m_{1}}{m_{2}}=\frac{T_{2}}{T_{1}} \tag{4}\]  
Figure 5: Effect of welding speed on layer thickness (**a**) and wall thickness (**b**) (standardized representation).  
Figure 6: Standardized representation of the effect of welding speed on layer and wall thickness (**a**). Evolution of the part temperature as a function of the build time and the PMF (**b**).  
By equating Equations (2) and (3) and rearranging them, Equation (4) is created. The mass ratio can be calculated by using the respective powder feed rates, taking into account the correct powder utilization rate. The temperature ratio is described by the respective areas under the temperature-time curve, which is determined by integration, and is to be interpreted as a dimensionless parameter. This results in 0.6985 = 0.7121, which is sufficiently accurate proof of the cause of the lower component temperatures and, thus, for the layer thicknesses increasing with the increase of PMFs.  
The significant effect on the layer thickness and the lack of correlation with the wall thickness is also described by [PERSON] et al. [18]. [PERSON] et al. [17], on the other hand, describe the influence of a modified powder feed rate on the seam geometry as small. The reason for this contradictory opinion is to be found in the data basis. [PERSON] et al. [17] only use a single coating layer as an evaluation parameter, and not an additive structure. This prevents a representation of typical effects in additive manufacturing, such as the multiple remelting of lower layers with associated hardness fluctuation [23], and leads to these deviations.
###### Abstract  
Urban areas are subject to subsidence due to varying natural and anthropogenic causes. Often, subsidence is interpreted and correlated to a single causal factor; however, subsidence is usually more complex. In this study, we adopt a new model to distinguish different causes of subsidence in urban areas based on complexity. Ascending and descending Sentinel-1 data were analyzed using permanent scatterer interferometry (PS-InSAR) and decomposed to estimate vertical velocity. The estimated velocity is correlated to potential causes of subsidence, and modeled using different weights, to extract the model with the highest correlations among subsidence. The model was tested in Alexandria City, Egypt, based on three potential causes of subsidence: rock type, former lakes and lagoons dewatering (FLLD), and built-up load (BL). Results of experiments on the tested area reveal singular patterns of causal factors of subsidence distributed across the northeast, northwest, central south, and parts of the city center, reflecting the rock type of those areas. Dual causes of subsidence are found in the southwest and some parts of the southeast as a contribution of rock type and FLLD, whereas the most complex causes of subsidence are found in the southeast of the city, as the newly built-up areas interact with the rock type and FLLD to form a complex subsidence regime. Those areas also show the highest subsidence values among all other parts of the city. The accuracy of the final model was confirmed using linear regression analysis, with an R\({}^{2}\) value of 0.88.  
Keywords:Alexandria; Egypt; SAR; Permanent scatterer interferometry (PS-InSAR); Sentinel-1; urban subsidence +
Footnote †: journal:
## 3 Problem Formulation and Modeling
### Modeling
#### 3.2.2 Actor  
The action space is defined as the set of operations involved in adjusting the duration of traffic lights. Each action is considered as a vector, encompassing the duration of traffic lights at each intersection. However, for effective traffic management and to prioritize buses without causing traffic congestion, it is essential to dynamically adjust the action space based on the vehicle arrival rate. Consequently, the action space is expanded to encompass dynamic adjustments of traffic light durations according to varying traffic conditions, enabling more efficient traffic flow control and optimized bus transit times.  
In the real-time signal-priority control of self-driving-bus arterial lines, the agent must select an appropriate action based on the current state, specifically adjusting the traffic light state at the present moment. Hence, the action space can be represented as a set comprising multiple elements, with each element corresponding to a traffic light state that the agent can choose as the current one. For the real-time signal-priority control task of self-driving buses, the action space is defined as follows: for each lane of each intersection, there is a set of minimum green time \(\Delta{a_{i}}^{\text{min}}(t)\) and maximum green time \(\Delta{a_{i}}^{\text{max}}(t)\) that can be applied. Here, \(k\) represents the intersection number. Therefore, the size of the action space is determined as \(2M\), where \(M\) represents the number of intersections. The pseudocode for the action space is presented in Table 1.  
To provide a more comprehensive illustration of the issue, the action space for adjusting the signal duration based on traffic flow conditions can be represented as a vector \(A=(a_{1},a_{1},a_{1}\cdots a_{n})\) containing \(n\) intersections, where each element \(a_{i}\) represents the traffic light duration of the \(i\)-th intersection. At each time \(t\), the duration of the traffic light at intersection \(i\) can be dynamically adjusted in response to the current traffic flow:  
\[a_{i}(t+1)=\max\Bigl{\{}\min\{a_{i}(t)+\Delta a_{i}(t),a_{i}^{\max}\},a_{i}^{ \min}\Bigr{\}} \tag{4}\]  
where \(a_{i}^{\max}\) is the maximum duration of traffic lights at the \(i\)-th intersection; \(a_{i}^{\min}\) is the minimum duration of traffic lights at the \(i\)-th intersection; \(\Delta a_{i}(t)\) is the action in the action space:  
\[\Delta a_{i}(t)=\sum_{j=1}^{2N}w_{i,j}q_{j}(t) \tag{5}\]  
where \(w_{i,j}\) represents the value of state \(q_{j}\) at time \(i\); \(q_{j}(t)\) represents the value of state \(j\) at time \(t\).  
The state \(q_{j}\) can be represented as a vector containing \(2N\) elements, consistent with the state space. The first \(N\) elements \(q_{j}(t)\) represent the number of vehicles in the current lane, while the last \(N\) elements represent the speed of incoming vehicles.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 2 Related Works
### Segmentation of Mirror Image Region  
This method requires the estimation of the position of the mirror from the shot image because the positional relationship between the mirror and the camera is not fixed to realize a wide observation range and a high degree of freedom. Therefore, the mirror image region in the shooting image is required to be segmented.  
In this method, it is necessary to estimate the position of the mirror from the captured image because the positional relationship between the mirror and the camera is not fixed, allowing for a wide observation range and a high degree of freedom. Consequently, the mirror image region in the captured image needs to be segmented.  
Accurately segmenting the mirror image region from a monocular image is generally challenging due to the reflective nature of mirrors. Previous research has explored methods such as using infrared light [13] and photometric stereo [14]. However, these approaches require additional equipment, resulting in larger setups. Alternatively, deep learning methods [15; 16] have been employed to address the segmentation challenge. These methods, such as MirrorNet proposed by [PERSON] et al. [15], utilize deep neural networks to detect mirror edges and segment the mirror image region implicitly. By extracting contextual features from multi-layered scale images and comparing these features between the mirror and the surrounding environment, MirrorNet effectively separates the mirror image region from the rest of the image. Importantly, deep learning-based approaches provide a solution that overcomes the limitations of equipment size, making them more practical for implementation in compact imaging systems.
# Considered Factors of Online News Based on Respondents' Eye Activity Using Eye-Tracker Analysis
## 3 Research Methodology
### Research Procedure  
#### 3.2.1 Pre-experiment Procedure  
The research started with a literature study from several previous related studies on eye-tracking and anxiety. We also consulted with experts in the field of psychology to understand more deeply about anxiety. Eye data recording devices used were in isolation rooms and included Tobii Eye Trackers, console recorders, desktops with screen displays (\(1920\times 1080\)), and Open Broadcaster Software (OBS). Data that is the news, were retrieved from Indonesian news websites from reliable sources. Selection of the respondent population followed random sampling in the age range of 18 to 60 years. In the stage of assessing the level of anxiety of respondents, the BAI (Beck Anxiety Inventory) instrument was used and converted to Indonesian following the method of \"forward translation with testing\" for research instruments so that the respondents felt more comfortable and were easier to understand [64].  
The pilot test phase was carried out before actual data retrieval. This phase intended to assess and improve research tools and materials before collecting real data [65]. In the trial, we processed all collected data until the trial's conclusion. The aim was to assess whether the tools and materials at that stage could produce data and outcomes accordingly. All eye-tracking data in this study, both quantitative and qualitative, were susceptible and needed to be accurately collected.  
After repeated trials, data collection took place in isolation to avoid unnecessary disturbances and factors when respondents read news content. By focusing respondent attention we hoped to minimize the possibility of invalid data, especially from eye-trackers. Respondents needed to sit stillwhile reading news in the same position from the beginning to the end of the process. Anxiety was evaluated after the respondent finished reading the news by filling in the BAI. Several questions were asked to obtain qualitative data from respondents about their opinions of news content, what factors influenced or encouraged their anxiety, and finally, their views about the news media in Indonesia. Qualitative data with BAI records were triangualted with eye-tracking data.