esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### IoT bioreactor Performance
#### 3.1.2 Methane Production and Monitoring  
Methane production, calculated from CH\({}_{4}\) content in the biogas using both gas chromatography and the IoT system's volumetric method, is presented in Figure 6. From day 20 to day 41, CH\({}_{4}\) production remained stable, with the reactor's performance yielding 296.61 \(\pm\) 23.28 mL CH\({}_{4}\) g\({}^{-1}\) VS d\({}^{-1}\). The bioreactor was considered to have reached steady-state conditions when CH\({}_{4}\) production varied by less than 10% over ten consecutive days.  
For the given substrate, methane yield in batch reactors was 450.63 \(\pm\) 14.63 mL CH\({}_{4}\) g\({}^{-1}\) VS, indicating that the s-CSTR produced approximately 34.17% less CH\({}_{4}\) than the batch reactors. This reduction is generally expected, as s-CSTRs frequently show lower CH\({}_{4}\) yields than batch reactors for the same substrate [35; 36].  
The IoT device's monitoring performance in measuring biogas CH\({}_{4}\) content closely aligned with reference measurements obtained from gas chromatography. Throughout the monitoring period, 85% of daily IoT device measurements deviated within \(\pm\)6% of gas chromatography readings, with 32% falling within a tighter \(\pm\)2% range. Larger deviations, ranging from +10% to \(-\)14%, were observed on specific days, potentially due to volumetric device maintenance (water evaporation). Additionally, the biogas flow rate of 60 mL min\({}^{-1}\) through the CO\({}_{2}\) scrubbers might have influenced the CO\({}_{2}\) diffusion efficiency, contributing to the observed discrepancies between the two measurement methods.  
To enhance the IoT device's performance, technical adjustments could be implemented, such as reducing the pump operating voltage to decrease the biogas flow rate or incorporating lower-flow-rate pumps to optimize CO\({}_{2}\) absorption by the NaOH solution. Additionally, incorporating diffusion stones into both CO\({}_{2}\) scrubbers could improve CO\({}_{2}\) dissolution by reducing bubble size, thereby increasing the NaOH solution's reactive surface area and enhancing CO\({}_{2}\) absorption efficiency [37].  
The proposed IoT system offers distinct advantages over gas chromatography, including lower acquisition and operational costs and the potential for remote applications  
Figure 6: Calculated methane production of the bioreactor after biogas’ CH\({}_{4}\) concentration measurement by gas chromatography and by the IoT device system.  
beyond laboratory settings, making it particularly suitable for field monitoring of pilot anaerobic bioreactors.
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 2 Image DNA Techniques
### Bitmap-Based Approaches  
Bitmap-based methods employ straightforward operations or preprocessing steps to compress bitmap information while maintaining image distinguishability. Examples include histogram analysis and hash-based feature extraction, such as average hash and difference hash. Bitmap-based methods operate on low-level image data, resulting in low computational overhead for DNA generation.  
This study adopts a bitmap-based image DNA method termed Nabla DNA (\(\
abla\)-DNA) [13], which converts image bitmaps into a reverse-pyramid structure. As illustrated in Figure 3, the method normalizes the brightness, color, and resolution of an image bitmap. It then segments the bitmap into quadrants along the diagonal, averaging pixel intensities within triangular regions to create a rotation-neutral vector representation.  
Nabla generates DNA by converting the resolution of the original image into a square bitmap, where the number of pixels along one axis of the bitmap is defined as the resolution of the DNA. The grayscale bitmap is then normalized according to the number of bits per pixel, which is referred to as the DNA depth. While higher resolution and depth enhance distinguishability, they may reduce the ability to identify transformed images. For representing the configuration of Nabla, a notation such as \"x8d4\" is used, where \"x8\" denotes a resolution of 8 \(\times\) 8, and \"d4\" indicates a depth of 4.
# Removal of Crotamiton from Reverse Osmosis Concentrate by a TiO\({}_{2}\)/Zeolite Composite Sheet
## 3 Results and Discussion  
### Adsorption of Crotamiton by the HSZ-385/P25 Composite Sheet  
The HSZ-385/P25 composite sheet was applied to the adsorption of crotamiton in the RO concentrate. In preliminarily experiments, we confirmed that crotamiton was rapidly adsorbed by HSZ-385 zeolite powder (Figure S2). We also confirmed that crotamiton was not adsorbed by P25 [31]. The crotamiton concentrations were plotted against time (Figure 2). Similar performances of the sheet in RO concentrate and ultrapure water revealed that other components in the RO concentrate (Table 1) did not obviously affect the adsorption of crotamiton by the composite sheet within the 24-hr treatment period.  
It has been reported that inorganic ions and organic materials affect the adsorption of pharmaceuticals [32]. Nevertheless, the adsorption of sulfonamide antibiotics from livestock urine and bisphenol A from landfill leachate by HSZ-385 was not affected by coexisting ions [14; 20]. Meanwhile, even if the organic carbon content of porcine urine was two orders of magnitude higher than those of the sulfonamides, the sulfonamides were also effectively removed [20]. For bisphenol A, the removal efficiency decreased slightly when more than 50 mg/L humic acid was added [14]. [PERSON] et al., reported that humic-like and fulvic acid-like matter in the RO concentrate were the major contributors to the color of the concentrate [33]. The RO concentrate used in this research appeared to be light brown. In the present study, when the HSZ-385/P25 composite sheet was used to adsorb the raw RO  
Figure 2: Adsorption of crotamiton using the HSZ-385/P25 composite sheet. Results are means \(\pm\) standard deviations (\(n\) = 2).  
concentrate without crotamiton spiking, only approximately 10% of the total organic carbon (TOC) was removed. Therefore, in the RO concentrate, crotamiton would be removed by adsorption on the composite sheet prior to the raw organic matter. In addition, the removal results for different initial crotamiton concentrations in the RO concentrate were similar, which implies that crotamiton can be adsorbed by the HSZ-385/P25 composite sheet over a wide range of initial concentrations.  
We previously investigated the mechanism involved in the adsorption of sulfonamides to HSZ-385. We found that HSZ-385 adsorbed neutral sulfonamides more effectively than non-neutral sulfonamides, and that hydrophobic interactions played important roles in the adsorption process [34]. Crotamiton is hydrophobic (log\(K_{\text{OW}}\) = 2.73) and has no ionizable functional groups. Therefore, hydrophobic interactions may play an important role in the adsorption of crotamiton by the HSZ-385/P25 composite sheet.
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 1 Introduction  
Locomotion is the act of moving from place to place. To move forward, legged animals, as do insects, use their limbs in a gait pattern. When considering each leg individually, a cycle of the gait pattern is divided into two phases: swing and support. In the swing phase, the limb rises from the ground and moves in the desired direction of movement; the support phase begins when the limb lands and supports a fraction of the total weight of the animal. During the whole cycle, static and/or dynamic equilibrium conditions must be kept for the gait pattern to be stable.  
Static stability is achieved when the projection on the ground of the robot's center of mass (CoM) falls inside the support polygon, defined as the convex hull of all feet in support phase [1, 2]. Dynamic stability occurs when the zero moment point (ZMP)--the point with respect to which reaction forces at the contacts between the feet and the ground do not produce any moment in the horizontal direction--is maintained inside the support polygon throughout the gait. Gait patterns whose stability is determined by dynamic conditions allow for faster displacements of the robot because the CoM projection can be located outside of the support polygon for short periods of time [3, 4]. Therefore, in order to guarantee stable locomotion, gait synthesizing algorithms must coordinate all limbs of the robot to make it move in the desired direction, while satisfying the static or dynamicequilibrium condition. In general, the stable locomotion is achieved by using precomputed gait patterns for given trajectory and terrain conditions, or by using parametric-adjusting of the gait by heuristic or bio-inspired approaches to cope with trajectory and terrain changes.  
The algorithm we propose, called K3P for kinematic tripod, is a real-time kinematic gait generator capable of on-the-fly computing of the limb motions of a legged robot in order to move along an unknown arbitrary trajectory on uneven terrain, while maintaining static equilibrium, maximizing horizontal displacement of the feet contact points, and avoiding collisions between two consecutive limbs. In this paper, we describe some related approaches, analyze the performance of K3P in a virtual test scenario, and use torque estimations to measure the viability of the synthesized gait pattern.
## 3 Active Power Control of the ASG to Suppress Frequency Fluctuation  
When the ASG is adopted as DG2, ASG controls its output power in order to suppress the frequency fluctuations of the power system. In this study, the DFIG is adopted as the ASG, and a fuzzy logic controller is installed for generating the reference of DFIG active power, as shown in Figure 7. Due to the installation of the fuzzy logic controller, it is expected that the frequency control system can achieve sufficient performance under a variety of controlled power system conditions, such as randomly fluctuating power from the wind farm. Though there are several fuzzy logic control methods, Takagi-Sugeno fuzzy logic is adopted in this study [20]. Measured frequency deviation \(\Delta\)F and its differential value are used for fuzzy logic control, and the reference for DFIG active power is calculated by using the fuzzy rule shown in Table 6 and the membership function shown in Figure 8a,b. Parameters shown in Figures 7 and 8 and Table 6 have been determined by trial and error method. Specifically, the gains \(\text{G}_{\text{P}}\), \(\text{G}_{\text{D}}\) and \(\text{G}_{\text{O}}\) shown in Figure 7, and the values on the horizontal axes in the membership functions shown in Figure 8a,b have been repeatedly employed in various patterns, and the values making the system response more stable (i.e., non-oscillatory) and improving the  
Figure 6: Pitch angle controller of the variable speed wind turbine.  
\begin{table}
\begin{tabular}{c c} \hline \hline Rated Frequency & 20 Hz \\ Stator winding resistance & 0.02 p.u. \\ Stator leakage reactance & 0.1 p.u. \\ \(D\)-axis synchronous reactance & 0.4 p.u. \\ \(Q\)-axis synchronous reactance & 0.4 p.u. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Electrical parameters of the permanent magnet synchronous generator (PMSG).
# Implementation of MS Circle Map in Digital Image Encryption
## 2 Materials and Methods  
### Cryptography  
According to [PERSON] [24], Cryptography is the science and art of learning how to maintain the security of a message. Meanwhile, [PERSON] et al. [25] define cryptography as a field that studies mathematical techniques related to aspects of data security, such as confidentiality, validity, integrity, and authentication.  
Apart from aiming to provide security and confidentiality of information, another goal of cryptography is to provide data integrity services, namely, to ensure that the message remains original or that the message has not been manipulated. Authentication is used to ensure that both the communicating party and the source of the information are correct. Non-repudiation is intended to prevent communicating entities from making communication errors, namely, the sender of the message denying sending the message or the receiver of the message denying receiving the message [26].  
The original data or information is called plaintext, while the data or information that was encoded is called ciphertext. The process of converting plaintext into ciphertext is called encryption. However, the reverse process of converting ciphertext into plaintext is called decryption. The encryption and decryption process requires a key that is only known by the sender or receiver [27].
###### Abstract  
Radiant floor heating is becoming increasingly popular in cold climates because it delivers higher comfort levels more efficiently than conventional systems. Wood is one of the surface coverings most frequently used in radiant flooring, despite the widely held belief that in terms of thermal performance it is no match for higher conductivity materials if a high energy performance is intended. Given that the highest admissible thermal resistance for flooring finishes or coverings is generally accepted to be 0.15 m\({}^{2}\)K/W, wood would appear to be a scantly appropriate choice. Nonetheless, the evaluation of the thermal performance of wooden radiant floor heating systems in conjunction with the building in terms of energy demand, thermal comfort, and start-up period, has been insufficiently explored in research. This has led to the present knowledge gap around its potential to deliver lower energy consumption and higher thermal comfort than high-thermal-conductivity materials, depending on building characteristics. This article studies the thermal performance of wood radiant floors in terms of three parameters: energy demand, thermal comfort, and start-up lag time, analysing the effect of wood properties in conjunction with building construction on each. An experimentally validated radiant floor model was coupled to a simplified building thermal model to simulate the performance of 60 wood coverings and one reference granite covering in 216 urban dwellings differing in construction features. The average energy demand was observed to be lower in the wood than in the granite coverings in 25% of the dwellings simulated. Similarly, on average, wood lagged behind granite in thermal comfort by less than 1 h/ day in 50% of the dwellings. The energy demand was minimised in a significant 18% and thermal comfort maximised in 14% of the simulations at the lowest thermal conductivity value. The vast majority of the wooden floors lengthened the start-up lag time relative to granite in only 30 min or less in all the dwellings. Wood flooring with the highest thermal resistance (even over the 0.15 m\({}^{2}\)K/W cited in standard EN 1264-2) did not significantly affect the energy demand or thermal comfort. On average, wood flooring lowered energy demand by 6.4% and daily hours of thermal comfort by a mere 1.6% relative to granite coverings. The findings showed that wood-finished flooring may deliver comparable or, in some cases, higher thermal performance than high-conductivity material coverings, even when their thermal resistance is over 0.15 m\({}^{2}\)K/W. The suggestion is that the aforementioned value, presently deemed the maximum admissible thermal resistance, may need to be revised.  
W 2022
# A High Thermal Conductivity of MgO-H\({}_{2}\)O Nanofluid Prepared by Two-Step Technique
## 2 Experimental Details  
### Materials and Method  
The co-precipitation method was used to create MgO nanopowder. Analytical grade chemicals such as magnesium sulfate (MgSO\({}_{4}\cdot\)7H\({}_{2}\)O), sodium bicarbonate (NaHCO\({}_{3}\)), ammonium hydroxide (NaOH), and sodium dodecyl sulfate (SDS) were utilized without additional purification. NaOH and SDS were both used as a surfactant at room temperature. The chemicals used were all from BDH Chemicals Ltd., Poole, Dorset, UK. First, a 0.8 M solution of NaHCO\({}_{3}\) and NaOH was prepared by dissolving a suitable quantity in deionized water. Similarly, Mg precursor solution was prepared by dissolving MgSO\({}_{4}\cdot\)7H\({}_{2}\)O in deionized water. Next, 50 mL of 0.5 M MgSO\({}_{4}\) and SDS solution were blended fully by stirring vigorously by utilizing a magnetic stirrer for 20 min at room temperature. Then, using a burette, 50 mL of 0.8 M NaHCO\({}_{3}\) solution was gently poured into it, while it was constantly stirred. Then, 50 mL of 1 M NaOH solution was added in batches into the above resulting solution under stirring. The final combination was left to swirl for 4 h after adding the surfactant and precipitating agent, with no parameters being modeled.  
The extremely finely powdered white precipitate MgO was collected at the bottom of the preparation flask once the time was up. The fine powder was carefully separated using a centrifuge. The whole precipitation was washed several times with distilled water. The main purpose of the washing process was to ensure that the precipitate was free from foreign trace elements, especially SDS residue. The resulting MgO powder was dried in an air oven at 60 \({}^{\circ}\)C for 2 h. Then, the prepared MgO nanopowder was produced via a calcination process using a furnace at 350 \({}^{\circ}\)C for 1 h. All details of the preparation method are explained in Figure 1 (first step).
# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
## 3 Proposed Methodology  
There are some existing methods which are furnishing us with network connectivity among nodes and cluster nodes during the handoff. The proposed technique in this study is purely dependent on the clustering mechanism of the vehicle. In IoV group is arranged by various clusters of vehicles by means of relative movement and afterward information is distributed amongst all the vehicles in a group. In the proposed methodology for maintaining the safe track of the handoff process, two nodes are used. The following lines provide discussion of the two nodes. The first node being used here is called head node and the second node is called an edge backup node. It is assumed that in the working of a network system, nodes are further divided by making more groups later on. Both nodes being used here are dynamically selected on basis of the following three parameters. 1. Storage capacity;
2. Communication range;
3. Energy. A list of candidate backup mobile edge-nodes will be created and scores will be calculated on the bases of these parameters. When a node is entered in the cluster, its score will be calculated and entered into the candidate backup list. Both cluster head and backup edge node have a copy of this list. If the cluster head faces disconnection, the backup mobile edge-node will become the cluster head and a high-scoring backup mobile edge node from candidate backup list will be selected. If the backup node faces disconnection. the head node will select a high-scoring backup mobile edge-node from the list. Issues of overhead on cluster head and packet loss rate are decreased and improved through this methodology. The following points show the proficient performance of the proposed technique.
## 4 Results and Discussions
### Regular Waves  
To establish the initial position of the platform with the clump masses mounted, the free decay is first calculated for the original OC3 model. However, this is done with concentrated force (equal to the weight of the clump masses) added at the location where the clump masses are mounted. The free decay calculation with no wind and no waves lasted for 1200 s before the models reached the equilibrium position. After the free decay, the surge and pitch motions of the new models are all around 0, while the heave motion is around \(-1.3\) m. Secondly, the OC3 CM models are built at this equilibrium position to ensure that the initial state of the model is balanced. In the end, the motions of the OC3 CM models under different load cases are calculated by the in-house code.  
The surge, heave, and pitch motions of the platform in the time domain are shown in Figures 5-7, respectively. In Figure 5, the surge motion of the OC3 CM models is significantly smaller than in the original OC3 model. In particular, the peak surge motion of the original OC3 model is about 18 m before reaching its stable state and about 14 m after reaching the stable state. However, the peak surge motion of the OC3 CM model is only about 13.5 m before reaching its stable state and about 10 m after achieving the stable state. Moreover, the surge motion of the OC3 CM model P1 to the OC3 CM model P5 is gradually reduced, indicating the more effectiveness of the clump masses when they are mounted at the location closer to the sea bed. However, it is necessary to point out that the clump masses cannot be located too close to the anchor point. Otherwise, they will make the connection node touch the seabed. As for the pitch motion, as shown in Figure 7, a decrease still occurs after the clump masses are added to the mooring lines. However, the decrease is not as obvious as in the surge direction, with no significant changes after moving the connection node from P1 to P5.  
Figure 5: Surge motion from 0 s to 900 s for (**a**) case 1, (**c**) case 2, and (**e**) case 3, and from 600 s to 900 s for (**b**) case 1, (**d**) case 2, and (**f**) case 3.  
Through the examination of the above figures and the representative statistics, the new model is considered to reduce the motions of the spar platform at a regular wave. This is especially evident when the value of the surge is reduced by about 30.89% even if there are no significant changes to the heave and pitch performance.