esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Tailpipe Measurements
#### 3.1.3 \(\mathrm{NO_{x}}\) Concentrations Comparison  
Figure 7 presents the differences between the \(\mathrm{NO_{x}}\) analyzers measuring at the tailpipe of the vehicle. As all of the analyzers had similar specifications, their mean value was considered as the reference. For the SI vehicle, the mean measured \(\mathrm{NO_{x}}\) concentrations were \(<\)30 ppm, while for the CI vehicle, they were between 20 and 140 ppm. The differences of the instruments were within 5% for the CI vehicle, but up to 30% for the SI vehicle. The 5% uncertainty is in agreement with the estimated uncertainty of the current analyzers [9]. The higher uncertainty at the low range is also in line with the higher uncertainty of the analyzers at low concentration ranges that approach their zero levels [9], and a study that compared \(\mathrm{NO_{x}}\) analyzers (10%) [10]. \(\mathrm{NO_{x}}\) analyzers are typically calibrated from 2000 ppm down to 100 ppm. A limited number of data showed that below 100 ppm, the uncertainty increases from 2% to around 5-10% at 10 ppm [9]. These results confirm that current PEMS have a high level of variation at low \(\mathrm{NO_{x}}\) levels; thus, for low \(\mathrm{NO_{x}}\) emission levels, the measurement uncertainty (margin in percentage) is higher than what is currently prescribed. Alternatively, for keeping a similar margin, PEMS need to reduce the uncertainty at low levels.  
There was no particular trend of specific PEMS or of principle (e.g., chemiluminescence detection [CLD] or non-dispersive ultraviolet [NDUV]) underestimating, overestimating, or having non-linearity issues. There was an indication that the tailpipe measurements were slightly lower than the PEMS.  
Figure 8 presents an example of the time aligned \(\mathrm{NO_{x}}\) data of the analyzers. The signals were almost indistinguishable from each other. Although PEMS #1 was tested with low concentrations in general, the few spikes with high concentrations (Figure 8, left panel) were at the same levels as the rest systems, indicating that the good results of Figure 7 (left panel) would also be valid at higher concentrations.  
Figure 6: Examples of \(\mathrm{CO_{2}}\) concentration (%) real time profiles. **Left panel:** SI vehicle. **Right panel:** CI vehicle.  
Figure 7: Differences of \(\mathrm{NO_{x}}\) analyzers. **Left panel:** SI vehicle. **Right panel:** CI vehicle. Each point is the mean value of three repetitions. Dashed lines indicate the 5% assumed uncertainty [9].
## 3 The Proposed Method  
In this paper, a blind robust 3D mesh watermarking technique based on mesh saliency and wavelet coefficients for copyright protection is proposed. The main contribution of this work is the use of QIM quantification of the wavelet coefficients as well as mesh saliency in order to ensure both high imperceptibility and robustness to a wide range of attacks. The imperceptibility requirement is achieved by exploiting the advantages of mesh saliency in order to define the wavelet vectors to be quantized, while robustness is ensured by using the QIM scheme of the selected wavelet vectors using the distance between the vertices and the mesh center as synchronizing primitives. Firstly, multiresolution analysis is applied to the original 3D semi-regular mesh giving a series of approximation meshes and a sequence of wavelet coefficients. Next, the mesh saliency is calculated for the mesh obtained after one wavelet decomposition and salient points are extracted. The reason behind using a certain level \(I\) (Figure 3b) is due to the good capacity-invisibility tradeoff offered. Afterwards, the norms of wavelet coefficients of these points are quantized using QIM quantization. The principle role of the mesh saliency is to define the candidate wavelet coefficients norms to be quantified. The wavelet coefficients, which correspond to the vertices that have the biggest saliency values, are chosen and their norms are quantized. A threshold (\(T_{r}\)) representing the 70% maximum values of the saliency vector was adopted for all the 3D meshes. The embedding and extracting schemes are described in Figures 4 and 5, respectively.
## 5 Results and Discussion
### Analysis of Results among DE Variants
\begin{table}
\begin{tabular}{c c c c c} \hline
**Variant** & **Min \(t\) (s)** & **Max \(t\) (s)** & **Mean \(t\) (s)** & **Std \(t\) (s)** \\ \hline rand/1/bin & 2.4970 & 4.3940 & 3.4077 & \(4.1057\times 10^{-1}\) \\ rand/1/exp & 4.5200 & 5.1780 & 4.8404 & \(1.9767\times 10^{-1}\) \\ best/1/bin & 3.5810 & 5.5190 & 4.6860 & \(6.3739\times 10^{-1}\) \\ best/1/exp & 5.1020 & 5.5660 & 5.4437 & \(1.1601\times 10^{-1}\) \\ current-to-rand/1 & 4.7700 & 5.1690 & 5.0107 & \(9.7751\times 10^{-2}\) \\ current-to-best/1 & 4.9310 & 5.4280 & 5.2747 & \(1.0424\times 10^{-1}\) \\ current-to-rand/1/bin & 4.6220 & 4.9890 & 4.8403 & \(9.0887\times 10^{-2}\) \\ rand/2/dir & 4.7210 & 5.2210 & 5.0707 & \(1.0618\times 10^{-1}\) \\ \hline \end{tabular}
\end{table} 
Table 11: Descriptive results for case study C4 concerning the execution time \(t\). \begin{table}
\begin{tabular}{c c c c c} \hline
**Variant** & **Min \(t\) (s)** & **Max \(t\) (s)** & **Mean \(t\) (s)** & **Std \(t\) (s)** \\ \hline rand/1/bin & 1.9860 & 2.3810 & 2.0911 & \(7.6625\times 10^{-2}\) \\ rand/1/exp & 1.9580 & 2.4860 & 2.2930 & \(1.4654\times 10^{-1}\) \\ best/1/bin & 1.9040 & 2.9310 & 2.4808 & \(3.1398\times 10^{-1}\) \\ best/1/exp & 2.1200 & 2.7530 & 2.4680 & \(2.0237\times 10^{-1}\) \\ current-to-rand/1 & 2.2250 & 2.3070 & 2.2728 & \(2.0898\times 10^{-2}\) \\ current-to-best/1 & 1.2250 & 2.5610 & 2.4084 & \(3.1461\times 10^{-1}\) \\ current-to-rand/1/bin & 2.0810 & 2.3380 & 2.2274 & \(6.0469\times 10^{-2}\) \\ rand/2/dir & 1.8990 & 2.3310 & 2.2566 & \(9.3149\times 10^{-2}\) \\ \hline \end{tabular}
\end{table} 
Table 8: Descriptive results for case study C1 concerning the execution time \(t\). \begin{table}
\begin{tabular}{c c c c c} \hline
**Variant** & **Min \(t\) (s)** & **Max \(t\) (s)** & **Mean \(t\) (s)** & **Std \(t\) (s)** \\ \hline rand/1/bin & 1.7490 & 2.1290 & 2.0658 & \(6.6250\times 10^{-2}\) \\ rand/1/exp & 2.0840 & 2.1770 & 2.1334 & \(3.1339\times 10^{-2}\) \\ best/1/bin & 2.6380 & 2.8540 & 2.7955 & \(4.2712\times 10^{-2}\) \\ best/1/exp & 2.3830 & 2.4660 & 2.4214 & \(2.9671\times 10^{-2}\) \\ current-to-rand/1 & 1.9430 & 2.0900 & 2.0276 & \(3.9621\times 10^{-2}\) \\ current-to-best/1 & 2.0050 & 2.3340 & 2.2448 & \(6.9939\times 10^{-2}\) \\ current-to-rand/1/bin & 1.5270 & 2.0720 & 1.8059 & \(1.7287\times 10^{-1}\) \\ rand/2/dir & 1.8820 & 2.1040 & 2.0429 & \(5.7365\times 10^{-2}\) \\ \hline \end{tabular}
\end{table} 
Table 9: Descriptive results for case study C2 concerning the execution time \(t\).
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 4 Model Performance and Results Analysis
### Overall Findings
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**MODEL** & \multicolumn{3}{c}{**First Approach**} & \multicolumn{3}{c}{**Second Approach**} & \multicolumn{3}{c}{**Third Approach**} \\ \hline  & **AIC** & **BIC** & **AUC** & **AIC** & **BIC** & **AUC** & **AIC** & **BIC** & **AUC** \\ \hline
**1 PL-IRT** & 1776.870 & 1961.771 & 0.712 & 1936.500 & 2424.108 & 0.8217 & 1416.443 & 1574.109 & 0.803 \\
**TP-IRT** & 1773.147 & 1960.125 & 0.712 & 1935.437 & 2423.044 & 0.8220 & 1472.328 & 1635.250 & 0.793 \\
**TWD-IRT** & 2370.730 & 2557.708 & 0.693 & 3598.069 & 4085.677 & 0.400 & 1689.128 & 1852.083 & 0.772 \\
**STP-IRT** & 2118.256 & 2305.234 & 0.692 & 1936.493 & 2424.101 & 0.8219 & 1764.483 & 1927.405 & 0.781 \\
**NRT-IRT** & 2083.337 & 2270.315 & 0.681 & 2632.602 & 3120.209 & 0.339 & 1511.271 & 1674.193 & 0.759 \\
**DTA-IRT** & 1761.410 & 1948.388 & 0.711 & 1936.493 & 2424.100 & 0.8222 & 1410.343 & 1573.265 & 0.804 \\
**ART-IRT** & 2022.327 & 2209.305 & 0.675 & 2632.752 & 3120.360 & 0.429 & 1614.018 & 1776.940 & 0.734 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of AIC, BIC, and AUC values across three computational approaches. Table 2 compares the performance of different IRT models based on TIF (Test Information Function), log-likelihood, and item fit values across the first, second, and third approaches. Higher log-likelihood values, higher TIF, and balanced item fit values indicate better model fit and reliability. Overall, the 1 PL-IRT and DTA-IRT models demonstrated the most stable and reliable performance. Notably, the DTA-IRT model achieved high log-likelihood values across all three approaches, indicating a strong fit to the data and providing a high Test Information Function (TIF). Similarly, the TP-IRT model also performed well but showed slightly higher log-likelihood values compared to DTA-IRT. On the other hand, the TWD-IRT, NRT-IRT, and ART-IRT models exhibited weaker model fit, particularly in the second and third approaches, with lower log-likelihood values and less stable item fit results. These models, especially those incorporating multiplicative transformations, suffered from performance loss, suggesting that multiplicative adjustments may introduce additional noise, reducing estimation stability. In conclusion, these findings indicate that models using subtractive transformations (DTA-IRT and TP-IRT) tend to produce more stable and reliable results compared to those employing multiplicative transformations (TWD-IRT and ART-IRT). The consistent performance of the DTA-IRT model across all approaches suggests that integrating response time as a subtractive factor enhances measurement accuracy and model robustness.
# Numerical Heat Transfer Simulation of Oil Shale Large-Size Downhole Heater
## 4 Discussion
### Field Application Measures  
In situ cracking in the field has revealed that, in addition to the oil shale reservoir's low thermal conductivity, the limited flow rate of heated gas that can be injected plays a significant role in the difficulty to heat oil shale reservoirs on an extensive scale. In comparison to earlier research [34; 37], it is clear that the downhole heater is limited by the well diameter, the inner volume of the shell range is tiny, and it cannot heat a big flow rate of gas; the maximum volume flow rate is approximately 150 m\({}^{3}\)/h. When compared to typical downhole heaters, large-sized heaters can greatly improve heating efficacy. As indicated in Figure 4, the lowest output temperature among the nine circumstances mentioned in this study is 487.283 K. Gas injection can still be utilized to heat the reservoir over time. The advantage of a larger downhole heater is that it offers a wider range of operating state adjustment intervals. Heating needs for in situ oil shale cracking can be met using either convection (>380 \({}^{\circ}\)C) or combustion (<380 \({}^{\circ}\)C) methods [1]. In addition to the harsh formation environment, persistent high-temperature heating of downhole heaters during operation can cause fatigue damage to the heater itself. According to Section 3.1, raising the injected gas flow rate reduces heat accumulation on the heating rod surface and adjusts the heater's working state. That is, using the large-size heater discussed in this paper, the injection of hot medium from the well at higher temperatures can be achieved, and at the same time, the long-term working time of the downhole heater can be realized by adjusting the control parameters, which indirectly makes the in situ high-efficiency heating of oil shale possible. In the field, the results of numerical simulation may be affected by the heater's material and the downhole high-temperature corrosion conditions, resulting in a degree of influence on the heater's heating performance and a degree of error with the numerical simulation results. As a result, the simulation findings are not without limits.
# Modeling Photovoltaic Potential for Bus Shelters on a City-Scale: A Case Study in Lisbon
## 1 Introduction
### Photovoltaic-Based Urban Applications: Solar Bus Shelters  
Bus shelters are public transport infrastructures that, through innovative solar-based applications, can deliver better services and contribute to increasing the citizens' quality of life, the goal of the smart city model. Today, bus shelters are public places that are designed to afford some protection from the wind, rain or sun while waiting for the bus. However, their function as public places can also be extended to an all-new range of PV-based services such as Wi-Fi access, digital touch-screens (with breaking news, meteorology, timetables or real-time traffic information), recharging stations for mobile devices, digital signage, advertisement LCD monitors, security cameras and, for visually impaired people, tactile labels indicating the lines that stop at the shelter as well as voice announcement systems.  
Improving lighting is also an opportunity, since good lighting allows travelers to feel safe when they arrive on a platform [3]. Most shelter lighting is connected to the electrical grid, usually the same grid that powers street lighting. Nevertheless, solar-powered lighting, generally provided by LEDs, can be installed on those shelters with adequate sunlight.  
Several cities have already invested in solar bus shelters, offering lighting solutions, providing services to help the citizens and the tourists, or allowing for mobile charging while waiting for the next bus (Table 1).
# High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain
## Appendix A  
In the 2-D VFI HFR method proposed in [12], each RF frame is divided into (possibly partially overlapped) blocks of \(\mathrm{M_{B}}\times\mathrm{N_{B}}\) pixels, which are weighted with a Hann's window to limit deleterious edge effects. The same-located blocks of two consecutive frames, \(s_{mn}\) and \(\bar{s}_{mn}\), are obtained at time t and t\({}^{\prime}\) = t + \(\tau\), respectively. If the scatterers inside a block cover a relatively short distance during the interval \(\tau\), \(s_{mn}\) can be considered as a 2-D shifted version of \(s_{mn}\). Hence, the \(\Delta z\) and \(\Delta x\) displacements can be computed from a 1-D discrete Fourier transform (DFT) for the z direction and a 2-D DFT for the x direction, as follows:  
\[\Delta z=\frac{M_{B}}{2\pi f_{m}}\Delta\phi_{f_{m}} \tag{1}\]  
\[\Delta x=\frac{N_{B}}{2\pi f_{n}}\left(\Delta\phi_{f_{m}f_{n}}-\Delta\phi_{f_ {m}}\right) \tag{2}\]  
where \(f_{m}\) and \(f_{n}\) are the spatial frequencies in the z and x directions, respectively, while \(\Delta\phi_{f_{m}}\) and \(\Delta\phi_{f_{m}/f_{n}}\) are the phase shifts in the frequency domain. In this work, displacements were estimated at multiple frequencies to improve the robustness of estimates. Hence, \(k_{f_{m}}\) = 3 frequencies were uniformly distributed on a 20% bandwidth around \(f_{0}\), while \(k_{f_{n}}\) = 10 frequencies were distributed on negative and positive spatial frequencies in a bandwidth between 10% and 40% of the Shannon's spatial frequency.  
Then, the displacements were averaged as follows:  
\[\overline{\Delta z}=\frac{1}{k_{fm}N_{B}}\sum_{n=1}^{N_{B}}\sum_{m=1}^{k_{fm} }\frac{M_{B}}{2\pi f_{m}}\Delta\phi_{f_{m}n} \tag{3}\]  
\[\overline{\Delta x}=\frac{1}{k_{fm}k_{fm}}\sum_{n=1}^{k_{fm}}\sum_{m=1}^{k_{ fm}}\frac{N_{B}}{2\pi f_{n}}\left(\Delta\phi_{f_{m}f_{n}}-\Delta\phi_{f_{m}n}\right) \tag{4}\]  
Finally, the velocity components were given by  
\[\overline{v_{z}}=\frac{\overline{\Delta z}\,\delta x}{\tau}\qquad\overline{v _{x}}=\frac{\overline{\Delta x}\,\delta x}{\tau} \tag{5}\]
# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 5 Experimental Results and Analysis  
The road network relied on for the experiments in this paper is derived from the map of Chengdu City provided by OpenStreetMap (www.openstreetmap.org, accessed on 28 December 2021), with 113,825 road sections and 81,371 nodes. The road network is contracted by edge aggregation to build 161,731 shortcuts. The latitude and longitude boundaries we used for the Chengdu City are [30.2775, 31.0328, 103.6767, 104.5578]. Considering that obtaining OD data of evacuation vehicles on the road network is limited, in our simulation experiments, we use the real-scene KDD CUP 2020 car-hailing datasets provided by [PERSON] (gaia.didichuxing.com, accessed on 23 December 2021), to simulate the route data of evacuation vehicles for partial experimental verification. We scaled up in time granularity (1 h as a statistical unit) to approximate all travelers. The data range from 1 November to 30 November 2016.  
_ODs Dataset_: The order dataset contains 7,062,959 OD records, each with a timestamp and geographic coordinates of the origin and destination. After matching the road network,the shortcuts were aggregated into 14,076 hierarchies to guide the OD semantic partition. The road network is also divided into 35 \(\times\) 40 (1400) geographical grids and the volume of ODs within the grids is aggregated. Since the end time of OD orders is not fixed, we take 1 h as the time step for prediction. For time-out orders are treated as completed in 1 h.  
_Trajectories Dataset_: The total number of trajectories is 1,096,618,419 with each record containing a time stamp, vehicle ID, and geographical coordinates of the location. The trajectory points are collected at intervals of 2-4 s. The number of sections after mapping to the road network is 4036, leaving 2239 after further contraction.  
_Road Section Impedance_: The road section impedance can be determined based on the relationship between evacuation time and traffic load. In this paper, the BPR function is  
Figure 4: Equilibrium mapping based on adaptive multi-graph fusion.  
used as \(T(x_{r},C_{r})=t_{r}^{0}(1+a\frac{x_{r}}{C_{r}})^{\beta}\), where \(x_{r}\) is the traffic load on road section \(r\), \(C_{r}\) is the capacity, and \(t_{0}^{r}\) is the free-flow evacuation time. The parameters \(\alpha\) and \(\beta\) are taken as 0.15 and 4.5, respectively.  
We choose three kinds of prediction errors to evaluate the performance of the model, MAE, RMSE, and accuracy, as in Equation (16):  
\[\begin{split} MAE=\frac{1}{m}\sum\limits_{t=1}^{m}|y_{t}-x_{t}|,\\ RMSE=\sqrt{1/m}\sum\limits_{t=1}^{m}(y_{t}-x_{t})^{2},\\ Accuracy=1-\frac{|y_{t}-x_{t}|}{x_{t}}.\end{split} \tag{16}\]  
where \(y_{t}\) denotes the predicted value and \(x_{t}\) denotes the true value based on the OD volume, background load, or assigned traffic. \(m\) denotes the prediction period.
## 4 Discussion
### Limitations and Future Perspectives  
The main limitation of the study was the very large data variability potentially due to different factors. The heterogeneity of the patient population, in terms of the time since stroke, stroke location, stroke etiology, MI ability, and UE motor impairment, is a factor to consider in the variability observed with the PAS interventions [47; 64]. Even if MI can be applied regardless of these characteristics [37], PAS may be more suitable for patients with small, subcortical lesions [8; 9] and with better motor performance [10]. Furthermore, functional benefits were observed when PAS was repeated over multiple sessions [17] and mainly when the ISI was individually defined, as performed in SCI individuals [12; 13; 15]. This needs to be further investigated in stroke participants.  
In addition, the lesions to the corticospinal tract (CST) due to the stroke make the MEP measurements difficult and unreliable. Many of the patients included in _MIPAS_ had a high motor threshold near or sometimes higher than the maximum TMS stimulator output, making it impossible to measure input-output (I/O) curves. Additionally, even if we verified the ability for MEP to increase, it was sometimes difficult to ensure that the selected intensity was in the linear portion of the I/O curve, which introduces greater variability in the sensitivity of MEP response.  
Significant CSE facilitation was observed 30 min after ShamPAS/MI, but not after the other two interventions. Even if we previously demonstrated that muscle ES alone for 30 min did not significantly affect CSE in stroke [8], we cannot rule out that it is the ES, present in ShamPAS, combined with MI that led to the significant and less variable CSE facilitation observed.  
All three interventions demonstrated beneficial effects on paretic hand motor function, but the results might not be related to the only effect of the interventions. The repetition of active wrist extension induces a generic learning effect and/or an increase in unspecific neurovegetative excitation. However, given that ShamPAS/MI induced the least important and lasting changes in aROM suggests that other mechanisms may be involved in the motor changes observed. Unlike the CSE results, the motor behavioral results are not in favor of MI size effects overshadowing the influence of PAS, but suggest different mechanisms of action between PAS and MI.  
Finally, the large variability of the data, associated also with the small sample size is responsible for the low study power, which limits the generalization of the results. After a second negative trial, we can say that even if the PAS mechanism is attractive, it might not be suitable for all stroke individuals and is not ready to be implemented in poststroke rehabilitation. Similarly, the difficulties in ensuring the methodological expectations of MEP measures in poststroke participants and the different results observed between the CSE and motor behavioral outcomes emphasize the need to not directly associate both outcomes. All the motor behavioral outcome changes in poststroke participants cannot be explained by changes in CSE alone.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 3 The Proposed DSAH Framework
### Complexity Analysis  
For each iteration, the time complexity is analyzed as follows. The time computational complexity of **B** is \(\mathcal{O}(k^{2}c+kc^{2}+(q+c+k)kn)\), **V** is \(\mathcal{O}(kc^{2}+k^{2}c+k^{2}n)\), **R** is \(\mathcal{O}((k^{2}+kc)n+k^{2}c+kc^{2}+c^{3})\), \(\mathbf{P}_{1}\) and \(\mathbf{P}_{2}\) are all \(\mathcal{O}(q^{3}+kq^{2}+(kq+kc+qc+q^{2})n)\). As \(\{k,c,q\}\ll n\), the training complexity is \(\mathcal{O}((kq+k^{2}+kc+q^{2})n)\). Given the iteration \(T\), the overall training complexity for DSAH is \(\mathcal{O}((kq+k^{2}+kc+q^{2})nT)\), where \(T\ll\{k,c,q\}\) is very small, which is linear to the training set size. Therefore, DSAH is highly scalable for large-scale cross-modal retrieval tasks.
# An Innovative Parabolic Trough Collector Design with a Twin-Cavity Receiver
###### Abstract  
An innovative parabolic trough concentrator coupled to a twin cavity receiver (PTC-TC) in evacuated tube conditions is investigated thermally and optically. The suggested design is compared with a PTC design with a flat receiver (PTC-F) in order to evaluate the efficiency of the proposed configuration. In the very first stages of the study, the optical efficiency was calculated for both collectors, and the optimum design was determined in the PTC-TC case. Then a mass flow rate independency procedure was conducted to ensure accurate results and to make a suitable comparison. The collectors were examined in a wide range of inlet temperatures ranging from 20 \({}^{\circ}\)C to 200 \({}^{\circ}\)C, and the thermal performance was calculated. Through the comparison process, it was revealed that the proposed collector appears to have higher thermal performance than the typical collector. In particular, there was a mean enhancement of approximately 8%, while the minimum enhancement was found to be greater than 5%. The simulation results regarding both configurations were verified through two models based on theoretical equations. In both geometries, the mean deviations in the verification procedure were lower than 5.6% in both the Darcy friction factor and the Nusselt number. The design and the simulations were conducted with the SolidWorks Flow Simulation tool.  
optical efficiency; thermal efficiency; innovative solar design; PTC; cavity +
Footnote †: journal: Applied Sciences