esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Some metallurgical processes deal with the production of metals from oxide ores (MeO), where the oxygen is removed through reaction with reductants such as coal or coke. Plastic material contains carbon and hydrogen, which might have the potential to substitute for coal in metallurgical processes, which require reducing conditions [11]. In addition to production from primary sources like ore, metallurgical processes also deal with secondary sources, such as slag, which is the byproduct of several metallurgical processes. Slag usually contains a significant amount of metal oxides. One method to recover the metals from slag is a fuming process, which involves treating the molten slag by the injection of a reducing agent into the slag bath. During the slag-fuming process, the oxides of zinc and lead are reduced by the injection of a coal-air mixture into the bath [12]. Through the utilization of SRM in nonferrous metallurgy, the plastic fraction can be used for reduction and energy supply. On the other hand, the metal content can be recycled through the processes and returned to the production of EEE. There are, however, very few studies that have examined the utilization of plastic as an alternative reductant [13; 14; 15]. The possibility of utilizing plastic material as a substitute for the current reductant and energy source has been studied in a full-scale trial at Umicore. These trials were performed with WEEE materials, which contained a high plastic content, with only coke and fuel oil as references. Energy balance calculations revealed that approximately 2/3 of the energy content of the WEEE plastics replaced the consumption of coke and fuel oil. The results showed that approximately 1/3 of the injected WEEE was used to increase the steam production [16]. The aim of this study was to study the potential of plastic-containing materials, especially SRM, as a reductant in metallurgical processes, where a zinc-fuming process was used as a case study. Three plastic-containing materials, namely polyethylene (PE), polyurethane (PUR), and SRM, were used. The first step was to compare plastic-containing materials to coal, particularly in terms of thermal conversion characterization. After a fundamental study at the laboratory scale, plastic materials were tested at the industrial scale through injection to the zinc fuming process at the Boliden Ronnskar smelter.
## 4 Discussion
### Benefits and Deficits of Current On-Board Operator Workspaces
#### 4.1.1 Workspace Design  
The following aspects are grouped by the WDQ Work Context subscale, following the Results section. A seemingly trivial, however neglected, aspect of workspace design in the investigated living labs concerns the _ergonomics_ of the overall setup of the on-board operator workspace. Operators complained about ergonomically poor workspaces. Almost all of the responses on this matter were negative. For example, P1 of the living lab HEAT criticized that the \"turn indicator buttons are too far away [from my position] so I have to stretch\". Additionally, in order to face the direction the shuttle was moving in, operators had to stand in front of the passenger seats and sometimes even lean over seats to view screens or operate input devices. Another issue operators mentioned was that sitting down was not possible without missing the traffic. As there was no dedicated seat for the on-board operator, the only option was to take a passenger seat. However, these were not directed toward the screens and control elements. Hence, the operator could not sit down within the risk of missing relevant situations in the traffic that might require their attention, nor view or operate the HMI. A third complaint refers to the positioning of some buttons. One operator said that they frequently hit the emergency button by accident with their elbow as it is located within a regular area of movement. This could have serious consequences as the control center or even first responders might be alerted automatically, leading to false alarms. However, there were some positive remarks as well: P3 in HEAT liked the simple operability of the HMI, P1 in HEAT enjoyed the virtual rear mirrors that provided a wider field of view than conventional rear mirrors, and P2 in TaBuLa praised the control screen for its reliability.  
In relation to _physical demands_, operators complained that they had to stand permanently. They were afraid this might inflict physical issues such as problems with posture.  
Regarding _equipment use_, there was a balanced number of positive, neutral, and negative responses. Operators noted that it took them considerable time to understand the HMI: \"[Learning how to operate the system] took me three months. It depends on your motivation\" (P3 HEAT).  
Unlike the previous categories, _work conditions_ depended significantly on the specific living lab. The on-board operator workspaces of the shuttles in HEAT and ReallLabHH were criticized for their lack of transparency on what happens to the operators' feedback (P3 HEAT), the overinvolvement of the control center in operations, and that a paper-based checklist that would have been preferred over a digital solution (both P1 RealLabHH), as well as being exposed to permanent stress due to too few breaks (P3 RealLabHH).  
In contrast, TaBuLa, which had been in operation for the longest period, received mostly positive comments on work conditions, for example, regarding pleasant work hours or shift plans (both P2).
## 3 Results
### Calculation of the Nominal Diameter of the Hydraulic Line  
The design of the nominal diameter of the tube have to be based on the interdependence between the flow from the Q\({}_{\mathrm{HG}}\) hydraulic pump, the flow velocity of the liquid v and the cross section of the tube S. This dependence is given by the continuity relation:  
\[\mathrm{Q}_{\mathrm{HG}}=\mathrm{S.v} \tag{4}\]  
where:  
Q\({}_{\mathrm{HG}}\)--maximum flow rate of the hydraulic pump, m\({}^{3}\)/s  
S--tube cross section \(\left(S=\frac{\pi D^{2}}{4}\right)\), m\({}^{2}\),  
v--fluid flow velocity, m/s  
In this case, two quantities are unknown, namely the flow velocity of the liquid and the nominal diameter of the tube, which we want to calculate. Based on that a gear hydraulic pump will be used in the design of the hydrostatic dynamometer, the theoretical value of the flow rate will be determined by the relation for the circumferential velocity:  
\[\mathrm{v}=\omega.\mathrm{r} \tag{5}\]  
where:  
\(\omega\)--angular velocity \(\left(\omega=2\pi n\right)\), 1/s  
After adjusting the relation (5) and substituting it into the relation (4), we get a formula for calculating the inner diameter of the tube:  
\[\mathrm{d}=\sqrt[3]{\frac{4.\mathrm{Q}_{\mathrm{HG}}}{\pi^{2}.n_{\mathrm{HG}}}} \tag{6}\]  
where:  
n\({}_{\mathrm{HG}}\)--hydraulic pump rotation speed, rpm  
d--diameter, m  
After substituting the data in the corresponding units into relation (6), the value of the internal clearance of the tube is calculated d = 19.6 mm. According to the EN 10305-1 standard, the next highest value of the internal clearance of the hydraulic tube is selected, d = 20 mm with a wall thickness s = 2 mm. From the parameters designed in this way, it is possible to recalculate the flow velocity of the liquid, resp. flow velocity range at varying hydraulic pump speeds (from 600 rpm to 3000 rpm). At the maximum rotation speed of the hydraulic pump n\({}_{\mathrm{HG}}\) = 3000 rpm, the theoretical velocity of the liquid flow is approximately v = 3.77 m/s and the maximum theoretical flow from the hydraulic pump Q\({}_{\mathrm{HG}}\) = 28 dm\({}^{3}\)/min.
# The Effect of Tongkat Ali Supplementation on Body Composition in Exercise-Trained Males and Females
###### Abstract  
Background: It has been suggested that supplementation with Tongkat Ali may affect testosterone concentrations; moreover, whether this influences body composition is unclear. Thus, the purpose of this investigation was to determine if four weeks of Tongkat Ali (400 mg daily dose) supplementation affected body composition and salivary free testosterone concentrations. Methods: Thirty-three exercise-trained males (n = 19) and females (n = 14) volunteered for this investigation (mean \(\pm\) standard deviation: age 33.1 \(\pm\) 13.0 years; height 171.1 \(\pm\) 11.3 cm; body mass 77.4 \(\pm\) 16.8 kg; average total years of training 13.9 \(\pm\) 13.2; average hours of resistance training/week 4.2 \(\pm\) 2.5; average hours of aerobic exercise/week 3.4 \(\pm\) 2.8; average hours of other exercise/week 1.1 \(\pm\) 2.0). Research participants were pre- and post-tested for body composition (InBody 270), Profile of Mood States (POMS), handgrip strength, and sleep (Pittsburgh Sleep Quality Index [PSQI]). In a subset of the research participants, saliva samples were collected and analyzed for cortisol and free testosterone. The research participants consumed 400 mg of Tongkat Ali or a placebo (rice flour) daily for four weeks. Results: There were no between-group differences (\(p\) > 0.05) for body composition (delta score, mean \(\pm\) SD: lean body mass kg-treatment \(-0.5\)\(\pm\) 1.9, placebo \(-0.4\)\(\pm\) 0.7; fat mass kg-treatment \(-0.5\)\(\pm\) 1.6, placebo \(0.3\)\(\pm\) 1.0, % fat treatment \(-0.1\)\(\pm\) 1.4, placebo \(0.5\)\(\pm\) 1.1). Moreover, there were no differences between groups for any additional assessments (e.g., handgrip strength, mood, sleep, and salivary free testosterone and cortisol). Conclusions: Four weeks of supplementation with Tongkat Ali in exercise-trained males and females does not affect body composition, mood, sleep, vigilant attention, handgrip strength, cortisol, and free testosterone.  
testosterone; exercise; training; skeletal muscle; ergogenic aid 22 May 2024
## 2 Materials and Methods
### Evaluation of the Predictive Perfomance of GMPEs--GMPE Ranking
Nevertheless, the other GMPEs followed closely, apart from MARG2002, which had a lower weight based on the \(LLH\) method. Additional adjustment of the weights was needed for the GMPEs used for the PGV calculations, given that SAKK2016 does not account for PGV. For this case, the GMPE that was ranked first was CHOU2018, with MARG2002 being ranked last again. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Functional Form** & **PGA Weight** & **PGV Weight** \\ \hline
[76] & \(\ln\)PGA = \(4.16+0.69M-1.24\ln(\text{R}+6)+0.125\pm 0.70\) & 0.14 & 0.18 \\  & \(\ln\)PGV = \(-1.51+1.11\text{M}-1.20\ln(\text{R}+5)+0.295\pm 0.80\) & & \\
[77] & \(\log\)PGA = \(1.07+0.45M-1.35\log(\text{R}+6)+0.09\text{F}+0.065\pm 0.286\) & 0.21 & 0.27 \\  & \(\log\)PGV = \(-1.46+0.64M-1.29\log(\text{R}+6)+0.02\text{F}+0.145\pm 0.32\) & & \\
[78] & \(\log\)PGA = \(0.883+0.458M-1.27\log\left(\sqrt{\text{R}^{2}+11.515^{2}}\right)+0.0388+0. 116\text{F}\pm 0.27\) & 0.20 & 0.26 \\  & \(\log\)PGV = \(-1.436+0.625M-1.152\log\left(\sqrt{\text{R}^{2}+10.586^{2}}\right)+0.0268+0. 086\text{F}\pm 0.283\) & & \\
[79] & \(\log\)PGA = \(0.814+0.472M-1.319\log\left(\sqrt{\text{R}^{2}+11.056^{2}}\right)+0.0475+0. 097\text{F}\pm 0.298\) & 0.23 & - \\  & \(\log\)PGA = \(0.816+0.748M-1.092\log(\sqrt{\text{R}^{2}+10.688^{2}}-0.0044\sqrt{\text{R}^{ 2}+10.688^{2}}+0.096\text{S}+0.146\text{F}\pm 0.285)\) & 0.22 & 0.29 \\  & \(\log\)PGV = \(-1.082+0.692M-1.614\log\left(\sqrt{\text{R}^{2}+12.641^{2}}\right)+0.1375+0. 315+0.068\text{F}\pm 0.306\) & & \\ \hline \end{tabular}
\end{table} 
Table 1: List of the selected GMPEs and their corresponding weights based on the \(LLH\) method for the study area. R is the epicentral distance (in km), S is the soil-type parameter, and F is the parameter related to the faulting style of the earthquake.
## 4 Experiments
### Ablation Experiments  
In the proposed method, we employed a two-target DIP learning strategy to generate intermediate denoising images. To compare the performance of different images as learning targets, we conducted ablation experiments on the Set12 dataset. Table 1 lists the average PSNR results for Set12. In Table 1, the columns \"Noisy\" and \"\(p_{i}\)\", correspond to the results of using the given noisy image and a single preliminary image as the learning target, respectively. Column \"Noisy+\(p_{i}\)\" indicates the use of the proposed two-target learning strategy. It is clear that simply using a preliminary image \(p_{i}\) as the DIP learning target can improve the PSNR. Moreover, the use of a two-target learning strategy can further improve denoising performance. Compared with the PSNR result of using noisy images as the DIP learning target, the PSNR result of the two-target DIP learning method was significantly improved (more than 1.6 dB). The reason behind this is because the preliminary image, which has a higher image quality than that of the given corrupted image, is generated from a supervised image denoising method. Some pixels in the preliminary image are inevitably over-smoothed. Meanwhile, the corresponding pixels in the noisy image may  
Figure 7: Unsupervised intermediate denoising images fusion network.  
be clean pixels that can be used as complementary information to the preliminary image. Therefore, using both preliminary and noisy images can provide more useful information for image denoising.  
During the intermediate image fusion process, the number of instances used for image fusion is an important factor. To explore the impact of the instance numbers on the denoising results, ablation experiments were performed on Set12 with noise level \(\sigma=25\). Table 2 shows the average PSNR using two to eight intermediate instances for image fusion on Set12. As presented in Table 2, using eight instances achieves the highest PSNR value, whereas using four instances can reach a PSNR value above 31 dB. Considering that generating four instances is much more efficient, and the PSNR result of using four instances is only 0.09 dB lower than that of using eight instances, we finally used four instances to generate a denoised image.
# Development of a Social DTN for Message Communication between SNS Group Members
## 2 Related Studies
### Creation of a Routing Table  
DTN routing tables can be created either proactively or reactively. In a proactive method, a routing table containing information about all nodes or dedicated relay nodes is created before communication is actually made between nodes. There are two types of proactive method: fixed type and encounter information sharing type. In the former, routing tables are created based on planned node contact patterns. In the latter, routing tables that contain information about all those nodes that have encountered each other accidentally or about all dedicated nodes are created. The sharing type is further divided into periodic sharing and passing-by sharing. In the former, nodes periodically exchange route information messages to share route information. In the latter, nodes share route information only when they pass by each other. On the other hand, in a reactive method, the source node sends a route establishment request message to the destination node when it wants to communicate with the latter. When a response message is sent back to the source node, relay nodes involved create routing tables for this connection.  
Message ferry [7] and routing based on traveling route information [8] are proactive methods and are both the fixed type. There is a method in which information about a node located two hops away from the encountered node is exchanged as meta information [9; 10]. Passing-by methods include PRoPHET [11] and HiBOp [14].  
The proposed method is a reactive type because a route is established only when the source node wants to communicate with the destination node. Some routing tables have information about only those relay nodes that are within a limited number of hops to the destination node or that have an encounter probability within a certain range. No such restrictions are applied to the proposed method.
# Measurement and Analysis of Downhole Drill String Vibration Signal
## 3 Result Analysis
### Frequency Analysis of Vibration Signal
#### 3.2.2. Whirl
Figure 17: The time-frequency diagram of the radial acceleration of the whirl using light and shade to represent the energy spectrum density. the radial acceleration during the whirl. It shows that these two main frequencies are the frequencies of the coupled vibration due to the torsional impactor and the cutting action of the drill bit. Three frequencies of 216.5 Hz, 361.7 Hz, and 391.8 Hz appeared in both vibration patterns. Except that 216.5 Hz is close to the quadruple frequency of torsion impactor, the other two frequencies are the coupled vibration frequency of the jar and the torsional impactor. Through the above analysis, it was found that the occurrence of downhole harmful vibration is not only related to the formation conditions but also related to the use of speed-up tools in the drill tool assembly. The occurrence of a large number of stick-slips and whirls in igneous rock drilling in this article is very likely to be related to the simultaneous use of jars and torsional impactors. The jar and the torsional impactor can generate axial vibration and torsional vibration, respectively. In the 4740-4750-m drilling of igneous rocks, the WOB changed greatly (Table 4). It is very likely that the downhole drilling tool was in a repeated 'bend-straighten' state, creating conditions for the drill string to vibrate laterally. In addition, the torsional impactor can generate torsional vibration, which makes whirl  
Figure 19: Time-frequency diagram of axial acceleration of whirl. Figure 18: Time-frequency diagram of tangential acceleration of whirl. very easy to occur. However, the acceleration amplitude of each axis in the time-domain signal of stick-slip vibration was relatively small. It shows that the torsion impactor can help the drill string get out of the'sticky' state, reduce speed fluctuations, and thereby reduce stick-slip vibration. In summary, in the process of igneous rock drilling in this well, not only proper drilling parameters must be selected, but also downhole speed-increasing tools must be reasonably used.
# Verification of Actual Prestressing in Existing Pre-Tensioned Members
## 3 Experimental Program
### Barkhausen Noise Technique  
The Barkhausen noise investigation was carried out on one upper prestressing wire from Specim 4. The scheme of the test is displayed in Figure 12. The experiment consisted of MBN measurement of the prestressing wire in the prestressed concrete sleeve at three points (Points I-III), removing of the wire, and subsequent calibration measurement in the laboratory under predefined loads (tensile stresses: 0; 100; 200; 400; 600; 800; 1000; and 1200 MPa). Finally, the obtained MBN signals were compared with signals measured on the specimen. For the measurement, the RollScan 350 (Stresstech, Jyvaskyla, Finland) was used. The analysis was carried out with the MicroScan 600 Software with the following parameters: magnetising voltage, 5 V; magnetising frequency, 125 Hz; sensor type, S1-18-12-01; frequency range of magnetic Barkhausen noise pulses in the range of 10-1000 kHz. The signals were obtained by averaging 10 MBN bursts. Magnetic Barkhausen noise refers to the root mean square effective (RMS) value of the signal [16; 17; 39]. Performance of the MBN measurement can be seen in Figure 13, whereas obtained results are presented in Figure 14. The investigation provided the following detected values of rms of MBN (mV): 270.44 \(\pm\) 14.0 (Point I); 255.51 \(\pm\) 15.0 (Point II); and 287.76 \(\pm\) 15.5 (Point III).  
Figure 11: Recorded stress relief in specimens. 1 (**a**); 2 (**b**); 3 (**c**); 4 (**d**); 5 (**e**); 6 (**f**); 7 (**g**).  
Figure 12: Scheme of MBN measurement (Specimen 4; Wire 1).  
Figure 13: Magnetic Barkhausen noise measurement (**a**); calibration (**b**).
## 3 Results and Discussion
### Use of Linear Combinations of Bands Rather Than VIs  
Figure 4 shows results at an eight-day temporal resolution and the highest spatial resolution used here (0.0083\({}^{\circ}\)) for two- and seven-band models where linear combinations of bands were used as FAPAR\({}_{\rm chl}\) proxies according to Equation 7. Both band models provided improved dynamic range in estimated GPP as compared with the NDVI\({}^{\prime}\)-based model, and the correlation improvements were statistically significant with \(p\)-values \(<\) 0.025. The seven-band and two-band models were not statistically distinguishable. All models underestimated GPP at the highest GPP values.
# Multi-Year Monitoring of Ecosystem Metabolism in Two Branches of a Cold-Water Stream
## Materials and Methods  
### Sampling Location  
Stream metabolism was assessed in two branches of Valley Creek in east-central Minnesota, USA (Figure 1). The Valley Creek watershed currently has an area of approximately 32 km\({}^{2}\), although before 1987, it was much larger but was hydroologically divided to reduce localized flooding [50]. The watershed is primarily dominated by forest, grasslands, and wetlands (27.3, 15.0, and 33.2%, respectively) with the remaining 25% being primarily low percentage (5-25%) impervious surfaces associated with large-lot residential areas and roads [50]. There are no point source discharges into Valley Creek. Both branches of the creek are designated as trout streams. Hydrological studies indicate that the South Branch receives water from groundwater that moderates its temperature [51]. The North Branch drains from Lake Edith, which is fed by groundwater from a marsh. The warming of the water in Lake Edith before it enters the North Branch results in fewer trout and more warm water fish [46; 51]. The sampling location on the North Branch is located in an open area where the stream passes through a small wetland (approximately 14,500 m\({}^{2}\)). The stream emerges from a wooded area about 100 m upstream. There is local groundwater input at the sampling site, but the stream loses water to the groundwater in the wooded area upstream. The sampling location on the South Branch is in a mostly wooded area (Figure 1).  
Figure 1: Map of Valley Creek in east-central Minnesota and the two sampling locations.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 4 Biofouling Mitigation and Advanced Cleaning Technologies
### Sustainable Cleaning Techniques
Research has demonstrated that by offering improved microbial inactivation, MBs outperform traditional cleaning techniques, particularly when combined with ozone [111, 112]. By dissolving mineral deposits and biofilms, NBs and MBs effectively clean reservoirs and irrigation systems. By avoiding blockages and preserving ideal water flow, this enhances the effectiveness of irrigation. In order to minimize the need for harsh chemical cleansers, MBs have also been used to remove pollutants from agricultural surfaces [113, 114]. Both NBs and MBs are utilized in the cosmetics sector for mild yet efficient surface cleaning. While MBs help with exfoliation and skin texture improvement because of their larger size, NBs penetrate deeper into skin pores to remove oils, debris, and impurities. When combined, they improve skin care therapies' efficacy and encourage healthier skin [66, 115]. NBs are showing promise as a biofilm control and biofouling mitigation solution in a variety of industries. By preventing microbes from adhering to surfaces, NBs help mitigate fouling by reducing the creation of biofilms that are challenging to remove. In the treatment of water and wastewater, NBs have demonstrated the ability to improve the effectiveness of filtering procedures and the removal of organic pollutants. By penetrating biofilms on medical devices and surfaces, NBs help the healthcare sector prevent infections through biofilm cleanup. Continuous technological advancements include the creation of more effective production methods and the incorporation of NBs with sophisticated filtration systems. Finally, NBs and sustainable cleaning methods encourage eco-friendly practices by lowering the need for dangerous chemicals, providing a more sustainable option than conventional cleaning processes.