esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurement/Calibration of \(R_{s}\)
### Laboratory Calibration
#### 4.1.3 Continuous Flow Design  
The aim of continuous flow design was to prevent depletion of aqueous solution by a continuous supply of freshly contaminated water to the exposure chamber, where multiple samplers were deployed under identical hydrodynamic conditions and the same aqueous concentration [87; 112]. During the calibration experiment, the sorption of dissolved organic or inorganic matter should be negligible in order to avoid overestimating the concentration of analytes in solution. Stable concentrations in solution might be maintained if the flushing rate could be made up for the total sampling rate of all samplers [16; 115]. The samplers were gradually removed from the exposure chamber, followed by eluting the sorbent to assess the \(R_{\mathrm{s}}\) of analyte. It should be noted that with decreasing sampler numbers, the flushing rate might be reduced if the hydrodynamic condition is kept constant [65; 116]. When aqueous concentrations are maintained constant, the relationship of \(R_{\mathrm{s}}\) and \(C_{\mathrm{s}}(t)\) might be fitted by Equation (4). If aqueous concentrations are unstable, a second-order polynomial function in time (Equation (17)) could be used to describe the concentration change in sorbent [87].  
\[\frac{C_{\mathrm{s}}(t)}{K_{mw}}=\bigg{(}C_{0}-\frac{C_{1}}{k_{e}}+\frac{2C_{2 }}{k_{e}^{2}}\bigg{)}[1-\exp(-k_{e}t)]+\bigg{(}C_{1}-\frac{2C_{2}}{k_{e}} \bigg{)}t+C_{2}t^{2} \tag{17}\]  
where \(C_{0}\), \(C_{1}\), and \(C_{2}\) are the constants of polynomial function.
### Urban Resilience: Spatial/Social Resilience Perspective  
As the concept of resilience is still unclear, especially in urban and social sciences [5], urban and social researchers are stressing its importance in science and practice as well as its strong connection to sustainability [6]. In the context of our research, resilience refers to a gentrification node's ability to keep its clientele's socioeconomic class affecting its spatial identity and form. The relation between social, spatial, and urban resilience here is undeniable. More explicitly, the metamorphosis of social morphology generates a transformation of urban morphology [4]. So, sustainability is an actor of social resilience and vice versa, which is about the ability of a particular community to resist the rupture in its socioeconomic morphology. Therefore, maintaining a gentrification node's social/spatial and urban character despite changes in the uses that this node host is the goal of spatial/social resilience. Moreover, the more resilient the gentrification node is, the more it will keep the socioeconomic morphology of its customers and thus its spatial morphology.  
Originally, \"resilience\" was defined in Merriam Webster as \"the capability of a strained body to recover its size and shape after deformation caused especially by compressive stress\" and as \"an ability to recover from or adjust easily to misforture or change\" [7]. In the Cambridge dictionary, \"resilience\" is \"the ability to be happy and successful again after something difficult or bad has happened\" [8]. In 2002, \"social resilience\" term appeared with various definitions, especially regarding communities. Sometimes, it is related to ecological resilience, especially when a social group is dependent on environmental and ecological resources [9]. Conversely, in other cases, the term was more related to social behavior. More explicitly, resilience was associated with the socioeconomic status of the population in their communities. It was linked to the domination of a particular social group or a specific class of clientele and the exclusion or marginalization of others [4].  
Urban systems, however, will be considered resilient if they are robust and can adapt their uses or transform themselves to be able to subsist as capable functioning systems after disturbances or instabilities [5]. Adaption does not necessarily mean spatial or social transformation. It is about how the urban system will recognize and reorganize itself in the face of disruption while keeping its identity. The social/spatial resilience of gentrification nodes as complex urban systems plays a vital role in the sustainability of new urban communities, thus their resilience. Users tend to migrate to new places where \"it is trendier\" to maintain their social validation [10]. This is why new urban anchors constantly suffer from the change of their spatial identities that follows a change of the social morphology of their users [1].
# Application of Three-Dimensional Porous Aerogel as Adsorbent for Removal of Textile Dyes from Water
###### Abstract  
The textile industry is one of the most important industries in the European Union. The main environmental problems of the textile industry are the high water consumption, the generated pollution, the variety of chemicals used and the high energy demand. Recently, adsorbents with a large specific surface area and low weight, such as aerogels, have attracted great interest as promising materials for removing dyes from polluted water. Cellulose aerogels are inexpensive and non-toxic. Langmuir and Freundlich isotherms were chosen as the best method to describe the performance of the adsorbent. In this study, the adsorption efficiency of Congo red, Naphth green B, Rhodamine B and Methylene blue were determined by using an adsorbent synthesized from paper and cardboard waste. The total organic carbon concentration was chosen as an indicator of the concentration of the dyes in the solutions. The aerogel capsules had 5% cellulose content. It was found that the adsorption capacity of the aerogel in the solutions of Congo red varied from 0.028 mg/g to 14.483 mg/g; in the solutions of Naphth green B, from 0.013 mg/g to 7.698 mg/g; in the solutions of Rhodamine B, from 0.020 mg/g to 8.768 mg/g; and in the solutions of Methylene blue, from 0.024 mg/g to 13.538 mg/g.  
adsorption; aerogel; cellulose; dyes; kinetic models; linear and nonlinear regression; modeling of adsorption equilibrium; textiles +
Footnote †: journal: Applied Sciences
## 1 Introduction
On the other hand, RGB LEDs are utilized to enhance the communication distance for OCC [13]. Nonetheless, to the best of our knowledge, no researchers have briefly examined the OCC performance emphasizing on the effects of the LED and camera parameters. In this study, we provide a comprehensive discussion of these parameters that affect OCC performance. We emphasize cameras using the rolling shutter technique, as these types are very popular, cost-effective, and widely available. Our main contributions in this paper are summarized as follows:  
* We briefly explore the role of LED and camera parameters in determining OCC performance. Variations in these parameters lead to our insights on variations in the OCC performance. We focus on key issues related to the quality of service, including data-rate enhancement, SINR improvement, increasing the communication distance, and decreasing BER. * We present a channel model for OCC based on Lambertian radiant intensity [14]. The channel model is also used as the basis for our analysis of pixel SINR. * We provide a theoretical representation of OCC users' satisfaction. * We conducted a short survey of existing modulation schemes and categorize them according to communication distance and data-rate characteristics in order to enable us to select the appropriate scheme for a specific application. * We present simulation results (including the outage probability analysis for OCC) that demonstrate which values of the parameters are optimal to achieve the required OCC output for performance improvement. The remainder of this paper is organized as follows. Section 2 provides an overview of the OCC data decoding principle and system parameters that include the OCC channel model and representation of pixel SINR. In Section 3, we outline the analysis of parameters that determine the performance of an OCC system. Section 4 presents a short survey of existing techniques to modulate the OCC transmitter. An analysis of the OCC user satisfaction is presented in Section 5. Simulation results involving OCC performance variations regarding changing LED and camera parameters are evaluated in Section 6. Finally, a brief summary of our work and possible future research are provided in Section 7.
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 3 Overview of Self Organized Fast Routing Protocol (SOFRP)
### Proposed Protocol Overview
Assume that the maximum distance between two nodes is 1000 m. If the maximum packet size is 16 bytes and the channel data rate is 5 kbps, then the time interval is \(0.666+0.0256=0.6916\) s (approx.). If we consider a number of 40 timeslots, they would start at instants 0, 0.6916, 1.3832, 2.0748, 2.766,..., 26.972 s. The total of 40 timeslots would last for 27.664 s, which would be the WP value. In this configuration, the probability of two nodes selecting the same timeslot out of 40 timeslots is 2.5%. No matter how large the number of timeslots, the chances of collision are always there. Different types of transmission modes have been used to optimally achieve delivery reliability and energy efficiency. There are four modes of transmission named mode1, mode2, mode3 and mode4. 1. mode1: the packet is transmitted once without any acknowledgement. 2. mode2: the packet is transmitted three consecutive times without any acknowledgement. The interval between transmissions is equal to one timeslot (TS). 3. mode3: all the candidate nodes send a packet within a period equal to the wait period. Then, acknowledgements are sent during the next WP. Hence, the total time for sending a packet and receiving its acknowledgment is 2*WP. This process of sending a packet and receiving its acknowledgment may repeat two more times in case of packet loss. Therefore, the overall period for the three transmissions remains fixed (which is 6*WP) whether the number of transmissions is one, two or three. mode4: this mode is a little bit different from mode3 in terms of packet retransmissions. In mode4, the packet and its acknowledgement are transmitted three times, even if the packet was successfully delivered in the first or the second transmission. The main novelties of this protocol are:  
1. It is designed to recreate the physical topology in the logical routing tables, creating the shortest possible paths,
2. The algorithm uses the knowledge of the topology to avoid collisions,
3. It helps to balance traffic among nodes that are the same distance from the gateway,
4. Our proposal self-adapts to different network sizes and number of nodes. Together, these characteristics help to save as much energy as possible, and minimize the cost, in time and computational effort, of forwarding a packet.
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 3 Method and Algorithm
### Paillier Federated Network
#### 3.4.3 Architecture of the Paillier Federated Network  
Here, we use Paillier encryption to protect the gradient data. Thus, even if crackers compromise the computing server, they cannot know the specific information of the gradient data from each learning client. In addition, it is impossible for crackers to use these encrypted gradient data to train shadow models.  
Since Paillier encryption requires key pairs, in order to generate and manage key pairs, we add a key management center (KMC) in the algorithm. The Paillier federated network is shown in Figure 4. It includes KMC, computing server, and several learning clients.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 2 Identifying the Distinctions between S-MPC and AR-RNN-LSTM
### Peak Performance  
In S-MPC, the quality of the optimization solution depends on the controller model's precision, which is frequently simplified for computational purposes. Stability and practicability are intrinsically ensure for S-MPC, whereas there is only an immature theory for these issues in AR-LSTM [72]. The absence of safety guarantees in AR-LSTM results from the constraints not being directly imposed in the formulation of the solution method. The optimality of the S-MPC solution depends on the accuracy of the model used to predict the system's behavior and the optimization algorithm's ability to find the optimization problem's global optimum. If the model is inaccurate or the optimization algorithm fails to find the global optimum, the performance of the S-MPC controller may not be optimal.  
The optimality of AR-LSTM relies on several factors, including the network's architecture, the training optimization algorithm, and the complexity of the task being performed. AR-LSTM is capable of achieving high levels of performance on a wide variety of sequential data processing tasks, such as language modeling, machine translation, and speech recognition. AR-LSTM is able to model complex temporal dependencies in sequential data, which is one of its main advantages. The ability of AR-LSTM to incorporate feedback loops enables them to capture long-term dependencies that would be challenging to represent using other models, such as a gated recurrent unit (GRU). In addition, the ability to incorporate memory into the network via mechanisms improves the performance of RNNs in tasks requiring long-term memory. Nonetheless, several factors can restrict the optimality of AR-LSTM. One difficulty is the issue of vanishing and exploding gradients, which can hinder the network's ability to discover long-term dependencies. This issue can be mitigated by employing specialized units, such as LSTM and GRU, and optimization algorithms designed to deal with these issues. Another issue is overfitting, which can occur when the model becomes excessively complex and begins to fit the noise in the data rather than the underlying patterns. This can be remedied by employing regularization techniques such as early stopping and dropout [73].
# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 5 Experiments
### Experimental Results  
To assess the performance of our PCP Network, we compared the results of our method with those of six current mainstream bottom-up pose estimation methods, including CMU-Pose [12], Mask-RCNN [5], G-RMI [6], AssocEmbedding [13], PifPaf [15], PersonLab [14], and HigherHRNet [16]. Table 1 summarizes the experimental results on the test-dev dataset. The differences between HigherHRNet-1 and HigherHRNet-2 are the backbone and input size. As shown in Table 1, our method is slightly inferior to PersonLab and HigherHRNet-2, which both use a more powerful backbone and larger training images. However, when using the same backbone and same input size, the performance of our method is better than Mask-RCNN, G-RMI, AssocEmbedding, PifPaf, and HigherHRNet-1. In addition to performance, we also consider the inference time of each method.  
As shown in Table 1, the speed of our PCP Network is outstanding, especially when DLA is used as the backbone. Even with the HRNet backbone, the inference speed of our PCP Network was 5\(\times\) faster than that of PersonLab. These results verify that our method has superior efficiency due to its excellent inference speed while maintaining very competitive performance for multi-person pose estimation tasks.  
To further prove that the performance of the proposed method is satisfactory, we also show some results from the proposed method that show intuitively that our approach is able to identify joints on a human skeleton accurately. Figure 5 shows qualitative examples from the MSCOCO dataset, including the intermediate body joint heatmaps and final predicted human poses. It is clear that our method performs well even on scenes with some challenging attributes such as sub-optimal scale, appearance variation, occlusion, or crowding.
## 3 Background and Theory
### Cross-Validation Metrics  
The cross-validation accuracy was used to evaluate the model's performance [34]. This method helps determine how well a machine learning model is likely to perform. The process involves splitting the dataset into \(k\) parts or folds. For example, let us say we have a dataset \(D\), divided into \(k\) folds, with each fold labeled as \(D_{i}\), where \(i\) ranges from \(1\) to \(k\). For each fold, the model is trained on the rest of the dataset, excluding \(D_{i}\), and then tested on \(D_{i}\) to calculate a performance score, \(S_{i}\). The overall performance is then determined by averaging the scores across all folds, as shown in the formula below (26):  
\[CV=\frac{1}{k}\sum_{i=1}^{k}S_{i} \tag{26}\]  
where the following apply:  
* \(CV\) is the cross-validation score;
* \(k\) is the number of folds;
* \(S_{i}\) is the performance score obtained when using the fold \(D_{i}\) as the test set and the remaining \(k-1\) fold as the training set (\(S_{i}\) can be accuracy, which is used in this study, precision, recall, F1 score, mean squared, etc.).
## 2 Fundamental Theories
### SVM Classifier Parameters Tuning
#### 2.2.2 Particle Swarm Optimization Algorithm  
In the original PSO algorithm, there are \(N\) particles in the population, and each particle \(i\) has a position vector \(X_{i}=(x_{i1},x_{2},\ldots,x_{iD})\) and a velocity vector \(V_{i}=(v_{i1},v_{i2},\ldots,v_{iD})\) to calculate its current state, where D is the dimensions of the solution space. First, the initial positions and velocities of the particles are randomly initialized in the search area, where the position of each particle represents a solution. The fitness value \(f_{i}\) can be calculated by substituting the vector \(X_{i}\) into the target function. Then by comparing the fitness value of each particle, the position with the best fitness value is the optimal position of the current population. At last, in the \(t+1\) iteration of the particle, the positions and velocities of the particles are updated according to Equations (8) and (9).  
\[V_{g+1,i}=\omega V_{g,i}+c_{1}r_{1}\big{(}P_{g,i}-X_{g,i}\big{)}+c_{2}r_{2} \big{(}P_{g}-X_{g,i}\big{)} \tag{8}\]  
\[X_{g+1,i}=X_{g,i}+V_{g+1,i} \tag{9}\]  
where \(w\) is inertia weight to control the impact of the previous velocity. \(c_{1},\ c_{2}\) are the learning factors, and \(r_{1},\ r_{2}\) are random numbers in \([0,1]\) for denoting remembrance ability. \(V_{g,i},X_{g,i}\) represent the \(g\)-thgeneration velocity and position of the i-th particle respectively, \(P_{g,i}\) is the optimal position that the \(i\)-th particle has searched so far, and \(P_{g}\) is the historical optimal value searched by the whole population.
## 4 Establishment and Validation of Numerical Simulation Model for a Linear Heat Source  
### Establishment of Numerical Simulation Model  
Two-dimensional finite element models require setting an equivalent element length, and unreasonable equivalent length settings may have a certain degree of impact on the calculation results. Such an impact can easily cause misunderstandings and confusion among other scholars. In contrast, the calculation results of three-dimensional models  
Figure 1: Layout illustration of dock bank slope erosion and deposition monitoring device.  
have good consistency and stability, which can effectively avoid this issue. Therefore, in our research process, a three-dimensional model was adopted to establish a finite element model of the soil medium and the linear heat source [18; 19]. At the same time, taking into account both the mesh accuracy and element type sensitivity analysis [20; 21; 22], the finite element model was established with the following parameters: 40 circular node layouts, radial node spacing from 0.1 \(r\) to 10.0 \(r\), axial node spacing of 5.0 \(r\), and the element type was the eight-node linear heat transfer hexahedron element (DC3D8).  
In the finite element model, the diameter of the soil medium is represented as \(D_{soil}\) = 5.0 m, its height as \(H_{soil}\) = 10.0 m, the radius of the linear heat source as \(r\) = 0.05 m, the length of the heat source in the soil as \(l_{soil}\) = 4.0 m, and the length of the heat source in water as \(l_{water}\) = 1.0 m. Here, the linear heat source has a density \(\rho\) = 7850 kg/m\({}^{3}\) and specific heat capacity \(c\) = 460 J/(kg\({}^{\circ}\)C) [23]. The seawater environment temperature is \(T_{\infty}\) = 25 \({}^{\circ}\)C, seawater density is \(\rho\) = 1020 kg/m\({}^{3}\), seawater specific heat capacity is \(c\) = 4200 J/(kg\({}^{\circ}\)C), and seawater convective heat transfer coefficient is \(h\) = 1500 W/(m\({}^{2.\circ}\)C) [24]. The environmental temperature \(T_{\infty}\) for seawater with either viscous or non-viscous soil is 25 \({}^{\circ}\)C. The soil has a thermal conductivity of \(\lambda\) = 0.25 J/(m\({}^{.\circ}\)C\(\cdot\)s) [25], a density of \(\rho\) = 2000 kg/m\({}^{3}\), and a specific heat capacity of \(c\) = 820 J/(kg\({}^{.\circ}\)C). The established finite element geometric model and mesh model are shown in Figure 2.
## 3 The Biosynthesis of Artemisinin (Qinghaosu)
### Plant Tissue Culture  
The production of artemisinin from plant tissue cultures of _A. annua_ has been reviewed [342]. Although artemisinin can be obtained in reasonable quantities from differentiated shoot cultures [343-345], it is produced in only small or undetectable amounts [346-348] by callus or suspension cell cultures of _A. annua_. Instead, the chemistry of undifferentiated callus and suspension cultures of _A. annua_ seems to be dominated by sterols, such as stigmasterol (**570**) and sitosterol (**568**) (Section 2.7.2) [86,313]; as well as phenylpropanoids, such as scopoletin (**184**) (Section 2.3; Table 17), all of which are also found in significant quantities in the parent plants [86,313]. Other phenylpropanoids, such as 4-methoxycinnamaldehyde (**579**), coniferaldehyde (**580**), (2-glycerol)-_O_-coniferaldehyde (**581**) [87], (2-propenal)-_O_-coniferaldehyde (**582**) [87] and balanophonin (**583**) [87] are not yet known from _A. annua_ plants themselves (Figure 4). The triglyceride, glycerol 1,2-di-9-octadecenoate 3-octadecanoate (**584**), with some structural similarities to 9-octadecenoic acid, 2,3-dihydroxypropyl ester (**82**) (Table 5), has also been obtained from plant tissue culture [86]. These major alterations observed in the metabolism of undifferentiated callus and suspension cultures may reflect the absence of glandular trichomes, in which much of the biosynthesis of the mono- and sesquiterpenoids which are described in Sections 2.5 and 2.6 is thought to occur.  
Much of the current research activity into the production of artemisinin by plant tissue culture revolves around hairy root cultures of _A. annua_[349] which have been transformed by _Agrobacteriumumefaciens_[350,351] (elicitors from endophytic fungi - see the next section - have also been employed in _A. annua_ plant tissue cultures [202,352,353]). Correlations have been observed between peroxidase activity and artemisinin levels in hairy roots [354], although it has also been suggested that a high peroxidase activity in cell cultures may be partly responsible for their very low artemisinin contents observed [355].