esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Isometric Mid-Thigh Pull  
Participants completed a general warm-up, consisting of 3 min of cycling and 10 repetitions of squats, lungs, and glute-bridges. Subsequently, an isometric-specific warm-up was conducted, comprising a 5 s of IMTP bout at a self-directed position at 50%, a 3 s bout at 75%, and a 3 s bout at 90% of maximal effort with 1 min rest between each bout [15]. After the warm-up, participants performed 3 maximal-effort isometric pulls separated by 3 min of recovery. The IMTP was performed with a custom-designed power rack that allows fixation of the steel bar height, with the participants standing on an 80 \(\times\) 80 cm force plate (WP800, Applied Measurements Ltd. Co., Aldermaston, UK, operating at 1000 Hz). At the start of each trial, participants were positioned in the second-pull phase of the clean, with knee angles set at 141\({}^{\circ}\)\(\pm\) 4\({}^{\circ}\) and hip angles set at 138\({}^{\circ}\)\(\pm\) 2\({}^{\circ}\)[15]. These angles were verified prior to each trial using a hand-held goniometer. To standardize grip strength, participants utilized lifting straps, and both grip width and foot positioning were standardized across participants [25]. Participants were instructed to maintain a low, steady baseline force at the beginning of each trial in order to avoid precontraction [16] and then directed to exert maximal pulling force for a period of 4 s, initiated by a countdown of \"3, 2, 1, Go!\" [14]. The duration of the collection period for each trial was established at 12 s, with a baseline measurement obtained during the 3 s countdown preceding the initiation of the pull [15]. Verbal encouragement was provided during each trial. Data from the force plate were collected at a sampling rate of 1000 Hz (Kyowa sensor interface PCD-320A, Chofu, Japan). The signal was then processed using a fourth-order, zero-lag Butterworth low-pass digital filter with a cutoff frequency of 20 Hz. From the force-time data, the highest force value achieved during the 4 s best trial, minus the participant's body weight in Newtons, was reported as the IMTPF. The contraction onset threshold was established based on five Standard Deviations (SD) above participant's body weight [26]. The reliability of the IMTP test to measure the maximal force has been examined previously and provided a very high intraclass correlation coefficient (ICC) (ranging from 0.92 to 0.99 with coefficients of variation (CV) >5%) [27].
## 4 Experimental Results
Figure 9: Experimental results from the proposed method: (**a**) source voltage (\(v_{s}\)), source current (\(i_{s}\)), capacitor voltages (\(v_{c1}\) and \(v_{c2}\)); (**b**) FFT spectrum of source current (\(i_{s}\)); and (**c**) line-to-line converter voltage (\(v_{ab}\)) and ac component of capacitor voltages (\(v_{c1}\) and \(v_{c2}\)). almost same speed, as shown in Figure 11. Moreover, Figure 11 clearly illustrates that the NP voltage imbalance cannot be reduced if only the difference-mode reference voltage is used. Figures 12 and 13 show experimental waveforms of step changes of the load resistance from 200 \(\Omega\) to 100 \(\Omega\) and the dc load voltage from 150 V to 120 V obtained by the two methods. It is seen that the proposed method achieved dynamic responses as quickly as the conventional method. Table 4 shows the calculation time of the conventional and the proposed methods to compare the computational complexity. The numbers of clocks of the conventional and the proposed methods were measured in the DSP board in the experimental setup and they were converted to the execution time  
Figure 11: Experimental results for capacitor voltages’ (\(v_{c1}\) and \(v_{c2}\)) correction from an unbalanced voltage condition obtained by: (**a**) the conventional method; (**b**) the proposed method; and (**c**) using only the difference-mode reference voltage without the common-mode voltage term. Figure 12: Experimental results for capacitor voltages (\(v_{c1}\) and \(v_{c2}\)) and source current (\(i_{s}\)) with a step change of the load resistor from 200 \(\Omega\) to 100 \(\Omega\) obtained by: (**a**) the conventional method; (**b**) the proposed method. Figure 13: Experimental results for capacitor voltages (\(v_{c1}\) and \(v_{c2}\)) and source current (\(i_{s}\)) with a step change of dc voltage from 150 V to 120 V obtained by: (**a**) the conventional method; (**b**) the proposed method. required to perform the two control algorithms. As can be seen from Table 4, the proposed method yielded almost the same calculation time as the conventional method. Table 5 summarizes the comparison between the conventional and proposed methods obtained by the above analysis and experimental results. Apparently, the proposed method did not require the tedious weighting factor selection procedure, which allowed time saving. The experimental results provided satisfactory performance under steady-state operation with slightly better source current THD and good capacitor voltages' balancing. Additionally, the dynamic performance was guaranteed as fast as the conventional method. Meanwhile, the difference in computational time was negligible.
## 3 Results
### Polydispersity Index (PDI) of the SBO-NLCs  
The predicted and actual PDI value of center point STDs was 0.26. A low PDI value of 0.16 was observed in the combination of 2.5% wax, 2.5% surfactant, and 10% PEG400 in the  
Figure 1: The response surface plot presents the effects of wax, surfactant, and percentage of PEG in the surfactant on the size of SBO-NLCs. **(a)** Interaction of wax and surfactant. **(b)** Interaction of surfactant and percentage of PEG400 in the surfactant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Respenses** & **Models** & \begin{tabular}{c} **Model** \\ _(p-Value)_ \\ \end{tabular} & \
\begin{tabular}{c} **Lack of Fit** \\ _(p-Value)_ \\ \end{tabular} & **R\({}^{2}\)** & **Adjusted R\({}^{2}\)** & **Predicted R\({}^{2}\)** &
\begin{tabular}{c} **Adequate** \\ **Precision** \\ \end{tabular} \\ \hline Size & Reduced quadratic & 0.0010 & 0.6268 & 0.8551 & 0.7681 & 0.5924 & 10.9504 \\ PDI & Reduced 2 FI & 0.0017 & 0.1280 & 0.8487 & 0.7646 & 0.5934 & 12.0536 \\ ZP & Reduced cubic & 0.0085 & 0.1503 & 0.8505 & 0.7196 & 0.5654 & 7.1890 \\ EE & Reduced cubic & 0.0019 & 0.9703 & 0.9902 & 0.9609 & 0.9587 & 19.9901 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of variance for the size, polydispersity index (PDI), zeta potential (ZP), and entrapment efficiency (EE).  
surfactant (Table 2). The reduced 2 FI model was adapted for the PDI of SBO-NLCs. The model was significant (\(p\) = 0.0017), and the lack-of-fit value was non-significant (\(p\) = 0.1280). Acceptable ranges of adjusted R\({}^{2}\) (0.7646) and predicted R\({}^{2}\) (0.5934) values were detected in the reduced 2 FI model (Table 3). The results recommended that the reduced 2 FI model was appropriate for predicting the PDI of SBO-NLCs. The CCD-generated reduced 2 FI model equation for the PDI of SBO-NLCs was as follows (Formula (4)):  
\[\mathrm{PDI}=-0.0644+0.0269\mathrm{A}+0.0669\mathrm{B}+0.0081\mathrm{C}-0.004 3\mathrm{AB}-0.0018\mathrm{BC} \tag{4}\]  
where A = the percentage of wax in the formulation (2.5 to 7.5%), B = the percentage of surfactant in the formulation (2.5 to 7.5%), and C = the percentage of PEG400 in the surfactant (10 to 30%).  
The concentrations of surfactant, wax, and PEG400 in the surfactant were the influencing factors on the PDI of the NLCs. Lower concentrations of surfactant and wax and a lower percentage of PEG in the surfactant produced SBO-NLCs with lower PDI values. The influences of variable factors on the PDI of the NLCs are represented in Figure 2.
# State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
## 3 Model Order Reduction by Combination of the State Residualisation and Kron Reduction Technique  
### State Residualisation Technique  
#### 3.1.1 State Residualisation Principle  
A power system can be modelled as a nonlinear differential-algebraic system of equations (DAE) as (41).  
\[\begin{cases}\frac{dx_{d}}{dt}=f(x_{d},x_{a},u)\\ 0=g\big{(}x_{d},x_{a},u\big{)}\,\ x_{d}\in\mathbb{R}^{Nd},x_{a}\in\mathbb{R}^{Nd},u \in\mathbb{R}^{p},y\in\mathbb{R}^{q},\\ y=h\big{(}x_{d},x_{a},u\big{)}\end{cases} \tag{41}\]  
Figure 2: (**a**) Transformer model, (**b**) Pi-line model, and (**c**) RL-load model.  
where \(x_{d}\), \(x_{a}\), \(u\) and \(y\) represent the algebraic input and output variables, and \(N_{d}\), \(N_{a}\), \(p\) and \(q\) are the related orders.  
The residualisation of a state variable consists of changing the state variable into an algebraic variable by neglecting its derivative in the associated differential equation, transforming it into an algebraic equation [9]. It represents a well-known process applied in the phasor approximation to state variables modelling power lines, and is used by most transient simulation programs [30].  
The reduced system obtained by state residualisation can be written as in (42).  
\[\left\{\begin{array}{c}E\frac{dx_{d}}{dt}=f(x_{d},x_{a},u)\\ 0=g(x_{d},x_{a},u)\\ y=h(x_{d},x_{a},u)\\ E=diag(\delta_{i}),\delta_{i}=\{0,1\},\forall i\end{array}\right.\qquad x_{d} \in\mathbb{R}^{N_{d}},x_{a}\in\mathbb{R}^{N_{a}},u\in\mathbb{R}^{p},y\in \mathbb{R}^{q}. \tag{42}\]  
The diagonal matrix \(E\) is called the residualisation matrix, consisting of 1 or 0 on its diagonal. Each diagonal element \(E(i,i)\) indicates whether the state variable \(x_{d}(i)\) is residualised or not. If \(E(i,i)=0\), the state variable \(x_{d}(i)\) is residualised, and otherwise \(E(i,i)=1\). The size of the reduced system is given by \(tr(E)\).  
The objective of state residualisation is to choose \(E\), providing a trade-off between the order of the reduced model and the expected modelling error.  
It is seen that the variables and parameters of the full and reduced models are exactly the same, which ensures that the steady state values of the full and reduced models are the same, as they are computed with the same system. It is also straightforward to directly consider the reduced system for analysis, or tuning and designing of controllers. Finally, state residualisation can be implemented in classical DAE solvers, as it involves substituting some derivative terms by 0.  
A method to achieve state residualisation, consisting of state categorisation and modal state residualisation, as proposed by [25], is summarised in Figure 3.
## 3 Results and Discussion
Figure 6: Linear correlation between Se, Pb, Sb, and As (**A–C**), between Fe and Mn (D), as well as between La and Ce (**E**), and air mass back-trajectories arriving at the Ring of Fire station on 26 September 2015. Each color in (**F**) indicates an air mass back-trajectory cluster and time percentage of that air mass originating from that cluster by using 24 air mass back-trajectories. and median 0.7. The wide range of the ratios indicates mixed sources of La and Ce in this area. The median ratio 0.7 aligns well with the median 0.69 observed at six air monitoring stations across Canada [51]. The highest La/Ce ratio of 1.5 in the Ring of Fire area was observed on September 26, 2015. Levels of other metals were elevated in this sample as well (e.g., Mn, As, Se, Sb, and Pb). Transportation and transformation in the atmosphere do not change the La/Ce ratio given their similar physical-chemical properties. Four clusters of back-trajectories for this 24 h sampling period suggest that elevated trace elements likely originated from long-range transport from the Great Lakes region (Figure 6F). Figure 7 shows CPF plots for PM\({}_{2.5}\) and select elements to identify directions of potential sources at the Ring of Fire station (i.e., the receptor) when their concentrations were above the MDLs by using the entire dataset. Potential sources of these pollutants appeared similar and originated from the southwest and southern directions. No potential sources were identified from the northwest where the proposed Eagle's Nest mine site is located. Given few local emission sources in the Far North, these pollutants could be related to long-range atmospheric transport from low latitudes where there are industrial and traffic emissions.
## 2 Materials and Methods
### Procedure  
At first, the physical environment was modeled, and performance metrics were calculated. The simulation results were compared against a limited and randomized set of illuminance measurements in the real building to check if they were in the same range.  
The results showed a difference between simulated and real values of \(20\pm 11\%\), which is within normal variability levels for simulated objects [20]. Secondly, using an online questionnaire, occupant characteristics were recorded, and their feedback regarding visual comfort was gathered. Finally, user feedback and simulation output were compared. This comparison was performed to investigate how metrics measured by simulation tools (to assess visual comfort) correlate with actual visual comfort perceived by the users, and to answer the research questions. The first sample of the population (\(N=15\)) filled out the questionnaire in October, and the second sample of the population (\(N=46\)) filled it out in June. For \(N=10\) of these people, all working in one wing of the survey location, the questionnaire was a repetition. In total, \(N=51\) unique people participated in the study, which means, in total, \(N=61\) complete survey results were gathered.  
An overview of the three research questions and the results of the comparison between POE and the performance metrics (PM) for different groups of participants at different data collection moments is illustrated in Figure 2.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 2 Related Work
### Low-Light Detection
#### 2.2.3 Different Methods Applied to Low-Illuminated Detection  
The authors of [57] proposed a method of domain adaptation for object detection in a low-light situation. Pretrained models are merged into this method in different domains using glue layers and a generative model, which feeds latent features to the glue layers to train them without an additional dataset. The authors of [58] suggest an active object detection approach and brightness control strategy based on reinforcement learning. Without having to retrain the detector, low-quality photos can be enhanced into high-quality images with the aid of the pretrained models, and overall performances are increased. In [59], a hyperbolic tangent curve is used to first map the image brightness to the desired level. Secondly, the YCbCr color space unsharp filter block matching and 3-D filtering algorithms are created. Finally, the nighttime surveillance task is concluded with pedestrian detection using a convolutional neural network model. With only modest processing resources and no supervised training, the authors of [60] described a Flash-No-Flash (FNF)-controlled illumination acquisition methodology that enables and impacts reliable object detection. The technique depends on the simultaneous acquisition of two images, one with and one without strong artificial illumination (flash/no flash). The authors of [61] provided a dataset of unprocessed short-exposure low-light photos together with comparable long-exposure reference images to aid in the development of learning-based pipelines for low-light image processing. They created a pipeline for processing low-light pictures based on end-to-end training of a fully convolutional network that works directly with the raw sensor input using the dataset that was presented. However, the performance of these existing methods in truly dark environments is not very satisfactory.
## 5 Computational Framework and Result
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **DE** & **CU** & **VaNAS** & **SDR** & **TTM** & **MF** \\ \hline Lingo & 0.000 & 0.090 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline DE & & 0.000 & 0.000 & 0.002 & 0.003 & 0.836 \\ \hline CU & & & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline VaNAS & & & & 0.000 & 0.000 & 0.005 \\ \hline SDR & & & & & 0.754 & 0.379 \\ \hline TTM & & & & & & 0.468 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Statistical test of objective function for large test instances. The computation results in Tables 11 and 12 illustrate that VaNAS performed better than other proposed and traditional methods did. VaNAS performed as well as the exact method did, and also showed the highest performance of solution stability over other algorithms as shown in Table 11. While others were significantly different from the exact method as seen in the statistical test result (Table 12), DE, CU, SDR, TTM, and MF were an improvement from the exact method by 14.59%, 4.37%, 11.19%, 11.04%, and 11.24%, respectively. However, VaNAS was at 19.55%, which means that VaNAS obtained the solution at 19.55% lower cost than that of the solution from Lingo. Lingo consumed 2880 min computation time, and VaNAS used only 30 min. The next experiment was tested with a large random dataset. This group of test instances was composed of 12 test instances, the number of jobs was randomly selected to be from 80 to 134, and the number of machines was set from 5 to 8 (L-1 to L-12). In this set of test instances, we included the case study, which had 201 jobs and 11 machines (C-1). In this case, Lingo was executed for 72 h to find the lower-bound solution; then, the termination condition as the computation time of all proposed heuristics was 45 min. The simulation was executed five times; the best solution and the maximum error of large instances are shown in Table 13 and the statistical results are shown in Table 14.  
The computation results in Tables 13 and 14 show that VaNAS performed better than other proposed and traditional methods did. The performance of VaNAS is similar to the exact method, while others were significantly different from the exact methods. DE, CU, SDR, TTM, and MF were 9.88%, 1.42%, 7.89%, 8.01%, and 9.33%, respectively, different from the exact method. In addition, VaNAS was 16.35% different from the exact method, which means it could find 16.35% lower cost than how much Lingo could. The maximum error of each method indicates that VaNAS outperformed other methods in terms of solution stability. In addition, Lingo required 2880 min of computation time while VaNAS only required 45 min.
## 5 Numerical Results and Discussion
In both Figures 3 and 4, it can be seen that the corresponding performance of the scheme that always selects the link that offers the maximum received SNR is always equal to the one of the proposed scheme, verifying the discussion that followed Figure 2. \begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Definition** & **Value** \\ \hline \(\sigma_{7}^{2}\) & Variance of the AWGN noise & \(4.435\cdot 10^{-28}\) \\ \(\eta\) & Optical to electrical & \\  & conversion coefficient & 0.8 \\ \(\zeta_{i}\) & Pointing error coefficient & 13.07 \\ \(\theta\) & Zenith angle & 65\({}^{\circ}\) \\ \hline \(w\) & Wind velocity & 4 Lm/s \\ \(W_{0}\) & Beam radius at the transmitter & 2 cm \\ \hline \(F_{0}\) & Phase front radius of & \(\infty\) \\  & curvature of the beam & \(2.5,2.8\) \\ \hline \(m_{1},m_{2}\) & Small-scale fading shaping & \\  & parameters & \(2.5,2.8\) \\ \hline \(\alpha_{1},\alpha_{2}\) & Shadowing shaping & \(1.2,1.4\) \\ \hline \(v\) & Path loss factor & \(2.1\) \\ \hline \(P_{t}\) & Transmit power for RF & \\  & communications & 20 dBm \\ \hline \(N_{0}\) & Noise power & \(-97.8\) dBm \\ \hline \(G_{T_{r}}\) & RF transmit antenna gain & 20 dB \\ \hline \(G_{R_{r}}\) & RF receive antenna gain & 20 dB \\ \hline \(\lambda_{r}\) & RF links wavelength & 0.158 m \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 3: OP vs. average SNR for different wind velocities. The performance improves as the wind velocity decreases. Figure 2: Proposed scheme’s OP performance and complexity analysis. (**a**) Outage probability vs. switching threshold, (**b**) number of path estimations vs. switching threshold, (**c**) switching probability vs. switching threshold. Finally, in Figure 5, the impact of small- and large-scale fading, which are controlled by parameters \(m,\alpha\), respectively, on the OP and SP has been evaluated. In these figures, the important difference that exists between the two limiting scenarios, i.e., the one with light fading/shadowing conditions (\(m=3,\alpha=1\)) and the one with severe fading/shadowing (\(m=1,\alpha=3\)), is shown. For the other scenarios under investigation, it seems that when light fading and severe shadowing exists, i.e., \(m=3,\alpha=1\), the performance is better for lower values of the average SNR, as compared to the reverse scenario. As far as the SP is concerned, it is depicted that for all scenarios investigated, except the one with good fading/shadowing conditions, as the average SNR increases the performances become equal. Figure 4: OP vs. average SNR for different elevation angles. The performance improves as \(\theta\) decreases. Figure 5: Performance of the proposed scheme for different propagation conditions for the RF links: (**a**) Outage probability vs. average SNR, and (**b**) switching probability vs. the average SNR.
## 2 System Model
### Geometry Model Based on Geocentric Angle
Figure 2: The architecture of the NGEO MSS comprises several key components: a GEO satellite (denoted by the black point G), a GEO user terminal (UT) represented by the green point D, a NGEO UT depicted by the orange point Q, and \(k\)-th tier NGEO satellites denoted by the red and purple points M, N, and P. The red points denote satellites positioned within the visible area, while the purple point corresponds to a satellite outside the scope of this work, residing in a non-visible region. Similar to the approach in [18], NGEO satellites are restricted from communicating within the exclusion zone. Let \(\alpha_{U},\alpha_{D}\) denote the exclusion angle of uplink and downlink, respectively. The corresponding off-axis angle \(\theta_{0}^{l}\), \(l\in\{U,D\}\) of the axis \(OG\) can then be expressed as  
\[\theta_{0}^{l}=\alpha_{l}+\arcsin\frac{r_{E}\sin\alpha_{l}}{r_{E}+H_{k}}. \tag{6}\]  
Without loss of generality, such as the geocentric angle \(\angle POQ\) depicted as \(\hat{\vartheta}_{k}\) in Figure 2, this could alternatively be denoted as  
\[ \begin{split}\hat{\vartheta}_{k}=\arccos\frac{\overline{OG} \overline{OG}}{|\overline{OG}||\overline{OG}|}\\ =\arccos[\sin\theta_{k}\sin\theta_{E}\cos(\zeta_{k}-\zeta_{E}) \\ +\cos\hat{\vartheta}_{k}\cos\vartheta_{E}],\end{split}  \tag{7}\]  
where \(\theta_{E}\) and \(\theta_{k}\) represent the off-axis angles of the axis \(OG\) for the UT and a satellite of \(k\)-th tier, respectively, with rotation angles \(\zeta_{E}\) and \(\zeta_{k}\). Based on (7), the representation of a spherical surface can be succinctly conveyed through the use of the geocentric angle.