esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The aim of this study was to examine intra- and inter-session reliability in IMTP and IBSqT for the kinetic variables PF, RPF, and RFD at various time windows (0-50; 0-100; 0-150; 0-200; 0-250 ms), force at specific time points (50; 100; 150; 200; 250 ms), and impulse (IMP at 0-50; 0-100; 0-150; 0-200; 0-250 ms) in strength-trained individuals and report the values for SEM and SWC in both tests. The main findings of the study were: (a) absolute and relative reliability for PF and RPF in IMTP and IBSqT within the session were determined; (b) acceptable reliability for all IMP variables except IMP0-150 ms in IBSqT, while other analyzed variables were considered unreliable; and (c) excellent reliability for PF and RPF in IMTP and IBSqT between sessions, and acceptable reliability for IMP 0-50 ms and RFD 0-250 ms in IMTP between sessions. Additionally, PF and RPF are sensitive metrics for detecting performance changes in both tests. This study demonstrated that PF and RPF within-session for both tests are reliable, with an acceptable range of variability, which is consistent with previous research [27; 42; 43]. Although RFD across all its time windows achieved reliability values ranging from low to excellent for IMTP and good to excellent for IBSqT, these metrics exhibited significant variability between trials, which does not ensure reproducibility within the session. Previous research has reported that RFD measures (0-200 ms) are reliable with ICC \(>\) 0.8 despite CV \(>\) 15% [27; 44]. [PERSON] et al. (2015) reported reliability for time windows ranging from 0-30 ms to 0-250 ms (ICC \(>\) 0.8 and CV \(<\) 10%), differing from the results found in this study, except for RFD 0-250 ms for IMTP, which is a reliable variable between sessions (ICC = 0.87-0.98, CV = 7.7-14.6%). The variable force at all its time points showed moderate to good reliability with CV \(>\) 10% for IMTP, and moderate to excellent with CV \(>\) 10% for IBSqT. This finding is inconsistent with [PERSON] et al. (2018), who reported reliability for force at 50, 90, 200, and 250 ms across various hip and knee positions (ICC = 0.95, CV 8.4%), and with [PERSON] et al. (2017), who reported within-session reliability (ICC = 0.85-0.94, CV = 5.7-10%) and between-session reliability (ICC = 0.86-0.96, CV = 3.8-7.9%) for force (30, 50, 90, 100, 150, 200, and 250 ms). Impulse at 90 ms, 200 ms, and 250 ms has been reported as reliable for IMTP and IBSqT in previous studies [17; 44], contrasting with this study where IMTP did not achieve acceptable values for IMP across all time windows, unlike IBSqT which achieved acceptable reliability within the session, except for IMP 0-150 ms.  
Between-session analyses demonstrated excellent PF values for IMTP, consistent with literature reports [28; 44], and good to excellent for IBSqT with no precedents in the scientific
# Energy and Exergy Analyses of an Existing Solar-Assisted Combined Cycle Power Plant
###### Abstract  
Solar-assisted combined cycle power plants (CCPPs) feature the advantages of renewable clean energy with efficient CCPPs. These power plants integrate a solar field with a CCPP. This integration increases the efficiency of solar power plants while decreasing the CO\({}_{2}\) emissions of the CCPPs. In this paper, energy and exergy analyses were performed for an existing solar-assisted CCPP. The overall thermal efficiency and the exergetic efficiency of each component in the power plant were calculated for different solar field capacities. Also, a parametric study of the power plant was performed. The analysis indicated that the exergetic efficiency of the power plant components has its lowest value in the solar field while the condenser has the lowest exergetic efficiency in the combined cycle regime of operation. Further, a parametric study revealed that the thermal efficiency and the exergetic efficiency of the power plant as a whole decrease with increasing ambient temperature and have their highest values in the combined cycle regime of operation. Owing to these results, an investigation into the sources of exergy destruction in the solar field was conducted.  
solar energy; energy analysis; exergy analysis; CSP; PTC; ISCC; power plant Article
## 3 Experimental Procedure
### Adsorptive Removal of CFA  
#### 3.5.1 Adsorption Kinetics  
The performance of the WMS and NR/WMS without and with the amine-functional group in the adsorptive removal of CFA in aqueous phase was evaluated by a shake-flask method. Before being used, the adsorbents were pretreated at 80 \({}^{\circ}\)C for 4 h. Adsorption assays were carried out at 35 \({}^{\circ}\)C as a batch experiment in which the CFA was dissolved in deionized water to obtain an initial concentration of 5 mg L\({}^{-1}\), and the ratio of adsorbent to aqueous solution was maintained at 1 g L\({}^{-1}\). The mixture was agitated (150 rpm) in an orbital shaker. The concentration of CFA remaining at different time intervals was measured by UV spectrophotometry using a Biochrom Libra S22 UV-VIS spectrophotometer.  
The experimental data were fitted with the pseudo-first, pseudo- and Ritchie-second-order kinetic models to obtain the kinetic parameters.  
The linearized pseudo-first order rate expression [48] was defined as Equation (2);  
\[\log(q_{m}\,q_{t})\!=\log\,(q_{m})\,-\frac{k_{1}}{2.303}t \tag{2}\]  
The linearized pseudo-second order rate expression [49] was defined as Equation (3);  
\[\frac{t}{q_{t}}\,=\,\frac{1}{k_{2}q_{e}^{2}}+\,\frac{t}{q_{e}} \tag{3}\]  
where \(q_{t}\) and \(q_{e}\) are the amount of CFA adsorbed at any given time (\(t\)) and at equilibrium (mg g\({}^{-1}\)), respectively, and \(k_{2}\) is the pseudo-second order rate constant (g mg\({}^{-1}\) min\({}^{-1}\)) determined from the plots of \(t/q_{t}\) versus \(t\).  
The linear form of the Ritchie-second-order equation [50] can be expressed as Equation (4);  
\[\frac{1}{q_{t}}=\frac{1}{k_{r}q_{e}t}+\frac{1}{q_{e}} \tag{4}\]  
where \(k_{r}\) is the Ritchie-second-order rate constant (min\({}^{-1}\)) derived from the plots of \(1/q_{t}\) versus \(1/t\).
## 3 Results and Discussion
### Effect of Liquid Flow Rate on Ammonia Synthesis  
The WF-DBD reactor studied in this paper is covered with a flowing liquid film on the center electrode, and the thickness and uniformity of the liquid film have an important influence on the nitrogen fixation effect and ammonia synthesis [29]. Figure S2 shows a schematic cross-section of the WF-DBD reactor, reflecting the relationship between the liquid film and the reactor size. The reaction conditions were the peak-peak discharge voltage of 31 kV, the gas flow rate of 200 SCCM, the frequency of 8.17 kHz, and the reaction time of 10 min. The state of the liquid film on the center electrode was varied by adjusting the flow rate of the liquid entering the reactor, and the experimental results are shown in Figure 3. Figure 3a shows that the total nitrogen fixation effect was enhanced and the ammonia synthesis rate increased when the liquid flow rate was increased from 40 mL\(\cdot\)min\({}^{-1}\) to 100 mL\(\cdot\)min\({}^{-1}\); when the flow rate was further increased to 120 mL\(\cdot\)min\({}^{-1}\)  
Figure 2: Typical (**a**) voltage–current curves and (**b**) Lissajous figure curves. [applied voltage: 31 kV, discharge gas: N\({}_{2}\), gas flow rate: 200 SCCM].  
the amount of ammonia synthesized was basically unchanged and NO2\({}^{-}\) was not detected in the products.  
It can be seen from Figure 3b that the ammonia selectivity reaches a maximum value of 40% when the liquid flow rate is 100 mL\(\cdot\)min\({}^{-1}\). The enhanced total nitrogen fixation may be attributed to the increased liquid flow rate that results in a thicker, more uniform liquid film and increased circulation of the liquid, which increases the interaction with the active species. However, when the flow rate is too fast, the residence time of the liquid in the reactor is shortened, which is not favorable for the ammonia synthesis reaction. Therefore, the liquid flow rate of 100 mL\(\cdot\)min\({}^{-1}\) was chosen as the experimental condition for ammonia synthesis.
## 2 Methodology
### BTEX Standard and VOC Analysis  
In this study, 100 L standards of benzene, toluene, ethylbenzene, and xylene isomers (BTEX) were prepared and stored in a 15 L pressurized Summa stainless steel canister (Figure 20b; Appendix A). Table 11 (Appendix C) summarized the concentrations of the BTEX standards. These concentrations were chosen based on the exposure measurements obtained from Montreal during The Urban Transportation Exposure Study (UTES) [47; 48] and were adjusted higher by a factor of 2 in order to simulate the concentration expected during rush hour on highly congested roads.  
Before the experiment, the canister of BTEX standards was placed upstream of the aluminum duct, and the BTEX standard was released at 100 accm (actual cm\({}^{3}\) per min) using a mass flow controller through a 60 cm long 1/8-inch diameter stainless steel tubing. Then, the standards passed through two turbulence blades to ensure they well mixed before entering into the cabin filter housing unit, without any filter installed. The BTEX standard was evaluated twice prior to the actual BTEX experiment. During the experiment, one of the three cabin filters (two charcoal filters and the OEM filter) was installed. Then, samples were extracted from two multi-hole probes that were positioned before and after the cabin filter simultaneously into two separated pre-vacuumed Summa stainless steel canisters, and the flow rate was restricted by a pre-calibrated needle valve. The absolute pressure inside the canister increased slowly over time as the BTEX was collected. When the absolute pressure inside the sample canister reached about 600 torr, the valve on the canister was shut and capped. Then, the canister was returned to the laboratory for VOC analysis. In this study, the BTEX experiment was done twice on each charcoal filter.  
In this study, an in-house method similar to the U.S. Environmental Protection Agency (EPA) TO15 method was used to characterize the BTEX compounds, which is an automated cryogenic concentration technique [49]. In brief, an aliquot of air was first drawn from the Summa canister into a cryogenic pre-concentrator. Then, the trapped sample was released stepwise through a series of different temperature-controlled traps to separate the target VOCs from other air components (e.g., water, carbon dioxide, argon, methane), which were also present in significant amounts. The procedure concentrates the samples and improves the signal to noise ratio, allowing the ambient level VOCs to be detected with a low detection limit. The concentrated BTEX compounds were analyzed by capillary gas chromatography using low-resolution mass spectrometric detection.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling
### Supervised Upsampling  
Supervised point cloud upsampling is trained with both low-resolution point clouds and corresponding high-resolution point clouds. Although these models are very different, they are essentially composed of individual components, such as a feature extraction component, an upsampling component, a point set generation component, and a loss function. A schematic diagram of the network model is shown in Figure 2.
## 4 Discussion
### Study Limitations  
A limitation of our study was that walking on various incline conditions was conducted without participants wearing shoes. It has been reported that barefoot walking affects spatiotemporal and kinetic parameters of gait by reducing step/stride length, increasing cadence, and reducing peak vertical ground reaction force at initial contact compared to wearing common footwear [70]. Participants in our study decided to walk barefoot based on several factors. First, the belief that force transmission would be more accurate, given that the foot could move freely without interference from the force absorption or structure of the shoe sole, thus reducing artifacts or measurement inaccuracies. Second, there is an expectation of more direct sensory feedback to the central nervous system regarding foot position and sensation facilitated by proprioceptors located on its plantar surface. Thirdly, the unavailability of a specific shoe type could mitigate potential distortion and variability arising from different shoe sole types in spatiotemporal gait parameters and/or ground reaction force magnitudes. Eventually, this more natural foot functioning could yield valuable insights for various professionals, including shoe designers, as their designs necessitate a comprehensive understanding of plantar force variations under the studied conditions. Given that footwear affects the kinematics and kinetics of gait both acutely and chronically [70], the results of this study should be interpreted with caution, as barefoot walking may have influenced the observed gait parameters in our study. Other limitationsrelated to sample characteristics and methodology may also limit the generalizability of our findings. The study focused on healthy young adults, potentially limiting the generalizability of the findings to other age groups or individuals with specific health conditions. Different age groups [71] and fitness levels might exhibit varied responses when walking uphill and downhill at the speeds examined. Furthermore, this study did not extensively explore individual differences that could influence gait responses. Factors such as individual biomechanics, adaptation to participants' treadmill gait, prior long-distance walking experience, psychological factors, or variations in participant motivation during the study could impact gait parameters and ground reaction forces differently compared to walking on natural terrain. The possibility that participants modified their behavior simply due to being observed (Hawthorne effect) may also have influenced their walking patterns [72]. This heightened awareness could result in altered behaviors that may not accurately reflect natural walking conditions. Finally, walking conditions implemented in this study might not fully capture the intricacies of hiking on varied outdoor landscapes, as the study is conducted on a treadmill, and the findings may not fully generalize to outdoor hiking conditions [33]. Treadmill walking lacks the variability and unpredictability associated with natural terrains, potentially limiting the ecological validity of the results.
# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 3 Proposed Method
### Training  
In this section, we shall introduce the configuration and parameter information in our SinNet training, as well as the customized mixed loss function used.  
(1) For the configuration and parameter information, in our training process, Adam [32] is chosen as the optimization method. The learning rate and the momentum are set to 0.1 and 0.9, respectively.  
Figure 3: The detailed SinNet structure.  
(2) For the loss function, we proposed a new mixed loss function based on weighted cross entropy loss function and dice loss function. The formulas of weighted cross entropy loss function, dice loss function, and our new mixed loss function are show as (1), (2) and (3), respectively:  
\[L_{\textit{weighted\_cross\_entropy}}(y,\hat{y})=\frac{-1}{h\cdot w}\sum\limits_{j =1}^{h}\sum\limits_{i=1}^{w}\left[\lambda^{+}y(i,j)\log(\hat{y}(i,j))+\lambda^ {-}(1-y(i,j))\log(1-\hat{y}(i,j))\right] \tag{1}\]  
\[L_{\textit{dice}}(y,\hat{y})=1-\frac{2\cdot\sum\limits_{j=1}^{h}\sum\limits_{i =1}^{w}\left(\hat{y}(i,j)\cdot y(i,j)\right)}{\sum\limits_{j=1}^{h}\sum\limits_ {i=1}^{w}\left(\hat{y}(i,j)+y(i,j)\right)} \tag{2}\]  
\[L_{\textit{mix}}=\alpha L_{\textit{weighted\_cross\_entropy}}(y,\hat{y})+(1- \alpha)L_{\textit{dice}}(y,\hat{y}) \tag{3}\]  
where \(y\) is the image of true singular point area, \(\hat{y}\) is the image of predicted singular point area, \(h\) is the height of the image, \(w\) is the width of the image and (\(i\),\(j\)) is the coordinates of image pixel. \(L_{\textit{weighted\_cross\_entropy}}\), as shown in (1), is suitable for the binary classification problem. Since positive and negative labels are unbalanced in our training, \(\lambda^{+}\) and \(\lambda^{-}\) are used to balance their loss contributions. \(L_{\textit{dice}}\), as shown in (2), uses the segmentation effect evaluation index directly as the loss to supervise the network, which is mainly to solve the problem that the proportion of positive and negative samples is seriously unbalanced. Our \(L_{\textit{mix}}\) shown in (3) integrates the characteristics of dice loss with ones of weighted cross entropy loss and works well in training.
## 4 Experimental Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Dataset** & **Penalty Factor C** & **RBF Kernel \(\gamma\)** & **Dataset** & **Penalty Factor C** & **RBF Kernel r** \\ \hline SVM & & 100 & 0.001 & IJCNN-1 & 10 & 0.1 \\ CVS & German Credit & 1 & \(1/p\) & 100 & 1/\(p\) \\ FLD & & 0.1 & 0.001 & & 100 & 0.01 \\ TRSD & & 1 & 0.01 & & 1/\(p\) \\ \hline SVM & & 1 & 0.01 & Skin Segmentation & 100 & 1 \\ CVS & Waveform-0 & 10 & 0.01 & & 100 & 1 \\ FLD & & 1 & 0.01 & & 100 & 0.1 \\ TRSD & & 1 & 0.01 & & 100 & 1 \\ \hline SVM & & 1 & \(1/p\) & Cod-RNA & 10 & 1 \\ CVS & Waveform-1 & 10 & \(1/p\) & & 100 & 0.001 \\ FLD & & 1 & \(1/p\) & & 100 & 0.01 \\ TRSD & & 1 & \(1/p\) & & 10 & \(1/p\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Finally, to compare the training time of SVM, CVS, FLD, and TRSD in detail, we divide it into two parts: selecting candidate vectors (SC) and training a final SVM model (TS) when \(\beta=0.3\), summarized in Table 6. From Table 6, we can notice that it especially takes longer time for TRSD and FLD than CVS to select candidate vectors with w8a dataset. This is because the time complexity of building a decision tree is \(O(pn\log n)\) where \(p\) is the number of features and \(n\) is the size of training dataset. However, our proposed method takes shorter than FLD since calculating TRSD is more computationally efficient than fisher linear discriminant and is the fastest overall. The results in Table 6 show that the proposed method efficiently selects support vector candidates while maintaining good classification performance.
# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
## 4 Conclusions  
The experimental work resulted in improved properties at the designed CC8, as compared to properties observed at MRCC/CC7. The \(TS\) and \(HD\) were maximized to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**CCs**} & **Desirability** & **Desirability** & **Desirability** & **Composite** \\  & **Value of TS** & **Value of IT** & **Value of HD** & **Desirability Value** \\ \hline  
\hline \hline \end{tabular}  
* MRCC/CC7.  
\end{table}
Table 7: Desirability and composite desirability values of the three responses. The highest desirability value of IT was observed at CC4, whereas the highest desirability value of TS, HD and composite desirability values were observed at CC8 (represented by bold letters).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Option**} & \(T_{c}\) & \(t_{c}\) & \(P_{au}\) & **Tensile Strength** & **Impact Toughness** & **Hardness** & **Average Composite** \\  & (\({}^{\circ}\)C) & (min) & (bar) & (MPa) & (J) & **(HRB)** & **Desirability** \\ \hline \hline CCS * & 140 & 120 & 7 & 1420 \({}^{\ddagger}\)��‡ & 16.5 ‡‡ & 57.6 ‡‡‡ & 0.260 \\ \hline \hline CC4 & 140 & 120 & 3 & 1400 & 17.4‡‡ & 55.7 & 0.187 \\ \hline \hline \end{tabular}  
* Optimal CC. ‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡�‡‡�‡�‡�������������������������������������������1420 MPa (11.4% improvement) and 57.6 HRB (12.5% improvement) when cured at CC8 (140 \({}^{\circ}\)C, 120 min, 7 bar), but the \(IT\) was maximized to 17.4 J (59.6% improvement) at CC4 (140 \({}^{\circ}\)C, 120 min, 3 bar).  
Due to the detection of two different optimal CC (CC4 and CC8) for optimum properties (_TS_, \(IT\), and \(HD\)), a composite desirability function was used to determine simultaneous optimum properties. The composite desirability function yielded _TS_ of 1420 MPa, \(IT\) of 16.5 J, and \(HD\) of 57.6 HRB as optimum properties at CC8 as optimal CC for understudy CFRP, suitable for aircraft skin application.  
The statistical analysis (ANOVA) concluded that \(T_{c}\) and \(t_{c}\) are the most significant curing parameters for influencing _TS_, \(IT\), and \(HD\) of CFRP.
# Glass Beads for Road Markings: Surface Damage and Retroreflection Decay Study
## 2 Glass Beads  
GB have been commonly used to reflectorise RM for over 70 years [1]; while they are available from many manufacturers worldwide, the production technology details remain proprietary. GB are most frequently formed from recycled float glass, which is mechanically ground to the desired dimensions and fed into special vertical furnaces where, at temperatures of approximately 1200 \({}^{\circ}\)C, the irregular shards become mostly round within milliseconds. Based on professional knowledge, such technology is suitable for obtaining GB with diameters smaller than approximately 850 \(\upmu\)m; due to the utilised recycled material, their RI is 1.5. Manufacture from virgin raw materials uses different technologies allowing not only for the preparation of GB with larger diameters, but also those with increased RI, most frequently through incorporation of titanium dioxide [42]. Selected characteristics and the basic compositions of GB types commonly utilised for RM are summarised in Table 1. Among them, the _standard_ GB are by far the most commonly utilised type worldwide, the least expensive and easiest to manufacture. The _large_ GB, due to their size, are not completely flooded during rain and thus slightly improve the visibility of RM under wet conditions. The _premium_ GB are characterised by marginally increased RI as compared to the _standard_ or _large_ GB; they furnish high R\({}_{\text{L}}\) and can also provide R\({}_{\text{L}}\) under wet conditions. The _high index_ GB are used seldom and mostly for special applications like airport markings; they are also employed in RM designed for high R\({}_{\text{L}}\) under wet conditions and in RM tapes. It should be noted that GB are an amorphous material: the production technology does not allow for the preservation of any crystalline phase in silicon dioxide. Before use, GB are appropriately coated with an organosilane to achieve adhesion. The effect of silane choice on adhesion of GB to RM has never been assessed, despite its profound importance; the only related work was done on fibreglass utilised for reinforcement of plastic composites [43]. The recent pioneering article concerning analysis of silane treatments on GB for RM was, unfortunately, done under unrealistic conditions [44]. This important aspect is undergoing further investigation and the results shall be duly reported; for the purpose of this work, it was assumed that adhesion was very good due to chemical bonding between the paint and the coating on the GB. All GB ought to be free from undesired toxic elements like lead, arsenic, antimony, mercury, chromium, and cadmium. While there were reports of such contamination in some GB imported to the United States and to Brazil [47; 48], more recent data from analyses done on GB produced in Europe did not find the presence of such undesired ingredients in laboratory measurements [49] or in field assessment [50; 51]. Hence, this valid environmental concern should be considered only a local issue associated with inadequate selection of raw materials.