esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud
## 4 Results and Discussion
### Description of the Benchmark  
The gravity packing problem of granular material, falling, under the influence of gravity, into a container, was considered in order to investigate the performance of the developed DEM SaaS because it often serves as a benchmark for performance measurements. The geometry and physical data of the problem are described for research reproducibility. The solution domain was assumed to be a cubic container with 1.0-m-long edges. Half of the domain was filled with monosized particles distributed by using a cubic structure. The granular material was represented by an assembly of 1,000,188 particles with a radius \(R\) = 0.004 m. The initial velocities of the particles were defined randomly with a uniform distribution, with their magnitudes being in the range of 0.0 to 0.1 m/s. The physical data of the particles of the artificially assumed material were as follows: density = 7000 kg/m\({}^{3}\), Poisson's ratio = 0.2, elasticity modulus = 1.0 \(\times\) 10\({}^{7}\) Pa, friction coefficient = 0.4, and coefficient of restitution = 0.5.  
The representative computational experiments for performance analysis were repeated 10 times, and the averaged values were examined. Performing the benchmark, the computation time of 5000 time steps was measured to investigate the computational performance of the developed DEM SaaS. The short time interval was considered in order to avoid domain repartitioning and reduce particle exchange between subdomains, which helps to focus on the main interprocess communication due to ghost particles. Ghost layers of different sizes were considered in order to study the influence of interprocess communication on the performance of the DEM SaaS. The ghost layers GL1, GL2, and GL3 had thicknesses = 2\(R\), 4\(R\), and 6\(R\), respectively, where 2\(R\) was the most common because it contained one layer of particles. Thicker layers might decrease the frequency of communication due to particle migration and domain repartitioning but increase the amount of data transferred between processes.
## 5 Conclusions  
ACO-based routing algorithms described in Section 2 have some common issues due to their intrinsic characteristics. First, due to the inborn defects of the ACO-based routing protocol, the route discovery process is time consuming and cannot find a feasible route to the destination quickly. Second, ACO-based routing protocols are prone to premature, stagnation phenomenon, which hinders their further route discovery.  
This paper proposes a hybrid routing algorithm combining AFSA and ACO to solve these issues. We present an AFSA-based initial route discovery to obtain effective feasible routes towards the sink node quickly. The simulation results demonstrate that the algorithm can speed up the time of first route establishment, as well as route discovery convergence time. This performance improvement is more prominent in large-scale networks. Furthermore, to avoid the stagnation phenomenon in ACO-based routing algorithms, FSACO combines the crowd factor in AFSA and the pseudo-random proportional route select model in ACO to propose a hybrid route discovery algorithm. According to the simulations, the dynamic crowd factor can effectively prevent the routing algorithm from falling into a local optimum and find the stable optimal path finally. Furthermore, FSACO presented an improved pheromone update strategy by considering both the energy levels and path length as key metrics when updating the local heuristic value and pheromone trail. The simulation results reported that FSACO showed a significant improvement over EEABR, IACO and SensorAnt in terms of energy efficiency and network lifetime.  
Figure 8: Comparison of network throughput of FSACO, EEABR, IACO and SensorAnt under a static network scenario with 100 sensor nodes.  
All of our results were obtained from simulation evaluation. However, the modelling of network behaviours in a simulation does not always reflect real-world scenarios. More accurate evaluation results can be gained through the implementation in a physical environment. We plan to implement the proposed algorithm on a physical test bed to study the impact of varying conditions on the performance of these algorithms.  
Conceptualization, X.L. Methodology, B.K. Project administration, B.K. Supervision, F.M. Writing, original draft, X.L. This research was funded by the Science Foundation for Youths of Hefei University, grant number 16 YQ08 RC. The authors declare no conflict of interest.
## 3 Results and Discussion
### Qualitative Composition of the VOCs from the Obtained Bio-Based Boards
Just to compare to available literature data about VOCs from SE materials, 134 substances were found in the SE-treated softwood bark pellets, 53 of which were identified and the main ones being reported as furans (furfural), terpen (\(\alpha\)-pinene), aldehydes (hexanal), and an unknown representing 1-4 \(\times\) 10\({}^{5}\) AU [19]. The mentioned study also reported the different VOC levels affected by SE treatment severity when milder conditions (180 \({}^{\circ}\)C / 5 min) resulted in higher levels of terpen and aldehydes, whereas the most severely treated (200 \({}^{\circ}\)C / 5 min) material emitted higher levels of furans. The commercially produced particleboard sample had a significantly different qualitative profile of the VOCs with terpenes (pinene, sylvestrene, and verbenone), hexanal, and the pleasantly odorous aldehyde nonanal, as the main chemical constituents (Table 5 and Figure 2D), which generally arise from the resins of the softwood particles used for the board production [2; 11]. The suberinic acid-bonded particleboard was similar to the previously described binder-less boards, with acetic acid and furans as the main VOCs, arising from the hemicelluloses of the biomass, but also with some compounds present, which were more typical for the commercial sample-hexanal and terpenes. It has been proven that the furfural level in SA-PB is highly dependent on the binder preparation parameter; namely, the pH 3 of the binder (pH 2 was for the binder used to produce SA-PB tested in this study) resulted in the highest furfural level [29]. Despite the variety of chemicals emitted from the SA-PB sample, the overall VOC concentration was significantly lower than the binder-less boards, which is explained by the untreated wood particles and the environmentally friendly binder. Figure 3 illustrates the primary chemical compound structures detected in the board samples--organic acids; aliphatic and cyclic carbonyls; furans, which arise from the decomposition of hemicelluloses; aromatic compounds and terpenes, which are more typical extractives. Acetic acid and aldehydes are considered the most odorous and most likely to be perceived by people through olfaction, causing sensory irritation [30]. Terpenes and aldehydes are known to be irritants and pose various health risks [2].
# Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
## 1 Introduction
When using the shape of the finger to estimate the force, the most common method is to formulate a model-based approach and use analytical techniques to estimate the force. Some research approximates the finger as a chained-beam constraint model (CBCM) [20]. CBCMs are used to model and discretize large deflections in compliant mechanisms [23]. Modeling soft grippers in this way is effective at low numbers of chained beams but loses accuracy as the chain grows. Other work, such as [19], uses an array of deflection sensors to measure the deformation of the gripper finger and map the values into an estimated force. Others use pseudo-rigid body models to approximate the finger as a group of connected four-bar mechanisms and estimate the stiffness of the joints using finite element modeling, combining the two solutions mentioned above [24]. Despite research on soft robotics, only a few studies have implemented closed-loop force control of a compliant gripping mechanism, with many applications using an on-off approach to grasping, which fails for fragile objects [25]. Existing solutions to the closed-loop control problem include robust control of continuum and soft robots to handle uncertainties in modeled dynamics [26]. As a result of the difficulty of control for many types of soft robotics, a standard solution is to operate the robot in open-loop mode, using finite element methods to pre-compute the desired position [27]. While soft robots are a quickly emerging field, most open-loop and closed-loop control methods are often designed for and implemented to solve position and shape control problems [28]. When force control is implemented, it is frequently done by indirectly controlling a separate feature, such as pressure control. Control methods for these implementations are commonly done usingstrategies such as PID or model predictive control [27; 29]. As many robot manipulation processes require efficient and reliable force control, filling this research gap is vital. In summary, this study aims to investigate a method for noninvasive force sensing using visual methods and to close the loop in the force control problem of a flexible robotic gripper. Accurate force feedback from visual sensing of soft gripper fingers can be used in many manipulation applications, from handling food products to standard pick-and-place operations. Integrating visual force sensing and adaptive control into a soft robotic system will contribute to the knowledge of force control for soft robotics. It will aid in developing versatile and efficient control strategies for soft grippers, capable of performing reliable grasps on a wide range of target objects.
# Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
## 3 Results and Discussions
### Performance Comparison with Other ML Models
severe conditions were considered, the GPR model achieved slightly better performance than the two other models. It is worth mentioning that the GPR technique is often preferred over supervised learning techniques such as SVM and RF in situations where the dataset is small or noisy, and high prediction accuracy is required. This is because GPR is a non-parametric model that can handle complex input-output relationships without imposing strong assumptions about the underlying data distribution. Moreover, GPR can provide probabilistic estimates of the output variable, making it particularly useful in situations where uncertainty estimates are required. Additionally, GPR can handle high-dimensional input spaces and limited training data more effectively than SVM and RF models.
## 3 Methodology
### System Development and Technology Realization
#### 3.4.6 Population Replacement  
The \"Generate Next Generation\" button in the program can be used to achieve the genetic generation operation of the population after the completion of the scheme score. This process is based on fitness evaluation, combined with elite strategy, cross-recombination, and mutation mechanism to generate a new generation of populations, providing iterative support for optimizing color schemes. The specific process is as follows:  
1. Elite Strategy: Survival of the Fittest and Retention Mechanism  
The program uses the elite strategy to retain high-fitness color schemes for the next generation. The user can set a fitness threshold, and schemes with scores above the threshold are directly retained without undergoing crossover and mutation, ensuring that excellent genes are not lost. Schemes that do not meet the threshold are eliminated and replaced by newly generated offspring.
2. Crossover Recombination: Generating Offspring  
As shown in Figure 13, the eliminated schemes generate offspring through crossover recombination. The roulette selection method is used to choose parent schemes based on fitness, with higher-scoring schemes having a greater probability of being selected. The crossover operation ensures that offspring retain characteristics from their parents while introducing randomness, promoting the optimization process. Each color is randomly selected for inheritance from the parents, ensuring the transmission of genetic traits and diversity, which leads to the generation of potentially better color schemes.  
Figure 12: Scheme scoring interface.  
Figure 13: Crossover and recombination.  
Variation mechanism: Introducing randomness  
In the resulting offspring, some schemes undergo variation to increase population diversity (Figure 14): the user can set the proportion of variation (the default value is 30%); that is, about 30% of schemes in the new population will undergo variation. Variation is achieved by introducing a random variation to the RGB value of the color, and the amplitude of the variation is limited by the maximum variation ratio (the default maximum variation is 30%). The randomness of variation introduces new possibilities to the optimization process and helps to escape local optimality.  
4 Population renewal and display  
After selection, crossover, and mutation, a new generation of the population is generated, including the retained elite schemes and newly generated offspring schemes. The user can view the new schemes by clicking the \"Generate Next Generation\" button, and the program will display the color schemes in a grid format. After the new population is generated, the user can continue with scoring, elimination, and replacement, with the program continuously optimizing to achieve iterative scheme improvement. The elite strategy ensures the transmission of excellent schemes, crossover recombination brings new combinations, and the mutation mechanism maintains diversity until a satisfactory color scheme is generated.
## 4 Literature Review: Other Research on CWSN
### Energy Efficiency
#### 4.2.1 Energy Efficiency in Sensing  
Energy efficient spectrum sensing technique is a basic requirement for the CR-WSNs. [PERSON] _et al._[79] proposed a low-power multi resolution spectrum sensing (MRSS) architecture for CR-WSNs. Unlike the conventional MRSS scheme, which consumes considerable power and is comprised mainly of analog circuits in which a sensing bandwidth and sensing sensitivity are altered by an analog variable filter, the scheme proposed in this paper carries out signal processing in a digital domain and can detect occupied frequency bands at multiple resolutions and with low power consumption.
## 4 Discussions  
The intelligent glaucoma detection system played the role of an essential tool used to monitor the progression of glaucoma disease more objectively and reliably. This intelligent system was able to assist medical personnel in recognizing glaucoma symptoms faster and more cheaply. Currently, several research undertakings have been performed, aiming to aid in the development of an automated glaucoma detection system. Almost all of those research works used optic discs and optic cups as input data to detect the presence of glaucoma. However, utilizing the optic disc and optic cup as a major object to diagnose glaucoma was extremely risky due to the differences between the optic disc and optic cup of each patient. This condition often leads to misinterpretation.  
The ORIGA-Light dataset was one of the most popular glaucoma datasets that was often used by several researchers. The ORIGA-Light dataset had a variety of data that was quite complex. It was proven by previous results, as illustrated in Table 4, though it could not easily achieve an accuracy of more than 90%. Regarding that problem, we offered an alternative way of evaluating glaucoma disease. We considered analyzing RNFL instead of using an optic disc and optic cup, due to their uncertain standard of size.  
We compared our proposed method with previous works that used the same dataset for glaucoma classification purposes. Table 5 illustrates the comparison result of our proposed method to other different ways of glaucoma classification using the ORIGA-Light dataset. According to Table 5, it can be recognized that our proposed solution successfully outperformed several previous studies, with an accuracy of 92.88% and an AUC of 89.34%.  
As demonstrated in Tables 4 and 5, we can conclude that our proposed solution successfully outperformed the previous research works. Hence, it can be inferred that analyzing RNFL was more suitable for recognizing the presence of glaucoma than using an optic disc and optic cup, particularly given the complex dataset. This result also proves the medical theory [24] that measuring the thickness of RNFL is an objective and reliable way to monitor the progression of glaucoma disease. Therefore, it is expected that our proposed model could help improve eye disease diagnosis and assessment.
## 2 System Model
### Problem Formulation  
In the downlink transmission, the SINR of the \(k\)th GU can be expressed as  
\[\text{SINR}_{k}=\frac{\left|\sum\limits_{l=1}^{L}\mathbf{h}_{k,l}^{H}\mathbf{ A}_{i,l}\mathbf{G}_{l}\mathbf{f}_{k,l}\right|^{2}}{\sum\limits_{l=1}^{L}\sum \limits_{i=1}^{K}\mathbf{h}_{k,l}^{H}\mathbf{A}_{i,l}\mathbf{D}_{l}\mathbf{A}_ {i,l}^{H}\mathbf{h}_{k,l}+\sum\limits_{i\
eq k}\left|\sum\limits_{l=1}^{L} \mathbf{h}_{k,l}^{H}\mathbf{A}_{i,l}\mathbf{G}_{l}\mathbf{f}_{l,l}\right|^{2}+ \sigma^{2}}. \tag{14}\]  
The data rate of the \(k\)th GU can be expressed as  
\[R_{k}=\log_{2}(1+\text{SINR}_{k}). \tag{15}\]  
Therefore, the corresponding sum rate is given by \(R_{sum}=\sum_{k=1}^{K}R_{k}\).  
The EE is defined as the ratio of the weighted sum rate to the total power consumption. The total power consumption is expressed as \(P_{cons}=LP_{cons}+LP_{c}+LP_{0}\) in our proposed system, where \(P_{cons}=1/\xi_{\max}\sum_{l=1}^{L}\mathbb{E}\left\{\left\|f(\mathbf{F}_{l} \mathbf{s})\right\|^{2}\right\}\) denotes the power consumed by the NPAs with the maximum operating efficiency \(\xi_{\max}\). In addition, \(P_{c}\) denotes the power consumption of the circuit including elements such as the local oscillator, baseband digital precoder, RF chain, etc., and \(P_{0}\) denotes the basic power consumption such as power supply and cooling of the satellite.  
Circuit power consumption \(P_{c}\) is expressed as \(P_{c}=N_{RF}P_{RFC}+P_{LO}+P_{BB}+NP_{SW}+NP_{PS}\), where \(P_{RFC}\) denotes the power consumption of each RF chain, including a digital-to-analog converter (DAC), mixer, and filter. \(P_{LO}\), \(P_{BB}\), \(P_{SW}\) and \(P_{PS}\) denotes the power consumption of the local oscillator, baseband digital precoder, each phase shifter switch, and each phase shifter, respectively.  
In this paper, we seek to maximize the EE within the constraints of the average total transmit power per satellite. The optimization problem of the EE maximization can be expressed as [28]  
\[\begin{array}{l}\max\limits_{\mathbf{F}}\frac{B_{w}R_{sum}}{p_{total}}\\ \text{s.t.}\ \mathbb{E}\Big{\{}\|f(\mathbf{F}_{l}\mathbf{s})\|^{2}\Big{\}} \leq P,\forall l\in\{1,2,\cdots,L\},\end{array} \tag{16}\]  
where \(B_{w}\) is the bandwidth, \(P^{total}\) is the total system power consumption and \(P\) denotes the limit on the average total transmit power per satellite. Additionally, we have  
\[\mathbb{E}\Big{\{}\|f(\mathbf{F}_{l}\mathbf{s})\|^{2}\Big{\}}=\text{Tr}\Big{[} \left(\mathbf{G}_{l}\mathbf{u}_{l}+\mathbf{d}_{l}\right)\left(\mathbf{G}_{l} \mathbf{u}_{l}+\mathbf{d}_{l}\right)^{H}\Big{]}=\text{Tr}\Big{\{}\Big{[} \mathbf{G}_{l}\mathbf{U}_{l}\mathbf{G}_{l}^{H}\Big{]}+\left[\mathbf{D}_{l} \right]\Big{\}}. \tag{17}\]
###### Abstract  
Acceptance by the construction industry of recycled concrete as a sustainable alternative material is contingent upon a reliable assessment of its permeability to corrosive agents. This study analyses the transport mechanisms associated with chloride (Cl\({}^{-}\)), oxygen (O\({}_{2}\)) and carbon dioxide (CO\({}_{2}\)) ions in concrete with cement made with 10% or 25% ground recycled concrete (GRC) separately or in combination with 50% mixed recycled aggregate (MRA). The findings show that, irrespective of aggregate type, concrete with GRC exhibited lower resistance to ingress than conventional concrete due to its greater porosity. Nonetheless, O\({}_{2}\) permeability was consistently below 4.5 \(\times\) 10\({}^{-17}\) m\({}^{2}\) and CO\({}_{2}\) penetration, under 4 mm/year\({}^{0.5}\), indicative of concrete with high quality. Resistance to CO\({}_{2}\) and Cl\({}^{-}\) penetration in the materials with 10% GRC was similar to the values observed in conventional concrete. On the other hand, the incorporation of 25% GRC increased the penetration of CO\({}_{2}\) and Cl\({}^{-}\) by 10% and 38%, respectively. Further to those findings in normal carbonation environments, reinforcement passivity would be guaranteed in such recycled materials over a 100 year service life.  
recycled aggregate; recycled concrete; durability; chloride penetration; carbonation +
Footnote †: journal: Applied Sciences
## 4 Agro-Food Substrates for Anaerobic Digestion
### Fruits and Vegetables Waste
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **pH** & **TS\%** & **VS (\%TS)** & **C/N** & **BIOGAS YIELD** \\ \hline Wheat waste & & & & & & \\ (straw, bran) [103; 104] & - & 88.7\(\div\)91.0 & 88 & 87 & - \\ \hline Barley straw & 7.87 & 89.17\(\div\)90.5 & 94.04\(\div\)94.3 & & \begin{tabular}{} \\ 229 L methane/kg VS \\ \end{tabular} \\ \hline Corn residue [105; 106; 107] & 5.05 & 81.8\(\div\)93.02 & 94.65\(\div\)97.50 & 51\(\div\)57 & \begin{tabular}{} \\ 317 L methane/kg VS \\ \end{tabular} \\ \hline Rice straw [105; 107] & 6.2\(\div\)8.14 & 88.7\(\div\)93.49 & 76.02\(\div\)91.9 & 50\(\div\)70.88 & \begin{tabular}{} \\ 195 L methane/kg VS \\ \end{tabular} \\ \hline Boiled rice [108] & - & 35 & 99 & 25.56 & 0.29 m\({}^{3}\) methane/kg VS \\ \hline Apple scraps [109] & - & 25 & 90 & 50.5 & - \\ \hline Fresh cabbage residue [108] & - & 5 & 84 & 9.73 & 0.28 m\({}^{3}\) methane/kg VS \\ \hline Tomato seeds and skin [105] & 4.7 & 32 & 97.8 & \begin{tabular}{} \\ 218 L methane/kg VS \\ \end{tabular} \\ \hline Sugar cane & & & & & \\ bagasse [106] & - & 93.51 & 83.91\(\div\)89.72 & 20.37 & \begin{tabular}{} \\ 363 m\({}^{3}\)/ton VS \\ \end{tabular} \\ \hline Tea residue [106] & - & 90.96 & 86.64 & - & \begin{tabular}{} \\ 385 m\({}^{3}\)/ton VS \\ \end{tabular} \\ \hline Grape marc [105; 109] & 3.58\(\div\)4.40 & 20.44\(\div\)60.90 & 88.66\(\div\)98 & 18.57\(\div\)29.23 & \
\begin{tabular}{} \\ 98–116 L methane/kg VS \\ \end{tabular} \\ \hline Brewery spent grain & & & & \\ [13;110; 111] & 5.08 & 21.1\(\div\)30 & 84\(\div\)96.5 & 11.25\(\div\)25 &
\begin{tabular}{} \\ 392–491 L/kg VS, \\ 187–273 L methane/kg VS \\ \end{tabular} \\ \hline Spent coffee grounds & & & & & \\ [112; 113] & - & 32.37\(\div\)95.88 & 92.76\(\div\)93.84 & 35.3 & - \\ \hline Coffee cut-stems & & & & & \\ [113] & - & 88.56\(\div\)95.88 & 92.76 & - & - \\ \hline Palm oil mill effluent & & & & & \\ [114] & 7.74 & 6.73 & - & - & - \\ \hline FVW (from & & & & \\ supermarket) [100] & 4.66 & 19.54 & 96.21 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Characteristics of some fruit and vegetable waste (FVW).