esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### The Effectiveness of the Differential Evolution Optimization Method  
It is difficult to optimize numerous eco-physiological parameters because of the strong correlations among them. Previous studies usually used a manual tuning method to calibrate the model based on EC flux data [23]. When there are so many parameters and vegetation types, it is not suitable to use the manual method. So, some scholars have used the PEST and Biome-BGC coupling method to optimize the sensitive parameters of the model [44]. But PEST has the disadvantages of easily falling into local optima and low efficiency of para38 meter optimization. If we used the DE and Biome-BGC coupling method to calibrate the model, we were able to globally optimize over 20 parameters with a fast convergence rate at the same time.  
We could determine that the optimization effect of Biome-BGC-DE was very great through the validation results of this study. The simulated GPP values of each typical vegetation type were close to the observed GPP values in trend and size. For two crops a year, previous studies usually simulated wheat and maize separately because of the lack of a crop rotation mechanism in the Biome-BGC model [43]. By contrast, this study adopted a method of separately simulating wheat and maize and then overlaying the simulation results. This study achieved an accurate simulation for two crops a year by optimizing the parameters of wheat and maize with DE (R\({}^{2}\) = 0.90, RMSE = 38.15 g C m\({}^{-2}\)). In conclusion, if we have enough EC flux data and combine with the DE parameter optimization method, we can simulate the actual water and carbon fluxes of typical vegetation more accurately.
## 2 Materials and Methods
### Cyclic Loading Test  
Figure 2 is the schematic representation of four implant abutment designs. The specimens were retained on the implant dies by friction and could only be removed by crown remover. The stability of the prosthesis was enhanced by the anti-rotation surface design of the dental implant abutment. The dimension of SA100 specimen was designed to fit the implant abutment with thickness of 0.5 mm over occlusal surface, diameter of 5 mm at base, and diameter of 3.5 mm at top. The reduction began 2 mm from the bottom to preserve retentive property, and tapered up to the desired occlusal surface area. Reduced volume was replaced with zirconia. Fifteen specimens in each group were tested vertically at 5 Hz and 300 N in a servo-hydraulic testing machine (Instron M8810, Instron Ltd. Norwood, MA, USA) until the specimen fractured or the test machine automatically stopped after 30,000 cyclic counts. To ensure that a consistent force was exerted on the specimen, the computer program had a 2-stage control to emulate the masticatory cycle of human jaw. The first stage, which simulated natural occlusal displacement, was position control. This stage ensured constant stroke distance between the load cell and specimen. The second stage was load control. This stage ensured a consistent force exerted by the load cell to the specimen remained constant throughout the test cycle.  
The indentation stress-strain relation is well defined by the classic Hertzian theory for ideally elastic, homogeneous bulk materials with [PERSON]'s modulus E and Poisson's ratio \(\
u\). The linear form of the Hertzian solution [28; 29] is expressed as:  
\[a=\sqrt{rd} \tag{2}\]  
\[p=3F/2\pi a^{2} \tag{3}\]  
where \(a\) is the radius of the contact area, \(r\) is the radius of indent and \(d\) the depth, \(F\) is the loading, and \(p\) is the maximum contact pressure.
# Modeling and Analysis of a Generic Internal Cargo Airdrop System for a Tandem Helicopter
## 6 Conclusions  
In this article, a generic internal cargo airflow model for a tandem rotor helicopter was proposed, capable of modeling helicopter flight operation loading an internal cargo and modeling helicopter flight operation in the process of internal cargo airdrops. Through the two cases, a few of the abilities of the model were shown. Stabilities of the tandem rotor helicopter in the process of internal airdrops were simulated and analyzed.  
Based on the present results, some meaningful conclusions are drawn as follows:  
1. Flight operation loading an internal cargo mainly evidently affects the trim positions of the thrust and longitudinal cyclic controls, especially flying at a large flight velocity. It has a small effect on the trim positions of the lateral cyclic and pedal controls. It also has a slight effect on the trim positions of the pitching and rolling attitude angles. Thus, the tandem rotor helicopter has a good attitude maintaining ability in the whole flight velocity envelope when the helicopter conducts airdrops.
2. The increase in flight velocity will constantly decrease the helicopter pitching attitude angle, thus increases the total airflow time and decreases the backward moving velocity of the cargo. Overall, considering the actual influence of the high speed of the moving cargo on the worker's safety tandem rotor helicopter flying at a velocity between 80 and 120 knots might be acceptable in the practical application.
3. The airdrop operation will also significantly affect the trim positions of the thrust and longitudinal cyclic controls. The increase in flight velocity will evidently increase the influence of the airdrop operation on the thrust control and the pitching attitude, and evidently decrease the influence of the airdrop operation on the lateral cyclic and pedal controls. In addition, the airdrop affects the rolling attitude a little.
4. The airdrop operation will not reduce the stability of the heave-pitch mode, but reduce the stabilities of the longitudinal velocity and pitch mode. It will also evidently reduce the stabilities of both the roll mode and the spiral mode, especially as the flight velocity increases. The stabilities of both the longitudinal and lateral periodic modes are continuously decreased during the backward movement of the cargo.  
Conceptualization, Y.C. and G.L.; Data curation, G.L. and M.W.; Formal analysis, G.L. and M.W.; Funding acquisition, Y.C. and G.L.; Investigation, G.L. and M.W.; Methodology, G.L. and M.W.; Resources, G.L.; Software, G.L.; Supervision, Y.C. and G.L.; Validation, Y.C. and G.L.; Writing--original draft, G.L.; Writing--review & editing, Y.C. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study
## 7 Results
The results showed meaningful associations within individual EF domains: the RI accuracy and RI time: \(r=-0.2\), \(p=0.10\), and the HLWM accuracy and HLWM time: \(r=-0.17\), \(p=0.16\); these results highlight the internal convergence between the accuracy and speed within tasks. Additionally, moderate inter-domain correlations were observed, such as the RI time and IC time (\(r=0.47\), \(p<0.001\)) and IC accuracy and CF accuracy (\(r=0.21\), \(p=0.08\)), suggesting an overlap in the cognitive processing speed across inhibition and interference control and in accuracy across interference control and cognitive flexibility. Conversely, weak or null associations were found between some planning-related measures and other EF domains (P time and CF accuracy: \(r=0.04\), \(p=0.75\)), possibly reflecting differences in the task structure, measurement sensitivity, or individual variability. Overall, the pattern of correlations supports the construct validity of the TeleFE battery, indicating that while tasks within the same domain are related, they also capture distinct cognitive components, aligning with a multidimensional model of executive functioning. Figure 5: Box plots comparing the group with ADHD and the TD group on Planning (P), Recall (R) and Map (M) in accuracy and time. The following text describes specific measures emerging from individual tasks. _Go/NoGo task._ Considering the specific result for this task, a significant difference emerged in Block 2, both in the response time for the wrong answer (\(\chi^{2}\) = 10.25, \(p\) \(<\) 0.001, \(\xi\), = 0.155; see Table 6) and in commission errors (\(\chi^{2}\) = 6.30, \(p\) = 0.012, \(\xi\) = 0.10; see Table 7); the young adults with ADHD showed a longer reaction time (273.58 ms vs. 90.82 ms) and more commission errors (1.03 vs. 0.26). _Flanker task._ In this task, a significant difference in the correct responses (CR) emerged between the two groups in the congruent and incongruent trials of Block 2, specifically the number of correct responses in both congruent and incongruent trials in the peripheral target condition measuring interference control (respectively, \(\chi^{2}\) = 8.48, \(p\) = 0.004, \(\xi\) = 0.129 and \(\chi^{2}\) = 3.89, \(p\) = 0.049, \(\xi\) = 0.059; see Table 8). In both cases, the group of young adults with ADHD had a worse performance compared to their TD peers (respectively, 96.81 vs. 100 and 92.12 vs. 95.58). A significant difference also emerged in the CR of congruent trials of Block 3--the mixed-rule condition measuring cognitive flexibility (\(\chi^{2}\) = 8.25, \(p\) = 0.004, \(\xi\) = 0.123, TD group = 96.76 vs. ADHD group = 94.50; see Table 8). _N-back._ In the working memory task, the difference emerged in the 'WM-2 RT' variables of Block 6, corresponding to the speed in working memory updating under a high cognitive load with letters as the stimuli (\(\chi^{2}\) = 6.01, \(p\) = 0.014, \(\xi\) = 0.091; see Table 9). In this case, the group of young adults with ADHD showed a slower reaction time compared to their TD peers (692.64 ms vs. 579.44 ms).
# A Review of Ghost Imaging via Sparsity Constraints
## 4 Discussion and Future Work  
Before the work of [PERSON] [1; 2; 3; 4], most researchers viewed the fluctuations of light as uninteresting \"noise\", and the statistical methods is a \"second-class\" approach in Optics as pointed out by [PERSON] [121]. However, as a computational imaging technology based on high-order correlation of light, GISC not only may be applied to many applications including remote sensing, scattering imaging, and X-ray diffraction imaging, but also opens a way of combining optical imaging technology with modern information sciences to maximize the information gain. The researches of GISC are of course far from closed. In GI, both signals in the reference and the test arms are detected in one measurement. While in the present optimal estimation framework of GISC, the fidelity term is approximated by the conventional \(\min f_{1}\left\{Y-AO\right\}\) model, where only one signal \(y_{i}\in Y\) is considered in one measurement once the system is calibrated. A self-consistent and more accurate optimal estimation framework for GISC, together with the developments of more powerful techniques of light field modulation, light field fluctuation detection and the electronics specially designed for GISC, are still the burning challenges to demonstrate ghost imaging applications with image quality outshine conventional imaging methods in the near future.  
[PERSON] contributed to the organization of the manuscript. Materials in Sections 2 and 3 were prepared by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. [PERSON], [PERSON] and [PERSON] drafted the manuscript. All authors revised and approved the submitted manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2017 YFB0503303), the National Natural Science Foundation of China (No. 11627811), and the Hi-Tech Research and Development Program of China in Projects (No. 2013 AA122902, No. 2013 AA122901, No. 2011 AA120102 and No. 2011 AA120101).  
The authors declare no conflict of interest.  
Figure 9: Photographs and reconstructed images of two objects: (**a**) the photograph of a double-slit, (**b**) the photograph of a starfish, (**c,d**) the reconstructed images of the double-slit and starfish, respectively.
## 2 Technical Mining Fundamentals
### Effect of Financial Analysis on Stope Production  
Detailed diluted stope financial analysis is essential for understanding the economic feasibility of underground production. The life of the mine, production unit cost, and net present value of the project are impacted by dilution. Financial analysis and the profitability of a stope are determined by several parameters, including the stope tonnage, diluted grade, recovery, commodity price, and related operational costs. The net profit per stope is defined using the formula below [26]:  
\[\text{Net Profit = (Ore Tonnage \times Diluted Grade \times Recovery \times Metal Price \times Payable Factor) - Total Operating Cost} \tag{1}\]  
The parameters of this equation have a specific role:  
\[\text{Ore Tonnage: Total tonnage of material mined after applied for mining dilution and loss.}\]  
Diluted Grade: Average diluted grade of the extracted material.  
\[\text{Recovery: Metallurgical recovery rate.}\]  
Metal Price: Market price of the metal (e.g., USD/tonne).  
Payable Factor: Fraction of recovered metal that is payable by the buyer, after refining charges.  
Total Operating Cost: Sum of mining, processing, and administration costs.  
It is also known that dilution has a negative impact on operating costs. To achieve the planned metal content, more feed must be processed at the processing plant, which will increase total operating costs. With dilution, the decrease in grade, along with the additional material produced, is expected to raise transportation and ore processing costs [27]. Dilution impacts revenue, expenses, and profit margins. In a study, a 10% reduction in dilution resulted in a 6% increase in the internal rate of return [28]. In another study, where the per-ton cost of dilution was reported as USD 30, a 1% decrease in dilution saved USD 150,000 over a total of 500,000 t [29].  
Assessing financial metrics in stope design and production planning provides more robust decisions on the schedule, minimizes the economic risks, and contributes to the sustainability of the mining operation.
# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 3 Wind Power Forecasting Model with Temporal Convolutional Networks  
In this section, a TCN-based approach for a long-term wind power forecasting model is presented. A detailed workflow of the TCN model design for 24-72 h wind power forecasting is described herein. The following three subphases comprise the TCN model development process with DE for determining the optimal parameters of the proposed model: (i) architecture design, (ii) determination of the architecture parameters of the model, and (iii) the overall process for model development.
## 5 Methodology  
We conducted a pilot study to evaluate the feasibility of Hearlt. In this study, we compared three variants: (1) Hearlt, (2) Partial-Hearlt, which provides the same function as Hearlt, except for topical cue, and (3) Baseline, without the paragraph-level skip and auditory cues. We designed the repeated measures experiment that each participant experiences the three methods. The participant was asked to explore lecture audio and find the specific positions related to the given contexts (searching without explicit queries). We prepared three audio files of a graduate lecture on AI (about 20 min for each). As a result that we simply recorded actual slide-based lectures in a University, it did not have explicit structure at first. To structuralize the audio, we first partitioned the audio by using the page numbers of the slide that the audio covers. Next, several partitioned audio intervals have a longer length than four minutes, so we further divided them based on the context. Finally, the partitioned audio interval was regarded as the paragraph, and there were 31 paragraphs from the three audio lectures. Under the lecturer's supervision, three contexts per lecture audio to be browsed and playback positions to each context (ground truth) were selected. An example of the contexts is \"Differences between rule-based systems and machine learning\".
# Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data
## 5 Analysis of Tunnel Driving Behavior
### Analysis of Headway Distribution  
The headway indicates the time difference between the two vehicles passing through the same position on the road and represents the maximum reaction time of the driver behind the vehicle when the current vehicle brakes suddenly. The headway distance is closely related to the traffic flow composition and driving behavior and is an essential indicator of road capacity, service level, and driving safety. The probability distribution of headway is shown in Figure 4.  
The time headway for different driving speeds ranges from 2.04 s to 2.52 s in synchronous flow and from 2.09 s to 3.76 in the wide moving jam. The time headway distribution for the two traffic phases shows a clear difference. In synchronous flow, the disturbance caused by the lane-changing behavior forces the following vehicle to speed adaptation, varying from high speed to low speed. The traffic inflow at the bottleneck is similar to the extrusion process, and the headway distance is gradually compressed. Finally, the headway decreases slightly, explaining that the road system still maintains a high flow input in the synchronous flow state.  
The traffic flow at the bottleneck has been compressed in the wide moving jam flow. The driver tends to drive at a higher distance for safety considerations in this state. In addition, the driver will have a certain psychological threshold of safe distance, even in a completely stationary state. There will still be a particular gap between vehicles, so it can be explained that in the state of wide moving jam flow, the distance between the fronts has been compressed because of the significantly reduced speed. The probability distribution of headway shows that the lower the speed, the larger the value.
## 5 Analysis of Results and Field Testing
### Field Tests
#### 5.5.2 Water Level Measurement Test and Analysis  
To quantify the accuracy of the water level measurement algorithm proposed in this paper, we sampled water gauge images from each of the two of the three hydrological stations mentioned earlier over a period of time, took separate water level measurements, and compared them with the results of manual readings. The comparison results are depicted in Figure 11. The error curves for the two stations show that the water level measurement error of the proposed algorithm is within 1 cm, which is sufficiently accurate to meet the requirements for water level measurement.
## 3 Results
### Removal of Bacteria from Water Filtered by Saturated Granules  
Three samples were prepared, each with water filtered through a saturated micelle-clay column. Within experimental error, initially all samples were expected to include the same numbers of bacteria. Sample M1 was not treated. The tests indicated that it included 5900 TBC per mL. Sample M2 underwent heating to a temperature of 90-98 \({}^{\circ}\)C for 22 min by placing it in a vessel on an electrical plate operated at 800 W. The analysis gave 2 TBC per mL. Sample M3 underwent heating in a MW oven operated at 140 W for 26 min. The test indicated \(<\)1 TBC per mL. It is remarkable that complete elimination of the bacteria from water was energetically at least five-fold more efficient than by conventional means.
## 2 Modeling Methodology and Estimation of Input Parameters
### Soil-Pile Interface Model  
Three types of situations, namely full contact, slippage, and separation, could occur between the soil and pile surface under strong earthquake loading. The soil-pile interface element should be able to reproduce these kinds of phenomena to predict the dynamic soil-pile interactive behavior accurately. In this study, the interface element that can consider both slippage and separation between the soil and pile under strong earthquake conditions was adopted. The applied interface model estimates the soil stiffness using normal and shear spring constants, which were calculated from Equation (3) (Itastca Consulting Group, 2006 [21]). Soil nonlinearity was considered in the shear modulus (G) and bulk modulus (K), which were input to Equation (3). The interface stiffness obtained by Equation (3) was input continuously according to the depth by FISH. On the other hand, the interface yield criteria were set according to Equation (4) and three types of interface behaviors such as fully contact, slippage, and separation occurred with the magnitude of external force (\(F_{n}\)).  
\[k_{E}=max\left[\frac{K+(4/3)G}{\Delta z_{min}}\right] \tag{3}\]  
where K is the bulk modulus, G is the shear modulus, and \(\Delta z_{\rm min}\) is the smallest width of the adjacent zone in the normal direction  
\[F_{smax}=cA+tan\varnothing(F_{n}-pA) \tag{4}\]  
where c is the cohesion along the interface, \(\emptyset\) is the friction angle of the interface, p is the pore water pressure, and A is representative area associated with the interface node.  
The soil at soil-pile interface zone has different strength characteristic with that in remote zone because of the soil-pile interaction and soil disturbance. Therefore, in this study, interface friction angle which is slightly smaller than maximum internal friction angle of the remote soil that is not affected by soil-pile interaction was adopted. [PERSON] (1990) [24] proposed a value that is approximately 70% of the maximum internal friction angle of the remote soil as the calculation criterion for the internal friction angle at the interface. [PERSON] et al. (2000) [25] proposed a value that is approximately 60% of the maximum internal friction angle of the remote soil. In this study, using the results obtained by [PERSON] et al. (1994) [26], the internal friction angle at the soil-pile interface element was calculated using Equation (5).  
\[\rm\delta=\varnothing_{max}-5^{\circ} \tag{5}\]  
where \(\rm\delta\) is the internal friction angle of the interface, and \(\varnothing_{max}\) is the maximum internal friction angle of the remote soil.  
\begin{table}
\begin{tabular}{c c} \hline
**Plasticity Index** & **k** \\ \hline
0 & 0.00 \\
20 & 0.18 \\
40 & 0.30 \\
60 & 0.41 \\
80 & 0.48 \\ \(\geq\)100 & 0.50 \\ \hline \end{tabular}
\end{table}
Table 1: Overconsolidation ratio exponent ([PERSON] and [PERSON], 1972).