esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Visually Induced VR Reduction Method for Virtual Reality Sickness
## 2 Materials and Methods
### Experimental Environment  
This section describes the experimental environment used to verify the effectiveness of the designed VR sickness reduction methods. The experiment was conducted on 40 subjects in their twenties and thirties (male: 20, female: 20). In the experiment, a 3D VR space-flight video that caused VR sickness was used. This video used for the test was self-produced to cause VR sickness using the camera's acceleration, deceleration, and rotation (yaw, pitch, roll). Considering that the level of VR sickness increases when exposed to VR content for a long time, the play time of VR video was set to 60 s [14]. Using HTC Vive Pro Eye [15], subjects watched videos with each of the VR sickness reduction methods described in Section 2.1. Figure 6 shows the experimental environment. Table 1 shows the VR sickness reduction method used in experiment.  
Figure 4: The fourth VR sickness reduction method (M4).  
Figure 5: The fifth VR sickness reduction method (M5).  
Before the experiment, subjects answered the SSQ for sickness condition measuring and performed calibration for eye-tracking after wearing VR HMD. In the experiment, a total of six videos (V0: original video; V1-V5: videos with VR sickness reduction methods using VGs) were used. At the end of each video, which was provided for 60 s, subjects had time to respond to the SSQ and the questionnaire on fatigue and immersion. The questionnaire on fatigue and immersion was conducted by subjects answering whether they felt fatigue or immersion for V1-V5. After answering the questionnaire, subjects had a rest time to relax VR sickness. Also, the original video (V0) without VR sickness reduction method and videos with VR sickness reduction method (V1-V5) were randomly placed to ensure reliability. Experiments were conducted using the protocol shown in Figure 7 for each VR HMD video. Also, in Figure 7, the V(x) symbol was used to represent the randomly placed videos (V0-V5).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Properties** \\ \hline M0 & Without VG \\ \hline \multirow{2}{*}{M1} & Size: 30\% of aspect ratio \\  & Position: Movement with head tracking \\ \hline \multirow{2}{*}{M2} & Size: 0\%\(\pm\)30\% of aspect ratio (every second) \\  & Position: Movement with head tracking \\ \hline \multirow{2}{*}{M3} & Size: 30\% of aspect ratio \\  & Position: Movement with CW (every second) \\ \hline \multirow{2}{*}{M4} & Size: 30\% of aspect ratio \\  & Position: Movement with CCW (every second) \\ \hline \multirow{2}{*}{M5} & Size: 30\% of aspect ratio \\  & Position: Movement with eye tracking (every second) \\ \hline \hline \end{tabular}
\end{table}
Table 1: VR sickness reduction method used in the experiment.  
Figure 6: Experimental environment.  
Figure 7: Experimental protocol.
# Bounce: A High Performance Satellite-Based Blockchain System
## 6 Experiments, Results, and Analysis
### Slot Protocol Measurements
#### 6.3.5 Sending Station Failures  
For the purpose of tolerating Sending Station failures, we envision several Sending Stations sending messages for the same slot. This implies that the satellite for a slot \(i\) might receive messages from many Sending Stations. The satellite orders the root arrays from the different Sending Stations, creating an array of arrays shown in Figure 9. It's important that the satellite orders the Merkle Tree roots to create an unambiguous sequence of transactions.  
Note that if the same root \(r\) appears in different slots, only its first inclusion in the blockchain is relevant, because accumulators as discussed in Section 4.3 will ignore later instances of \(r\).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Number of Roots in a** & **Sending Station Message** & **Satellite’s Validation Time** \\
**Sending Station Message** & **Creation Time** & **Satellite’s Validation Time** \\ \hline
1 & 2 ms & 12 ms \\ \hline
3 & 2 ms & 26 ms \\ \hline
5 & 2 ms & 39 ms \\ \hline
10 & 2 ms & 66 ms \\ \hline \hline \end{tabular}
\end{table}
Table 5: Timing for Slot Events affected by Different Number of Merkle Tree Roots. Experiments conducted using Raspberry Pi 4 as the satellite Bounce Unit.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 5 Conclusions and Future Work  
In this paper, an enhanced MAC/routing protocol was proposed for underwater acoustic networks to achieve efficiency in energy consumption and to prolong the lifetime of UWSNs. The proposed EE-UWSNs protocol depends on a number of principles to save energy and balance energy consumption. The performance of our proposed protocol was evaluated using a Python simulator developed by MIT called AUVNetSim. The simulation results show that the proposed UWSNs protocol has achieved progress in terms of average energy consumption, outperforming CSMA/CA/FBR and DACAP/FBR by 68.49% and 60.34%, respectively. Our EE-UWSN outperforms DACAP/DUCS and DACAP/CBAR by 10.51% and 20.69%, respectively. In addition, the proposed protocol reduces the average number of collisions and end-to-end delays. Furthermore, the performance of the protocol is evaluated using different cone angles and numbers of sinks. For future work, the effect of sensor distribution on network performance can be studied by considering regular and random distributions. Furthermore, the impact of changing the number of sensors and sensing environment will be investigated based on the purpose of the experiment. In addition, machine learning techniques can be applied to reduce the computational complexity and thus the processing delay.  
[PERSON] collected the data, performed the experiments, analyzed the results, and wrote the paper. [PERSON] supervised the research and critically revised the paper. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No (RG-1440-122).  
Not applicable.  
The authors thank the Deanship of Scientific Research and RSSU at King Saud University for their technical support.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**Abbreviation** & **Explanation** \\
5G & Fifth-Generation \\ ACK & Acknowledgment \\ CA & Collision Avoidance \\ CBAR & Cluster-Based Adaptive Routing \\ CDMA & Code Division Multiple Access \\ CSMA & Carrier Sense Multiple Access \\ CSMA/CA & Carrier Sense Multiple Access/Collision Avoidance \\ CSSTU-MAC & Spatial-Temporal Uncertainty MAC \\ CTS & Clear to Send \\ DACAP & Distance Aware Collision Avoidance Protocol \\ DCO-MAC & Data-Collection-Oriented MAC \\ DEEB & Distributed Energy-Efficient and Balanced \\ DUCS & Distributed Underwater Clustering Scheme \\ ED-MAC & Efficient Depth-based MAC \\ EE-UWSNs & Energy-Efficient protocol for UWSNs \\ FBR & Focused Beam Routing \\ FDMA & Frequency-Division Multiple Access \\ \end{tabular}
# Effect of Different Control Strategies on the Heat Transfer Mechanism of Helical Energy Piles
###### Abstract
To summarize, although helical pitch is an important factor affecting the heat transfer efficiency of SGHEs and can be improved by reducing the pitch, recent studies have found it difficult to quantitatively describe the effects of pitch size on the heat transfer of energy piles, which leads to a limitation in the scope of application. At the same time, energy piles are often buried at shallow depths and only exchange heat with the soil near the surface, which can lead to abnormal changes in soil temperatures and, thus, thermal damage, hindering the extraction of geothermal energy. Although groundwater seepage can effectively curb the problem of thermal damage in the ground, steady seepage is prevalent in layers with a vertical depth of more than 30 m. Surfaces with a vertical depth of less than 30 m are usually affected only by rainwater infiltration, which is limited by unpredictable local weather conditions. Energy piles are typically used in engineering to stabilize soil layers, avoiding unstable soil layers. Therefore, the problem of heat damage caused by shallow energy piles at depths of less than 30 m and the quantitative prediction of heat damage need to be solved urgently. At present, energy piles are often used to meet central heating or cooling needs, which often leads to the continuous operation of geothermal heat pump systems, causing the ground temperature to continue to change without recovery, resulting in a decrease in the effectiveness of heat transfer. In office buildings, however, systems only need to operate during working hours. The strategy of intermittent operation gives the ground enough time to recover and utilize surface geothermal energy with more sustainability. First, this paper models and validates the energy pile. Then, it intends to simulate the heat transfer process of an SGHPs-P energy pile using Fluent software and mainly focus on the most commonly used helix pitches [10; 12; 13; 14; 15; 16; 17; 18; 24; 25; 26; 27; 28; 29] in engineering applications on the heat transfer of SGHEs-P and to analyze the temperature distribution of the energy pile using the G-function method. Finally, it analyzes the effects of an operation pattern (continuous and intermittent) on the ground temperature and the influence of helix pitch, helix radius, and diameter of the helical tube on the helical tube wall surface convective heat transfer coefficient.
## 3 Results and Discussion
### FTIR Spectroscopy  
FTIR analysis showed the absorption peaks at 3351, 1671, 1405, 1038, and 597 cm\({}^{-1}\) (Figure 3) for the synthesis of AuNPs. The absorption band at 3351 cm\({}^{-1}\) for Hydroxy group, H-bonded OH stretch, 1671 cm\({}^{-1}\) for Amide [35], 1405 cm\({}^{-1}\) which expose symmetric stretching of COO\({}^{-}\) from an amino group, 1038 cm\({}^{-1}\) for Phosphate ion, and 597 cm\({}^{-1}\) for Alcohol, OH out-of-plane bend.  
The proposed reaction was Au\({}^{+}\) ions' reduction into metallic Au nanoparticles in the presence of metabolites and redox enzymes [36]. The significant reduction in reaction time with _Eclipta_ leaf is an important result and will enable nanoparticle biosynthesis methods to compete with other plant-assisted biosynthesis routes for the formation of nanoparticles that are currently much more rapid and reproducible [37]. The IR band proved the presence of amines, alcohols, phenols, and aromatic groups and strongly suggested the presence of certain proteins in _Eclipta prostrata_ that act as reducing/capping agents and may be responsible for the synthesis of AuNPs using leaves of _E. prostrata_.
###### Abstract  
Air pollution concerns have led to the widespread deployment of air quality monitoring stations. While high-cost government stations provide accurate data, their deployment is limited, whereas low-cost sensors offer widespread coverage but with lower accuracy. To enhance the accuracy of measurement data from low-cost air monitoring sensors, this study proposes a Multi-Scale Convolutional Residual Time-Frequency Calibration Method (MCRTF-CM), focusing on the PM\({}_{2.5}\) sensor as an example. This method leverages multi-scale convolution in the feature extractor to capture diverse features at various scales using parallel convolutional kernels. Residual connections merge the original and multi-scale features, preserving the initial input for enhanced stability. The calibration module employs Gated Recurrent Units (GRUs) to capture long-term dependencies in time-series data through reset and update gates. Additionally, the Frequency Enhanced Channel Attention Mechanism (FECAM) uses Discrete Cosine Transform (DCT) to convert time-domain data to frequency-domain, assigning weights to different frequency components to enhance key features and suppress irrelevant ones. Experimental results demonstrate that MCRTF-CM outperforms optimal Long Short-Term Memory (LSTM) networks, reducing RMSE, MAE, MSE, and MAPE by 13.59%, 14.04%, 25.33%, and 8.22%, respectively, indicating its better performance.  
air quality; low-cost sensors; calibration; MCRTF-CM; GRU; FECAM
## 3 Results
### Genotype Variations for Biomass and Biological Yield
** & **BDW-BT** \\ \hline BNDVI-P & \(-0.04\) & \(-0.14\) & \(-0.09\) & \(-0.19\) & \(-0.07\) & \(-0.14\) & \(-0.13\) & \(-0.03\) & \(-0.14\) & **0.61** \\ BNDVI-M & \(-0.24\) & \(-0.23\) & \(-0.29\) & \(-0.43\) & \(-0.16\) & \(-0.39\) & \(-0.31\) & \(-0.13\) & **\(-0.35\)** & **0.44** \\ GNDVI-P & \(-0.03\) & \(-0.13\) & \(-0.10\) & \(-0.16\) & \(-0.09\) & \(-0.14\) & \(-0.10\) & \(-0.06\) & \(-0.14\) & **0.61** \\ GNDVI-M & \(-0.24\) & \(-0.22\) & \(-0.31\) & \(-0.40\) & \(-0.18\) & \(-0.40\) & **\(-0.30\)** & \(-0.15\) & **\(-0.36\)** & **0.48** \\ RNDVI-P & \(0.02\) & \(-0.11\) & \(-0.03\) & \(-0.11\) & \(-0.09\) & \(-0.11\) & \(-0.06\) & \(-0.02\) & \(-0.08\) & **0.65** \\ RNDVI-M & \(-0.11\) & \(-0.18\) & \(-0.18\) & \(-0.26\) & \(-0.17\) & **\(-0.30\)** & \(-0.17\) & \(-0.10\) & \(-0.24\) & **0.60** \\ RENDVI-P & **0.60** & **0.62** & **0.71** & **0.77** & **0.75** & **0.88** & **0.65** & **0.60** & **0.71** & **0.39** \\ \hline WI-M & **0.63** & **0.46** & **0.65** & **0.77** & **0.51** & **0.82** & **0.57** & **0.52** & **0.68** & **0.60** \\ NWI-1-P & **-0.64** & **-0.45** & **-0.64** & **-0.74** & **-0.53** & **-0.79** & **-0.56** & **-0.51** & **-0.66** & **-0.57** \\ NDWI-P & **0.61** & **0.42** & **0.62** & **0.75** & **0.48** & **0.80** & **0.57** & **0.52** & **0.68** & **0.60** \\ NDMI-P & **-0.63** & **-0.47** & **-0.66** & **-0.75** & **-0.66** & **-0.83** & **-0.60** & **-0.54** & **-0.68** & **-0.52** \\ NDWI-M1 & \(-0.01\) & 0.12 & 0.04 & 0.12 & 0.10 & 0.13 & 0.07 & 0.03 & 0.09 & **-0.65** \\ NDWI-M2 & **0.49** & \(-0.02\) & **0.35** & **0.62** & 0.03 & **0.48** & **0.46** & 0.03 & **0.43** & **0.55** \\ \hline  & \multicolumn{10}{c}{**Anthesis Stage**} & \multicolumn{1}{c}{**BDW-AN**} \\ \hline BNDVI-P & **0.63** & **0.43** & **0.65** & **0.74** & **0.69** & **0.83** & **0.50** & **0.45** & **0.54** & **0.77** \\ BNDVI-M & **0.52** & **0.26** & **0.55** & **0.46** & **0.48** & **0.58** & **0.30** & **0.30** & **0.38** & **0.52** \\ GNDVI-P & **0.67** & **0.46** & **0.67** & **0.83** & **0.71** & **0.88** & **0.53** & **0.46** & **0.55** & **0.80** \\ GNDVI-M & **0.60** & **0.35** & **0.65** & **0.62** & **0.58** & **0.75** & **0  
In general, nearly all SRIs exhibited strong correlations with BDW-BT (r > 0.64 or \(<\)\(-\)0.64), BDW-AN (r > 0.85 or \(<\)\(-\)0.81), and BY (r > 0.70 or \(<\)\(-\)0.70) when data from two years and different growth stages were combined. In most cases, the correlation coefficients between SRIs and the three traits for an individual year were lower than the coefficients using two-year combinations (Table 4). All water-related SRIs, except the modified normalized difference water index-1 (NDWI-M1) and one index from vegetation-related SRIs (i.e., the published red-edge normalized difference vegetation index (RENDVI-P)), provided a moderate to strong correlation with the three traits at the BT stage. The opposite was true at the EMG stage, whereas nearly all vegetation- and water-related SRIs exhibited moderate to strong correlations with the three traits at the AN stage (Table 4).
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 1 Introduction
Subsequently, we delve into the design and implementation of a microservice-based edge server, delivering essential, time-sensitive services to farmers in disconnected Internet environments. To enhance animal intrusion detection, we explore different sensor placement strategies and develop an algorithm to locate invasive animals and predict their future locations. Finally, we meticulously evaluate our system's performance, conducting comparisons with current state-of-the-art frameworks across various metrics such as cost, latency, energy consumption, and distance. This paper makes the following contributions:  
* Adoption of the LoRa protocol effectively addresses the limitations of intermittent Internet connectivity and high latency of cloud-based infrastructure. * A microservice-based architecture at the edge is used to enable latency-sensitive services to be delivered just in time. * The proposal of three sensor layouts and an algorithm that accurately predicts the future locations of animals. * A comprehensive analysis and comparison of the layouts through experiments. * A comparison of our proposed solution (combination of rotating camera with PIR sensors) with an all-camera strategy in terms of costs and energy. * A rigorous evaluation and discussion on the accuracy of the algorithm, power consumption, and the practicality of the system.
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Discussion and Conclusions
#### 3.1.2 Other Drugs  
[PERSON] et al. studied the ability of hydroxypropylmethyl cellulose (HPMC) to act as an inhibitor of the nucleation and crystal growth of felodipine (a drug used to treat high blood pressure) [49]. It was found that the presence of ppm levels of pre-dissolved HPMC could inhibit both the nucleation and growth of felodipine crystals. Moreover, the authors proved that delaying nucleation was much more crucial to the stabilization of supersaturated solution than hindering crystal growth. [PERSON] et al. investigated the influence of the drug-alginate miscibility on maintaining drug supersaturation using three different model drugs: lovastatin (statin used to prevent and treat coronary heart disease), indomethacin (a drug used to reduce fever, pain, stiffness, and swelling from inflammation) and itraconazole (a drug for fungal infections), and they concluded that alginate could be used as a potential crystal growth inhibitor [50]. [PERSON] et al. studied the precipitation inhibitory effect of HPMC and methylcellulose polymers using grisefoulvin (applied in fungal infections) as a poorly water-soluble model drug [51]. [PERSON] et al. studied the impact of water-soluble polymers on the supersaturation behavior of amorphous pharmaceuticals [52]. HPMC, PVP and Eudragit L-100 were used as polymers and grisefoulvin or danazol (a drug used in the treatment of endometriosis and some benign breast disorders) as model drugs. The resultsof experiments demonstrated that the polymers contributed to drug supersaturation by inhibiting both nucleation and growth but the effect of the polymers was drug dependent.  
Although most studies concern the influence of a predetermined polymer on the selected drug, there are also some articles which describe a more complex approach to the issue. [PERSON] et al. tested the ability of different polymers of vinylpyrrolidone and a copolymer of vinylpyrrolidone and vinylacetate for their impact on the supersaturation of sixteen different drugs [53] using the CheqSol method. They observed that, contrary to basic drugs, acidic compounds displayed enhanced solubility in the presence of PVP. [PERSON] et al. provided an overview of different mechanisms by which cellulose derivatives inhibit the crystallization of drugs in the solid state and from a supersaturated solution [54].  
Polymers Used in Pharmaceuticals to Control the Nucleation of Crystalline Drugs or Precipitation of Amorphous Drugs
# Mechanical Properties and Flexural Behavior of Sustainable Bamboo Fiber-Reinforced Mortar
## 2 Materials and Methodology
### Bamboo Plant
#### 2.1.2 Bamboo Fiber Treatment  
A variety of treatments to improve the durability and the bonding of natural fibers embedded in concrete and mortar matrix are available, with different levels of complexity including but not limited to cement surface coating, lime surface coating, cement-line coating and oil impregnation [41; 42; 43; 44]. For the present study, two treatments were selected for the bamboo fibers. To enhance the durability, lignin was partially removed by simply heating the bamboo fibers in water at 85 \({}^{\circ}\)C for 72 h and drying them at 80 \({}^{\circ}\)C for 24 h [45]. In addition, the bond between the fibers and the mortar matrix was improved by an alkaline treatment, which involved the stirring of the bamboo fibers in a lime (Ca(OH)\({}_{2}\)) solution for 2 h. The solution contained 40 g of lime per liter of water. This suspension was stirred repeatedly during the entire treatment duration to avoid the sedimentation of the fibers and any undissolved lime particles. After the lime treatment, the bamboo fibers were dried at 80 \({}^{\circ}\)C for 24 h. This treatment is known to modify the surface of the fibers and improve their mechanical strength [45].
## 3 Results and Discussion
### Cytotoxicity Analysis
[63] reported the possible use of ZnFe\({}_{2}\)O\({}_{4}\) nanoparticles as radiosensitizers in cancer therapy. Therefore, the obtained nanoparticles in our study can be better explored in biomedical applications including cancer therapy, drug delivery and magnetic resonance imaging in the near future. Figure 6: **(a)** The cytotoxic effect of synthesized CuFe\({}_{2}\)O\({}_{4}\) nanoparticles on MCF-7 cells (human breast cancer cell line). Each value is expressed as a mean of \(\pm\) S.D (\(n\) = 3); **(b)** The cytotoxic effect of synthesized ZnFe\({}_{2}\)O\({}_{4}\) nanoparticles on MCF-7 cells (human breast cancer cell line).
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 3 The Proposed VSS-QR  
The overall block diagram of the proposed scheme is shown in Figure 3. It consists of an encoder and a decoder. The secret QR code \(\mathbf{H}\) is split into three shares by a VSS-QR algorithm. These three shares are transmitted to the receiver. At the receiving end, the receiver stacks all the three shares \(\mathbf{S}_{1},\ \mathbf{S}_{2},\ \mathbf{S}_{3}\) to generate a color QR code \(\mathbf{T}\). Using a standard QR code decoder, the color QR code can be converted into a B/W QR code \(\mathbf{T}^{\prime}\), which then can be decoded as an ordinary QR code. The encoder is described in Section 3.1 and the decoder is described in Section 3.2. The proposed algorithm is a \((3,\ 3)\)-threshold VSS-QR, which can be extended to \((k,\ n)\)-threshold VSS-QR.
# Friction Factor Correlation for Regenerator Working
## 4 Computational Model
### Pressure Drop and Grid Size  
The mesh was defined to be denser near the wall and at the interfaces between the rig components inside the resonator. The mesh near the dynamic wall was made coarser to allow for the movement of the wall. The grid independency test has been carried out by increasing the number of mesh points by a factor of 1.3. The grid structure remained unchanged with only the mesh density being increased. The resulting pressure drop obtained for grids of different sizes is shown in Figure 4. A grid size with a total number of cells of 8794 has been found to be sufficient to provide a solution that is independent of the grid and therefore selected for this investigation. The model has also been run in both single and double precision solver in readiness to test for round-off error. It appears that the single precision is sufficient to solve this two-dimensional axis-symmetrical model.