esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Staged Combustion Cycle: Comparison of Techniques and Objective Functions  
Similarly to Section 3.2, the same objective functions and optimization techniques are tested on an RFNA-RP1 SC cycle. Mission constraints and payload remain the same and the results for three OF are shown in Figure 6. A 'box' is included in each graph to depict the operating range of \(p_{c}\) generally ascribed to SC cycle engines [11].  
This given mission has returned values (for all techniques and objective functions) that lie below the operating \(p_{c}\) range for SC cycles. However, for the purposes of this research, as the SC cycle is compared to GG cycle for a given mission, the overall trends and results for each technique will be discussed.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{1}{c}{**T/W**} & \multicolumn{5}{c}{**GG Cycle**} \\
**OF** & **NM** & **SA** & **CSA** & **PSO** & **PIO** & **GA** & **GA-Swarm** \\ \hline F (kN) & - & 280 & 259 & 251 & 257 & 261 & 260 \\ \(\Delta t\) (s) & - & 106.7 & 102.1 & 99.3 & 101.4 & 102.6 & 102.3 \\ \(p_{c}\) (bar) & - & 36.5 & 84.3 & 175.2 & 97.4 & 75.4 & 79.7 \\ \(l_{sp}\) (s) & - & 249.1 & 267.6 & 284.2 & 270.8 & 265.1 & 266.4 \\ GLOW (tonnes) & - & 14.4 & 12.1 & 10.9 & 11.8 & 12.3 & 12.2 \\ T/W & - & 1.98 & 2.19 & 2.35 & 2.22 & 2.16 & 2.17 \\ \hline \hline \end{tabular}
\end{table}
Table 10: GG Cycle: Values of Variables at max T/W for each technique.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{1}{c}{**GLOW**} & \multicolumn{5}{c}{**GG Cycle**} \\
**OF** & **NM** & **SA** & **CSA** & **PSO** & **PIO** & **GA** & **GA-Swarm** \\ \hline F (kN) & 257 & 269 & 259 & 255 & 259 & 257 & 259 \\ \(\Delta t\) (s) & 101.4 & 104.4 & 101.9 & 100.9 & 102.0 & 101.5 & 101.8 \\ \(p_{c}\) (bar) & 96.9 & 52.4 & 86.9 & 112.6 & 84.5 & 96.2 & 87.8 \\ \(l_{sp}\) (s) & 270.7 & 257.1 & 268.3 & 274.1 & 267.7 & 270.5 & 268.5 \\ GLOW (tonnes) & 11.8 & 13.3 & 12.0 & 11.6 & 12.1 & 11.8 & 12.0 \\ T/W & 2.22 & 2.07 & 2.19 & 2.25 & 2.19 & 2.21 & 2.19 \\ \hline \hline \end{tabular}
\end{table}
Table 8: GG Cycle: Values of Variables at min GLOW for each technique.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{1}{c}{**\(I_{sp}\)**} & \multicolumn{5}{c}{**GG Cycle**} \\
**OF** & **NM** & **SA** & **CSA** & **PSO** & **PIO** & **GA** & **GA-Swarm** \\ \hline F (kN) & 262 & 257 & 261 & 251 & 257 & 258 & 259 \\ \(\Delta t\) (s) & 102.7 & 101.5 & 102.4 & 99.1 & 101.5 & 101.8 & 101.9 \\ \(p_{c}\) (bar) & 71.5 & 96.0 & 77.6 & 192.1 & 95.7 & 89.9 & 86.4 \\ \(l_{sp}\) (s) & 264.0 & 270.5 & 265.8 & 286.3 & 270.4 & 269.0 & 268.2 \\ GLOW (tonnes) & 12.4 & 11.8 & 12.2 & 10.8 & 11.8 & 11.9 & 12.0 \\ T/W & 2.14 & 2.22 & 2.17 & 2.37 & 2.22 & 2.20 & 2.19 \\ \hline \hline \end{tabular}
\end{table}
Table 9: GG Cycle: Values of Variables at max \(l_{sp}\) for each technique.
###### Abstract
Therefore, it is necessary to take into account the non-ideal state of motion and the unknown relative velocity in practical detection. This may lead to changes in the magnetic induction strength, which in turn affects the analysis of magnetic anomaly signals [18; 19]. In practical scenarios, the variable velocity of the detection platform can reveal that energy detection is not maximized after the orthogonal basis decomposition. Thus, it is difficult to set the energy threshold properly, which affects the detection performance of the detection platform [20]. As a result, it is necessary to propose a new method to fit unknown motion information and to obtain an accurate energy estimation. In response to this quandary, this study introduced an adaptive scale factor method for unknown magnetic target motion states. The primary objective was to select the most fitting scale factor by modulating its magnitude, ensuring that the energy detection post-orthogonal basis decomposition reaches its zenith. It is crucial to detect the non-ideal motion of unknown magnetic targets. This method delineates the connection between the scale factor \(\beta\) and the magnetic target's velocity, subsequently engaging in curve-fitting exercises. Depending on the speed variances, distinct scale factors were chosen, resulting in divergent energy readings for magnetic anomaly signals, thus increasing the probability of magnetic anomaly detection. To verify the precision of our proposed method, extensive simulations and experiments were conducted. The results demonstrate the effectiveness of the method in improving the detection probability of magnetic targets in unknown motion states. In conclusion, this paper presents a new approach to address the challenges of unknown velocity and non-ideal motion states in magnetic anomaly detection. Our empirical findings substantiate the method's viability and efficacy, laying a solid theoretical groundwork for practical implementations and providing substantial research value.
## 2 Materials and Methods
### FORMIND--Amazon Version and Regionalization  
We assumed that forest dynamics are similar in areas with similar environmental factors [12]. The Amazon rainforest was therefore divided into regions with similar environmental conditions, as described in [12]. The considered environmental conditions were mean annual precipitation and mean annual photosynthetic photon flux density (PPFD) (available for the entire Amazon at a resolution of 0.5\({}^{\circ}\) (derived from WFDEI [27])), and clay content, available at a resolution of 8 km [28]. With these data, it was possible to assign each 1 km\({}^{2}\) sub-area in Amazonia to one of 1280 environmental regions, within each of which homogeneous environmental conditions prevail [12]. For each environmental region, FORMIND was used to simulate 100 ha of forest dynamics over 1000 years, resulting in a total of 128,000 ha of simulated forest area. In the simulations, the drivers for mortality and photosynthesis were adjusted for each environmental region depending on the environmental conditions [12]. Additionally, simulated aboveground biomass dynamics are influenced by random stand-replacing disturbance events, e.g., fire, thus leading to a broad range of undisturbed and disturbed successional states [29]. From these simulations, forest parameters such as aboveground biomass (AGB), gross primary productivity (GPP), and net ecosystem exchange (NEE) were obtained.
# A Wireless EEG System for Neurofeedback Training
## 3 System Architecture of the EEG Device  
### General Appearance  
The functional block of the developed EEG device comprised the transmitting and receiving modules (Figures 1 and A1). The transmission module consists of an analog front-end (AFE), a microcontroller, and a transmitter. Communication between the AFE and the software platform in the microprocessor part occurs with a transceiver via an SPI for program control.  
The receiving part of one module included a receiver, a microcontroller, and a USB communication module (Figures 1 and A1). The development of the real-time EEG data acquisition system was based on wearable modules with battery-powered, analog-to-digital, 24 bit resolution preamplifiers with conversion chips (ADS129x) that had a sampling frequency of up to 1 ksps per channel and low-power microcontrollers. They send data via wireless transmission to a host high-performance central processing unit (CPU) from 40 dry electrodes during tasks. The system was validated in a 12-channel EEG neurofeedback study in a school environment through research of typical-reading children and those with DD.
# Sintering with Sodium Carbonate and Leaching of Wolframite Cakes
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment** & **1. Leaching** & **2. Duration,** & **3. Particle** & **4. Stirrer** & **5. Coefficient of** & **Yee, \%** & **Yee, \%** & **Yee, \%** \\
**No.** & **Temperature,** \({}^{\circ}\)**C** & **min** & **Size,** \(\mu\)**m & **Speed, rpm** & **Excess Sodium** & **(Sinter)** & **(Sinter)** \\
**5** & **75** & **30** & **635** & **360** & **1.2** & **94.98** & **97.36** & **94.61** \\ \hline
**6** & **75** & **45** & **365** & **420** & **1.05** & **95.69** & **96.75** & **95.45** \\ \hline
**7** & **75** & **60** & **100** & **240** & **1.1** & **94.18** & **92.57** & **97.00** \\ \hline
**8** & **75** & **75** & **815** & **300** & **1.15** & **95.88** & **95.55** & **95.27** \\ \hline
**9** & **85** & **30** & **635** & **360** & **1.2** & **95.9** & **95.88** & **97.23** \\ \hline
**10** & **85** & **45** & **365** & **420** & **1.05** & **95.61** & **95.93** & **98.18** \\ \hline
**11** & **85** & **60** & **100** & **240** & **1.1** & **96.25** & **99.65** & **96.59** \\ \hline
**12** & **85** & **75** & **815** & **300** & **1.15** & **95.86** & **96.93** & **97.79** \\ \hline
**13** & **95** & **30** & **635** & **360** & **1.2** & **96.82** & **97.98** & **99.44** \\ \hline
**14** & **95** & **45** & **365** & **420** & **1.05** & **96.53** & **99.03** & **97.48** \\ \hline
**15** & **95** & **60** & **100** & **240** & **1.1** & **97.02** & **98.02** & **98.56** \\ \hline
**16** & **95** & **75** & **815** & **300** & **1.15** & **95.32** & **98.03** & **98.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Conditions of experiments on cake leaching for 4 levels and 5 factors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Leaching Stage** & **Specific Gravity of Solution** & **WO\({}_{3}\), g/L** & **SiO\({}_{2}\), g/L** & **SiO\({}_{2}\)/WO\({}_{3}\)\%** \\ \hline
**1** & **1.26** & **230** & **0.18** & **0.08** \\ \hline
**1** + **2** & **1.19** & **172** & **0.17** & **0.1** \\ \hline
**1** + **2** & **1.12** & **104** & **0.14** & **0.13** \\ \hline
**4** & **1.014** & **15** & **0.08** & **0.5** \\ \hline
**Process waters** & **1** & **4** & **0.03** & **0.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Composition of sodium tungstate solutions by leaching stages. The assessment of the extraction degree of tungsten into the solution was controlled by the composition and weight of the insoluble residue; the effect of the transfer of silicon impurities into the solution, etc., is insignificant due to their low content in the cake.
# Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers' Behavior
## 3 Results
### Impact Analysis Based on Trajectory Characteristics
#### 3.1.1 Small Intersection  
Scenario 1 consists of three sub-scenarios based on guide line (1-A: no guide line, 1-B: one guide line, and 1-C: full guide lines): Figure 6 compares the trajectory characteristics of vehicles in each sub-scenario. In Figure 6, the \(X\)-axis represents the standardized lateral distance of the vehicle during a left turn, and the \(Y\)-axis represents the standardized longitudinal distance of the vehicle during a left turn. Initially, as vehicles enter the small intersection with a single left-turn lane, the fluctuation of vehicle trajectories along Y direction is relatively small. However, as vehicles approach the exit, the fluctuation range significantly increases. Notably, in scenarios with no guide line, one guide line, and full guide lines, the fluctuation ranges are similar, with maximum fluctuation ranges of 0.137, 0.132, and 0.130, respectively. This suggests that in small intersections with a single left-turn lane, guide lines have a relatively minor impact on the left-turn driving behavior of novice drivers.
## 4 Software System
### Path Planning  
Once the 3D point cloud of the lasted upper is acquired, it can be used in conjunction with the corresponding 3D outsole model to plan the adhesive application path. In the shoe manufacturing process, the lasted upper undergoes manual stitching and lasting, resulting in greater geometric variation compared to the outsole. Therefore, to ensure precise bonding, each lasted upper must be individually scanned in 3D prior to adhesive application. In contrast, outsole variations are minimal, as their dimensions remain highly consistent throughout production. As such, the 3D models of all the available outsole sizes--both left and right--can be pre-scanned and stored in a database. These pre-scanned outsole models are then utilized during path planning for each upper, enabling accurate adhesive application regardless of individual shape differences.  
The adhesive path-planning procedure involves the following steps:  
1. Identifying the foot orientation (left or right) and size of the lasted upper.
2. Aligning the adhesive line of the lasted upper with the corresponding height profile of the outsole.
3. Extracting normal vectors along the adhesive line to guide precise robot motion and ensure proper spray direction.
## 4 Analysis of Reservation Frame Slotted-ALOHA
### Discrete-Time Absorbing Markov Chain
where \(P_{s}(S_{ij},c_{i},f_{i})\) is the probability that there exists a number \(S_{ij}\) of end-devices that succeed in transmitting their first data packet in a given frame with \(f_{i}\) free slots having \(c_{i}\) contending end-devices, and \(p_{r}\) is the probability that a reserved slot is released (_i.e._, a slot becomes free) when an end-device finishes transmitting its \(L\) data packets. We derive both probabilities \(P_{s}(S_{ij},c_{i},f_{i})\) and \(p_{r}\) later in Section 4.2. The rationale for the transition probability is the following. In the first condition, \(S_{ij}\) end-devices succeed in transmitting their first data packet to the coordinator, and \(F_{ij}\) slots become free among the \((m-f_{i})\) slots reserved. In the second condition, the number of contending end-devices increases, _i.e._, \(c_{j}>c_{i}\), it decreases below its lower bound, _i.e._, \(c_{j}<c_{i}-\max\left(S_{ij}\right)\), or the number of free slots increases above its upper bound, _i.e._, \(f_{j}>m-c_{i}+c_{j}\). The three options are impossible and have therefore zero probability. In the third condition, no new end-devices succeed in transmitting their first data packet, _i.e._, \(S_{ij}=0\), and the process is not yet absorbed, _i.e._, \(c_{i}=c_{j}\
eq 0\) and \(f_{i}=f_{j}\
eq m\). In the fourth condition, the transition probability is one, because the process has finished and remains in the absorbing state \((0,m)\).
## 2 Fisher-Snedecor \(\mathcal{F}\) Composite Fading Model
### Cumulative Distribution Function (\(F_{\gamma}\))  
The cumulative distribution function (\(F_{\gamma}\)) of an FSO RIS-ACs system over the F-S \(\mathcal{F}\) TI fading channels can be obtained by using the following formulae:  
\[F_{\gamma}(\gamma)=\int_{0}^{\gamma}p_{\gamma}(\tau)d\tau \tag{19}\]After substituting (17) in (19), the \(F_{\gamma}(\gamma)\) of an FSO RIS-ACs system can be written as:  
\[F_{\gamma}(\gamma) = \frac{a_{1}{}^{a_{1}}(b_{1}-1)^{b_{1}}a_{2}{}^{a_{2}}(b_{2}-1)^{b_{2 }}\gamma^{(b_{1}+b_{2})/2}}{4\beta(a_{1},b_{1})\beta(a_{2},b_{2})} \tag{20}\] \[\times\,\int_{0}^{\infty}\frac{r^{a_{2}/2+b_{1}/2-1}dr}{(a_{2}r^{ 1/2}+(b_{2}-1)\tilde{\gamma}^{1/2})^{a_{2}+b_{2}}}H_{1}\]  
where \(H_{1}\) is:  
\[H_{1}=\int_{0}^{\gamma}d\tau\frac{\tau^{a_{1}/2-1}}{(a_{1}\tau^{1/2}+(b_{1}-1) \tilde{\gamma}^{1/2}r^{1/2})^{a_{1}+b_{1}}} \tag{21}\]  
The integral form (I-F) expression \(H_{1}\) can be solved using the variable substitution, \(s=a_{1}\tau^{1/2}+(b_{1}-1)\tilde{\gamma}^{1/2}r^{1/2}\) and, then, by applying the binomial formula ([39], Equation (1.111)). The value of \(H_{1}\) was calculated and given as:  
\[H_{1} = \frac{2}{a_{1}{}^{a_{1}}}\sum_{k=0}^{a_{1}-1}\binom{a_{1}-1}{k}(- 1)^{k}\frac{((b_{1}-1)\tilde{\gamma}^{1/2}r^{1/2})^{k}}{b_{1}+k} \tag{22}\] \[\times\left(\frac{1}{(b_{1}-1)\tilde{\gamma}^{1/2}r^{1/2})^{b_{1} +k}}-\frac{1}{(a_{1}\gamma^{1/2}+(b_{1}-1)\tilde{\gamma}^{1/2}r^{1/2})^{b_{1} +k}}\right)\]  
After substituting (22) in (20) and, then, by using ([39], Equation (3.259.3)), the C-F \(F_{\gamma}(\gamma)\) expression of the received SNR for the considered FSO RIS-ACs was calculated and given as:  
\[F_{\gamma}(\gamma)=\frac{(b_{1}-1)^{b_{1}}a_{2}{}^{a_{2}}(b_{2}- 1)^{b_{2}}\tilde{\gamma}^{(b_{1}+b_{2})/2}}{\beta(a_{1},b_{1})\beta(a_{2},b_{ 2})}\sum_{k=0}^{a_{1}-1}\binom{a_{1}-1}{k}(-1)^{k}\frac{((b_{1}-1)\tilde{ \gamma}^{1/2})^{k}}{b_{1}+k}\] \[\times\left(\frac{(\frac{(b_{2}-1)\tilde{\gamma}^{1/2}}{a_{2}})^{ a_{2}}\beta(a_{2},b_{2})}{((b_{1}-1)\tilde{\gamma}^{1/2})^{b_{1}+k}((b_{2}-1) \tilde{\gamma}^{1/2})^{a_{2}+b_{2}}}-\frac{(\frac{a_{1}\gamma^{1/2}}{(b_{1}-1 )\tilde{\gamma}^{1/2}})^{a_{2}+b_{1}+k}\beta(a_{2}+b_{1}+k,b_{2})}{(a_{1} \gamma^{1/2})^{b_{1}+k}(b_{2}-1)\tilde{\gamma}^{1/2})^{a_{2}+b_{2}}}\right.\] \[\times\left.{}_{2}F_{1}\left(a_{2}+b_{2},a_{2}+b_{1}+k;b_{1}+a_{2} +b_{2}+k;1-\frac{a_{1}a_{2}\gamma^{1/2}}{(b_{1}-1)(b_{2}-1)\tilde{\gamma}} \right)\right) \tag{23}\]
## 4 Discussion
### Implications for Helmet Testing Programs  
The existing interaction of the friction between the helmet and the headform and V\({}_{\rm T}\) needs to be recognized when a testing program is being planned. Existing helmet testing methods have been shown to use speeds which are lower than those found in real world situations, mainly due to limitation of the height of the helmet drop facilities of the testing laboratories [21]. This implies lower magnitude for the tangential component of the velocity than those found in real world situations. Therefore, on top of the underestimation caused by a lower tangential velocity, there may be an additional underestimation of the rotational motion of the head if the friction between the headform and the helmet is low and, consequently, the headform slides inside the helmet without being stopped by the interaction between the geometries of the headform and the helmet. As discussed above, there is evidence showing that the friction coefficient between the human head and the liner of the helmet can be around 0.7. This friction is expected to increase if there is sweating, which can be common especially in warm weathers [32]. Thus, as several existing testing programs are starting to investigate different injury metrics related to the rotation of the headform and these programs should be looking into testing the helmets so that they are effective in a worst-case (but possible) scenario, our study suggests that the friction at the interface helmet/headform in oblique helmet programs should be high enough to guarantee the joint motion of the unit without sliding of the headform.  
In addition, most helmet standards call for helmets to be tested in linear impacts (without tangential component) at the highest levels of severity ignoring the helmet response to lower severity impacts. Recently, some helmet standards such as FRHPhe-01 [27] and ECE 22.06 [26] included a low severity linear impact test at 5 m/s and 6 m/s respectively in their test methods in order to avoid that helmets transmit unacceptably high levels of linear acceleration in low severity impact events. Beside of these low severity impact tests, these standards also include an oblique impact test at 8.0 m/s against a 45-degree anvil, which originate two impact velocity components (normal and tangential) with the same magnitude of 5.66 m/s. This study supports that the oblique impact test also included in these helmet standards could be sufficient to characterize the linear acceleration in low severity impacts without the need of a specific low severity test. Since the influence of the V\({}_{\rm T}\) on the linear acceleration is insignificant and the magnitude of the normal component of the impact velocity of the oblique test (5.66 m/s) is already within the range of the low-speed tests recently included in the aforementioned standards.
## 6 Conclusions  
This study proposed an approach for automatically converting IFC clipping representation to the shapefile format for the use in GIS. To achieve this goal, this study proposed to instantiate unbounded half spaces by creating B-Rep that is sufficiently large in space and using an imagined boundary. Several tests were performed to determine an appropriate size for the imagined boundary, including measuring the size of building components, assessing the relationship between boundary size and file size of half spaces, as well as the relationship between boundary size and producing time of half spaces and building components. Four BIM models, including two house models and two building models from KIT and Open IFC Model Repository were used to validate the proposed method. In comparison with previous studies that aimed to convert clipping geometry for use in GIS, this method is relatively easy to perform and can process all types of clippings, including multi-clipping with unbounded half space, and generate valid B-Rep into the shapefile format.  
The main findings of this study include the following: (1) the proposed method can successfully automate the conversion of IFC clipping into the shapefile format, regardless the type of clipping and the type of half space; (2) increasing the boundary size will not increase the file size of corresponding B-Reps for half spaces, but it will slightly increase the producing time of half spaces and processing time of building components; and (3) based on the previous point, an appropriate boundary size has been suggested to instantiate unbounded half spaces using B-Rep, which is 100 by 100 m in the x-y plane and a buffer of 20 m in the z-axis direction.  
The primary limitation of this work is that it currently depends on a Python site package provided by a commercial GIS product, ArcGIS. This site package is of low efficiency and can only run on the Windows system. In the future, an alternative open source package should be used to further improve data conversion efficiency and support more operating systems. Apart from the geometry conversion involved in this study, transferring semantic information from IFC into the shapefile format is another challenge that should be properly addressed in the future.  
Conceptualization, [PERSON], P.W., and X.W.; Formal analysis, J.Z.; Funding acquisition, P.W. and X.W.; Methodology, J.Z.; Software, J.Z.; Writing--original draft, J.Z. and P.W.; Writing--review and editing, P.W., M.C., M.J.K., and T.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Australian Government through the Australian Research Council's Discovery Project, grant number #DP180104026 and #DP170104613, and the Shanghai Economic and Information Commission Special Fund Programs, grant number Shanghai J-2018-27.  
The authors would like to thank the anonymous reviewers for their comments and suggestions that helped improve the comprehensiveness and clarity of our paper.  
The authors declare no conflict of interest.  
The pseudocode for processing clipping entity.