esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
## 2 Methodology
### B.g. [PERSON] DCT Algorithm Optimization  
Optimization in low-power VLSI design is accomplished at both the logic and algorithmic levels. Multiplication operations that demand more hardware technology compared to additions are involved in computing the DCT. A low-power DCT algorithm comprising more additions than multiplications would be suitable to cater to the low-power demands. The growing demand to optimize the design metrics (power, area, and delay) drives the ASIC designs, leading to efficient algorithms capable of accomplishing the desired throughput constraints.  
The cosine constant factors used are shown in Table 1. In Tables 2 and 3, the eight-point input pixel data are represented by the input registers from 0 to 7. The eight stages of the pipelined DCT/IDCT architecture are represented by clocks from 1 to 8. The 1D DCT/IDCT computation for an 8 \(\times\) 8 image block segment requires eight clock cycles. Four clock cycles are required for each pixel row in the original B.G. [PERSON] DCT transformation, so it remains idle for the subsequent four clock cycles before taking the next pixel row. The input data redistribution and the incorporation of pipelining and approximation for the uniform distribution of operations over the various stages are achieved in the proposed method. The DCT/IDCT operation is carried out for eight clock cycles by the inclusion of registers in such a way that only one addition or multiplication operation is included in each clock cycle. Tables 2 and 3 describe the input data streams of the improved [PERSON] eight-point DCT/IDCT, respectively and the corresponding cosine constant factors from 'a' to 'h' are represented in Table 1. S\({}_{\text{Sy}}\) and I\({}_{\text{xy}}\) represent the results of the 8 pipeline stages in the DCT data stream with 'x' stages and 'y' inputs.
## 5 Conclusions  
In this study, a numerical analysis of a solar air collector system whose absorber surface included porous stainless-steel scourers was analyzed. Toward this aim, a 3D CFD analysis and an experimental test for verification are presented. The measured values of air flow rate and inlet temperatures from the experimental work were used as the boundary conditions in the numerical study. The air flow section from the inlet to the outlet of the designed collector, namely the solution domain, was created in 3D with a one-to-one scale. Stainless-steel scorers were placed on two absorber surfaces, called C1 and C2. A surface without any element was also modeled for comparison (C3).  
The numerical analysis was performed using ANSYS-CFX-R18.2 The air velocity and temperature were compared to the values measured in the experimental work. The efficiency was also calculated using experimental measurements and numerical results. It has been shown that there is a good agreement between each method.  
The addition of stainless-steel scourers to the absorber surface has an impact on temperatures, velocities, and efficiency. While an efficiency of around 80% was achieved on surfaces with stainless-steel scourers, the efficiency remained around 55% in the flat-plate collector. Furthermore, the highest air temperature and air velocity were obtained in the collector with the most complex surface. This is because the stainless-steel scourers placed on and below the absorbing surface create turbulent flow by enhancing the surface area. This results in a high heat transfer coefficient, a high sink temperature, and reduced thermal heat loss. On the other hand, compared to the case where stainless-steel scourers were used  
Figure 13: Comparison of experimental and numerical efficiencies.  
on the absorber surface, lower temperature and velocity values were found in the flat-plate collector case where no elements were used.  
The porosity values of the scourers used in this study could not be determined exactly and their dimensions were fixed. The effect of parameters such as size and porosity should be investigated in future studies. Moreover, even if it is known to be economically cheap, its use in the solar industry should be encouraged by a good economic analysis.  
Conceptualization, O.F.C.; methodology, F.O. and O.F.C.; data curation; A.T. and C.K.; writing--original draft preparation, N.C.; writing--review and editing; N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are not publicly available as the Turkish language is used in all files, but they are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 Calculation of Results and Discussion
### Detection of Short-Period Geomagnetic Disturbances, Second-Resolution Data Processing
To estimate the state in the near-Earth space, the upper part of Figure 8 represents solar wind speed (SWS) (Figure 8a) and interplanetary magnetic field vertical component IMF Bz (Figure 8b). According to the resource [50] data, SWS increased from 360 to 500 km/s in the middle of the day on 20 April, IMF Bz fluctuations grew up to Bz = \(\pm\)12 nT. The magnetic storm began on 21 April at 15:19 UT (red vertical line). The analysis of method results (Figure 8d,e,j,h,j,k) shows short-period geomagnetic disturbances at the Paratunka and Magadan observatories before the event. Disturbances increased as the storm beginning was approaching. We can also note the short-time insignificant increase of geomagnetic activity from 13:45 to 14:00 UT at the times of SWS growth (Figure 8a) and insignificant increase of southward IMF Bz fluctuations (Figure 8b). At the time of the storm beginning, disturbance intensity increased significantly. A comparison of the results for the data from different observatories shows clear common dynamics of the process. Still, at the Magadan observatory, the disturbances have the highest amplitude associated with the site location near auroral latitudes. A comparison of the results for initial (Figure 8d,e) and preliminary processed data (Figure 8g,h) shows that noise does not affect the method results significantly and confirms its efficiency.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Implementation Cost** & **Energy Savings** & **Lifetime** & **CEP** & **NPV** \\ \hline  & **DKK** & **kWh/m\({}^{2}\) Year** & **Year** & **DKK/Saved kWh** & **DKK** \\ \hline Attic 1-a & 2,493,070 & 2.6 & 40 & 0.21 & \(-\)19,161,528 \\ \hline Attic 1-b & 2,499,790 & 3.0 & 40 & 0.19 & \(-\)19,127,272 \\ \hline Attic 1-c & 2,529,064 & 3.3 & 40 & 0.21 & \(-\)19,141,125 \\ \hline Attic 1-d & 2,553,116 & 3.6 & 40 & 0.22 & \(-\)19,160,525 \\ \hline Attic 1-e & 2,586,170 & 2.9 & 40 & 0.24 & \(-\)18,985,270 \\ \hline Attic 1-f & 2,601,514 & 3.0 & 40 & 0.25 & \(-\)19,208,829 \\ \hline Attic 1-g & 2,601,514 & 3.1 & 40 & 0.28 & \(-\)19,253,766 \\ \hline Attic 1-h & 2,635,590 & 3.2 & 40 & 0.34 & \(-\)19,246,288 \\ \hline Attic 1-h + a & 2,724,266 & 4.4 & 40 & 0.32 & \(-\)19,243,021 \\ \hline Attic 1-h + b & 2,730,986 & 4.7 & 40 & 0.32 & \(-\)19,223,721 \\ \hline Attic 1-h + c & 2,760,260 & 4.9 & 40 & 0.32 & \(-\)19,252,529 \\ \hline Attic 1-h + d & 2,784,312 & 5.0 & 40 & 0.34 & \(-\)19,271,929 \\ \hline Attic 1-h + e & 2,832,710 & 5.2 & 40 & 0.35 & \(-\)19,316,520 \\ \hline Attic 1-h + f & 2,832,710 & 5.3 & 40 & 0.5 & \(-\)19,320,233 \\ \hline Attic 1-h + g & 2,866,786 & 5.5 & 40 & 0.37 & \(-\)19,374,143 \\ \hline Attic 1-h + h & 2,866,786 & 5.6 & 40 & 0.37 & \(-\)19,359,187 \\ \hline Attic 2-a & 2,541,034 & 3.1 & 40 & 0.18 & \(-\)19,083,424 \\ \hline Attic 2-b & 2,574,816 & 3.3 & 40 & 0.26 & \(-\)19,129,327 \\ \hline Attic 2-c & 2,609,382 & 3.3 & 40 & 0.29 & \(-\)19,176,641 \\ \hline Attic 2-c + a & 2,746,022 & 3.5 & 40 & 0.40 & \(-\)19,406,345 \\ \hline Attic 2-c + b & 2,779,804 & 3.9 & 40 & 0.40 & \(-\)19,413,363 \\ \hline Attic 2-c + c & 2,814,370 & 4.3 & 40 & 0.40 & \(-\)19,420,298 \\ \hline Attic—3 a & 2,526,810 & 3.0 & 40 & 0.23 & \(-\)18,974,049 \\ \hline Attic—3 b & 2,556,084 & 3.3 & 40 & 0.24 & \(-\)19,189,801 \\ \hline Attic –3 c & 2,569,622 & 3.6 & 40 & 0.24 & \(-\)19,190,261 \\ \hline Attic –3 d & 2,612,812 & -2.9- & 40 & 0.27 & \(-\)19,033,265 \\ \hline Attic \(\text{---3 d + a}\) & 2,735,228 & 3.8 & 40 & 0.39 & \(-\)19,348,016 \\ \hline Attic—3 d + b & 2,764,502 & 4.1 & 40 & 0.39 & \(-\)19,354,390 \\ \hline Attic –3 d + c & 2,778,040 & 4.4 & 40 & 0.37 & \(-\)19,339,894 \\ \hline Attic—3 d + d & 2,821,230 & 4.7 & 40 & 0.39 & \(-\)19,386,294 \\ \hline Attic 4-a & 2,642,020 & 2.1 & 40 & 0.60 & \(-\)19,409,651 \\ \hline Attic 4-b & 2,697,984 & 2.6 & 40 & 0.57 & \(-\)19,437,343 \\ \hline Attic 4-c & 2,789,784 & 3.1 & 40 & 0.59 & \(-\)19,527,173 \\ \hline Attic 4-d & 2,935,848 & 3.6 & 40 & 0.68 & \(-\)19,681,057 \\ \hline Attic 5-a & 2,705,728 & 2.1 & 40 & 0.74 & \(-\)19,38,2581 \\ \hline Attic 5-b & 2,769,699 & 2.5 & 40 & 0.71 & \(-\)19,405,679 \\ \hline Attic 5-c & 2,873,229 & 3.0 & 40 & 0.73 & \(-\)19,486,254 \\ \hline Attic 5-d & 3,037,738 & 3.5 & 40 & 0.82 & \(-\)19,632,620 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Single renovation actions for attic floor slab, with re-establishment of the floor (usable attic space).
## 3 Results and Discussion
### Relative Density (RD)
#### 3.1.3 Optimization of RD  
Figure 6 demonstrates the outcomes of the RSM optimization performed on the RD, alongside the factors that were pertinent to this analysis. The values of the AMC parameters and responses are shown for all RSM results as red dots and blue ones, respectively. The RD optimization objective was configured to \"in range\", using \"maximize\" as the solution destination, and the anticipated result of the desirability function was in the format of \"larger-is-better\" attributes. The optimal AMC conditions were found to be P (A) = 893.89 MPa, H/D (B) = 0.854, and Al2O3% (C) = 0.031%. This led to the highest RD value possible, which was 92.791%.  
Using a genetic algorithm technique, the objective functions for each response were determined and then subjected to the AMC's boundary conditions of P, H/D, and Al2O3%. The following are the adopted objective functions along with their constraints:  
Minimized P, H/D, Al2O3 % subjected to the AMC's conditions; \(500\leq\mathrm{P}\leq 900\) (MPa), \(0.75\leq\mathrm{H/D}\leq 2\), \(0\leq\mathrm{Al2O3}\leq 10\) (%).  
The performance of the fitness value, run solver view, and matching AMC conditions of the best RD for the GA optimization technique are displayed in Figure 7a,b. The fitness function, which was subject to the AMC's boundary condition, was the maximizing of the  
Figure 4: Comparison between RD% of experimental and predicted values of Al-4 Cu/(Al2O3)x composites.  
An initial population of the hybrid DOE-GA had the following specifications based on the DOE's ideal AMC conditions: P = 700 MPa, H/D = 1.25, and Al\({}_{2}\)O\({}_{3}\) = 0%. The maximum RD value determined by the hybrid DOE-GA was 92.872% at 900 MPa, a 0.75 H/D, and 0% Al\({}_{2}\)O\({}_{3}\) (Figure 7b).
## 4 Monitoring System Design
### Displacement Measuring Points  
In the incremental launching process, settlement monitoring points are set at the concrete foundation of the temporary piers, and settlement monitoring points are made on the incremental launching piers. The monitoring points are marked with reflective paste, and this reflective paste is set at the front end of the guide girder, as illustrated in Figure 9, the measurement points are marked with red circles. Four displacement data points are set on the upper part of the steel box girder, and four prisms are set at the front end and the back end of the steel box girder. Throughout the incremental launching process, the displacement changes of the front end and back end of the steel box girder are monitored in real time, and the horizontal alignment of the incremental launching is controlled. The incremental launching trajectory should be followed to compare the monitoring value with the calculated value.  
Figure 8: Finite element model of steel box girder.
# The Influence of Waste Composition on Landfill Gas Generation in a Pilot-Scale Lysimeter
## 2 Materials and Methods
### Design of the Lysimeter Reactor and Filling  
To maintain a volume of 100 L for the waste sample, stainless use steel (SUS) was selected for the lysimeter reactor. A moisture injection tube, gas sampling port, and pressure gauge for checking gas generation were installed. At the sides, sampling ports were installed at the top, middle, and bottom in preparation for recirculation of leachate and a water meter for checking moisture inside was installed. Thermometers were installed at the top and bottom to check the temperature in the reactor. For temperature control, a heat-insulating material was installed on the side and a water-heating device was installed at the bottom. The thermometer and electric panel for temperature control were installed in the middle. At the bottom, a leachate spill tube was installed, and pebbles were placed to prevent the loss of waste when leachate leaked and the pebbles were filled up to the bottom of the heating device to prevent waste from being directly heated (Figures 1 and 2). The pebbles were filtration sand (membrane) containing 90% or more SiO2. Small sized pebbles 5-10 mm and 20-30 mm in size were filled first.  
For the waste sample, domestic waste collected from Suwon city in Gyeonggi was used. For other wastes, waste dumped at the landfill site at the Sudokwon landfill site was collected three times and 100 L of waste was filled mixing it by weight ratio. The waste size was set at 20 mm or less. Waste of the same shape was mixed with other wastes in a reactor. For consolidation, force was applied between filling. The apparent density was measured to understand the degree of consolidation.  
\begin{table}
\begin{tabular}{c c} \hline
**Waste Type** & **Composition Ratio (a)** \\ \hline C\&D debris waste (b) & Brick construction waste 12\%, Earth and sand construction waste 38\%, Combustibles (c) 50\% \\ \hline C\&D debris waste + sewage sludge & C\&D debris waste 72\%, Sewage sludge 28\% \\ \hline C\&D debris waste + solidified sludge & C\&D debris waste 79\%, Solidified sludge 21\% \\ \hline Domestic waste & Food 13\%, Paper 52\%, Plastic 28\%,Wood 1\%, Textile 5\% \\ \hline Mixed waste & C\&D debris waste 55\%, Domestic waste 24\%, Sewage sludge 21\% \\ \hline Brick construction waste & Waste concrete, construction aggregates, and bricks 100\% \\ \hline Earth and sand construction waste & Earth and sand construction waste 100\% \\ \hline \end{tabular} \({}^{(a)}\) ref. Sudokwon Landfill Management Corporation, Sudokwon landfill statistical yearbook, 2012, 10; (b) Actual landfill ratio of C\&D debris waste from Sudokwon landfill; (c) Applied wood, plastic, paper, textile, etc.  
\end{table}
Table 1: Type and composition ratio of seven wastes from the Sudokwon landfill site.
# Fermented Cashew Nut Cheese Alternative Supplemented with _Chondrus crispus_ and _Porphyra_ sp.
## 3 Results and Discussion
### Elemental Analysis  
The concentrations of minerals and trace elements for seaweed and the FCNCAs are presented in Table 2 and the \(p\)-values of supplemented products in the Supplementary Materials (Table S3). For the FCNCAs, calcium (Ca) ranged from 0.81 to 1.77 g\(\cdot\)kg\({}^{-1}\), showing a significant difference between FCNCA-C and FCNCA-CC (\(p\) = 0.0495). The results also show that _C. crispus_ has higher levels of Ca (4.29 g\(\cdot\)kg\({}^{-1}\)) than _Porphyra_ sp. (1.77 g\(\cdot\)kg\({}^{-1}\)), and this has a decisive impact on the paste of FCNCA-CC (1.07 g\(\cdot\)kg\({}^{-1}\)). As [PERSON] et al. [1] reported, plant-based products have lower calcium content when compared to dairy products. However, the values determined in this study are higher than those observed for cashew cheese brief (0.41 g\(\cdot\)kg\({}^{-1}\)) without any type of supplementation [19]. Potassium (K) content in FCNCAs ranged from 2.59 to 3.79 g\(\cdot\)kg\({}^{-1}\), showing significant differences between FCNCA-C and FCNCA-CC (\(p\) = 0.0495) and reflecting the higher K content found in _C. crispus_. When compared to fermented cashew cheese brief (1.76 g\(\cdot\)kg\({}^{-1}\)) [19], all FCNCA levels are higher, especially when supplemented with _C. crispus_ (3.79 g\(\cdot\)kg\({}^{-1}\)). Magnesium (Mg) ranges from 3.13 to 3.51 g\(\cdot\)kg\({}^{-1}\), showing a significant difference between FCNCA-C and FCNCA-CC (\(p\) = 0.0495), with _C. crispus_ exhibiting a positive effect when added to the paste, which is explained by the obtained results for the seaweeds, namely, 4.26 for _Porphyra_ sp. and 6.82 for _C. crispus_. Magnesium (Mg) deficiency is common in humans [36], playing an important role in the pathogenesis of ischemic heart disease, congestive heart failure, cardiac arrhythmias, and hypertension, among others [37], and as such is a critical mineral in the human body [38]. Sodium (Na) ranged from 6.19 to 9.53 g\(\cdot\)kg\({}^{-1}\) and showed significant differences between all samples (\(p\) = 0.0495), with supplemented products presenting higher values than controls, which are directly related to the contents determined for seaweeds, namely, 31.81 g\(\cdot\)kg\({}^{-1}\) for _C. crispus_ and 22.40 g\(\cdot\)kg\({}^{-1}\) for _Porphyra_ sp. The values obtained for controls (6.19 g\(\cdot\)kg\({}^{-1}\)) are in agreement with values reported by [PERSON] et al. [19] for cashew cheese brief (6.26 g\(\cdot\)kg\({}^{-1}\)). Phosphorus (P) ranged from 6.64 to 7.98 g\(\cdot\)kg\({}^{-1}\) and showed significant differences between FCNCA-C and FCNCA-CC (\(p\) = 0.0495), since the P levels measured in _C. crispus_ are very low (1.70 g\(\cdot\)kg\({}^{-1}\)).
## 4 Simulation and Experimental Results
### Experiment  
The experiment platform included PC,robotic controller, servo motion driver, Delta parallel mechanism, industrial camera and so on. Tests were implemented to verify the proposed method's efficiency. The servo system used Panasonic's MINAS A5-series servo motor and driver. The servo driver provided speed and torque monitor output signals, which could be collected by oscilloscope. Then the collected data was filtered. The starting point coordinates were (\(-152.5\) mm, \(0\), \(-800\) mm), and end point coordinates were (\(152.5\) mm, \(0\), \(-800\) mm), and \(h=50\) mm. The prescribed geometrical constraint was \(6\) mm, \(V_{B}=1.2\) m/s, \(V_{N}=0.8\) m/s, \(V_{\max}=2.4\) m/s. The actuated joint's angular velocity and the torques in motors of proposed method are shown in Figure 7. The actuated joint's angular velocity and the torques in motors of comparison method are shown in Figure 8. In the experiments, once the prescribed geometrical constraint was given, the proposed method was immediately used to plan the trajectory. The controller ran on a PC with a 2130 MHz processor, and had a sampling interval 0.001 s. By optimizing the algorithms and programs, we implemented an iterative program with a single PPO with the average execution time of 0.207 ms, which satisfied the real-time requirements of the robot system.  
Figure 8: Experimental results of angular velocity and torque by comparison method. (**a**) Angular velocity; (**b**) torque.  
Figure 7: Experimental results of angular velocity and torque by proposed method. (**a**) Angular velocity; (**b**) torque.  
From the experimental results, we can get the conclusion that the experimental results match the simulation results well. Experiments also show that the proposed method can achieve the prescribed geometrical constraint and have good motion characteristics. As a result, the method proposed in this paper not only has good motion performance similar to the comparison method, but also can quickly plan the trajectory according to the prescribed geometrical constraint. Some techniques, such as symmetry, are used to achieve real-time operation of the system.
## 3 Methodology  
### System Architecture and Mathematical Model  
Figure 1 depicts the system architecture of the proposed model for QACM. This architecture is divided into five phases. The mobile users are identified based on the quality of sensing elements used in their devices in phase 1. Here, incentives are proportional to the quality of sensing elements present in their mobile devices. Phase 2 and phase 3 represent the categorization of mobile users based on _QOI_ parameters. \(M_{u1}\), \(M_{u2}\), and \(M_{u3}\) are the three categories which are ready for deployment in the subsequent phase 4. Phase 5 involves the transmission of data to the sink/destination through crowd sensing.
# The Effect of Inulin Addition on Rice Dough and Bread Characteristics
## 3 Results and Discussion
### Effect of Inulin on Bread Characteristics
Larger-sized pores were situated mainly in the central part of the bread (presented in Figure 2a). The size of the pores decreased toward the outer parts, thereby reducing the average porosity value. The texture characteristics of the bread were only marginally impacted by the presence of inulin. Nonetheless, the breadrum hardness, chewiness, and adhesiveness generally decreased with the inclusion of inulin. This is a positive result, as hard breadrumbs are a common defect associated with gluten-free bread. The positive influence on the crumb hardness rose slightly with the increasing content of inulin until it reached a minimum in the bread made from a blend containing 20% inulin. In the breads prepared from blends containing a higher amount of inulin (30% and 40%), the crumb hardness began to rise. The presence of small pores surrounded by a thick dough layer might explain the hard crumbs in these breads. A similar effect of inulin on the crumb hardness was also recorded in bread prepared from a blend of rice and acorn flours [27]. However, the impact on wheat bread was the opposite [34; 43]. Figure 2: \(Cont.\)  
The positive effect of inulin's addition on the crumb chewiness increased with the rising amount of added inulin. A significant impact on springiness, cohesiveness, and resilience was observed only in the bread prepared from the blend containing the highest amount of inulin (40%). Resilience and cohesiveness were decreased by inulin, suggesting a tendency for this bread to crumble when sliced or spread. A similar negative impact was not observed in the breads with 0%, 5%, 10%, 20%, and 30% added inulin. A similar effect was observed in steamed wheat bread [34], but other authors reported the opposite effect [27, 43]. The observed discrepancies support the conclusion [7, 10] that the effect of inulin on the textural characteristics is influenced by many factors affecting inulin function, including the degree of polymerization, the level of inulin replacement, type of fermentation, and protein characteristics. Moreover, these discrepancies highlight the need for further investigation into the effect of different types of inulin on the characteristics of bread prepared from various ingredients using different production technologies. In our study, the addition of 30% inulin seemed to be optimal for producing bread with an acceptable loaf-specific volume, bread porosity, and other breadcrumb characteristics. However, it should be noted that higher baking losses should also be expected at the same time.