esa-sceva/satcom-chunk-collection huggingface dataset:
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 5 System Evaluation
### Network Performance Simulation  
We also simulate the network throughput of the multi-UAV-based network infrastructure using the formation control algorithm. Since there is no existing method considering the formation of the UAVs of the network infrastructure, we compare the two cases where one applies \(F_{coneq}\) and the other does not apply \(F_{coneq}\). The comparison is valid since the formation without \(F_{coneq}\) shows hexagonally patterned deployment of the UAVs, which is widely used in the placement of the wireless network infrastructure [39]. We firstly deployed the mobile devices with a cluster centered at \((-100,100)\) in the map, and let the devices move in the direction \((1/\sqrt{2},-1/\sqrt{2})\), as shown in Figure 8(a). The devices start to move at 50 s from the start of the simulation, and stop to move at 70 s. The devices send packets to the GCS with 100 Kbps after the 20 s from the start of the simulation, and the network throughput is measured per second. Figure 8(b) shows the changes of the network throughput with respect to the time for two cases.  
The simulation evaluates two aspects: the network throughput enhancement and the time-variant flexibility of the formation shaped by the algorithm. Figure 8(b) shows that our formation control algorithm with \(F_{coneq}\) outperforms the network throughput at the whole time, except for the short intervals such as 25 to 26 s and 72 to 73 s. The reason of the temporal drop was the large number of the AP connection changes in a short period due to the change of the formation, which leaded to the update of the routing path. After the short interval, the network throughput is recovered and outperforms the case without \(F_{coneq}\) again. The simulation verifies that the algorithm recognizes the dynamic location changes of the mobile devices by the exchange of the neighbors' network status, and changes the formation of the UAVs to enhance the network throughput in real time. By the calculation of the average network throughput of 10 repetitions, the case with \(F_{coneq}\) results in 41.2% better performance than the case without \(F_{coneq}\) on average. Since the case without \(F_{coneq}\) does not  
Figure 8: Mobile device experiment scenario and the performance evaluation.  
change the formation of the UAVs in time, the case is the representation of the deployment of the location-fixed network infrastructure, and then difference to the case with \(F_{coneq}\) is the profit obtained by the proposed algorithm addressed in Section 4. Through the simulation, we verified that the proposed formation control algorithm enhances the network throughput of the multi-UAV-based network infrastructure on the fluctuation of the terrestrial network requirement.
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 5 Experiments
### Uncertainty Analysis
Furthermore, note that the world coordinate system here usually is not the coordinate system desired, the uncertainty of the corresponding Euclidean distance for \(\Delta P_{w}\) is calculated, which is generally more available. For the calibrated system, consider a measured region where Length \(\times\) Width \(\times\) Height (\(L\times W\times H\)) is \(20\times 40\times 1\) mm\({}^{3}\), in which Width is the direction of the displacement platform, the uncertainty of the Euclidean distance between two points computed with simulation. Without loss of generality, one point is at the corner of the measured region and another point moves from the formal point to another corner along the diagonal of the measured region. The depth varies randomly within 1 mm. The corresponding uncertainty result obtained from the uncertainty propagation formula is shown in Figure 10.  
Figure 10: Uncertainty of the calibrated system from simulation. The blue line presents the uncertainty of measurement result, and the red line defines the envelope lines of the uncertainty. The measured uncertainty fluctuates with the depth random change, meanwhile it rises with the increasing \(\Delta d\), which is the relative distance in the Width direction. The upper and lower bounds of uncertainty for corresponding \(\Delta d\) are labeled with a red line in Figure 10. It is indicated from the result that the uncertainty of measured Euclidean distance \(D\) can be simplify as Equation (21). \[U(D)=\left\{ \begin{array}{c}0.6(\upmu\mathrm{m}),\ \Delta d\leq 20\ \mathrm{mm}\\ 0.02\times\Delta d+0.2,\ 20<\Delta d\leq 40\ \mathrm{mm}\end{array} \right.. \tag{21}\]
# An Improved Monte Carlo Reliability Analysis Method Based on BP Neural Network
## 2 Calculation Theory of the BP-MCS Method  
### BP Neural Network Algorithm  
The finite element model (FEM) is usually used to calculate the structural response value of engineering structures, and the mapping relationship between the actual engineering response value and the influencing variables can be represented by the mapping relationship between the finite element model response value and the influencing variables. With the continuous application of the machine learning algorithm in the field of civil engineering, the BP neural network, as an intelligent prediction algorithm with strong nonlinear mapping ability and high prediction accuracy, has been widely used in the prediction of actual engineering structure response [25]. In the structural reliability analysis, the trained BP neural network model can be used to replace the finite element model with long calculation time, thus improving the calculation efficiency and reducing the calculation cost.  
BP neural network is mainly composed of input layer, hidden layer, and output layer. The input layer contains various influence variables and is linked to the hidden layer through different mapping functions and link weights. The output layer is generally the response value of the structure, and its main structure is shown in Figure 1. When constructing the BP neural network, it is necessary to have a training sample set consisting of input variables \(X_{i}(i=1,\ 2,\ \cdots,\ n)\) and output variables \(Y_{j}(X_{i})(j=1,\ 2,\ \cdots,\ m)\). In the training process of the BP model, it is necessary to compare the accuracy of the predicted value \(Y_{j}^{\prime}(X_{i})\) with the actual value \(Y_{j}(X_{i})\), and carry out feedback correction on the link weight of the network structure according to the error back calculation, thus completing the whole learning process. When training the BP model, its nonlinear prediction ability and generalization ability can be improved by adjusting the basic parameters (such as weight and bias) and hyperparameters (such as the number of hidden layers, the number of hidden layer nodes, and the learning rate) of the algorithmic structure [26], where the hyperparameters are determined before the algorithm is trained, and the basic parameters of the network are continuously and adaptively adjusted to obtain the optimal network performance in the process of network training.
## 4 Conclusions  
In this study, FeBC was prepared by the impregnation method using rice straw biochar. Material characterization shows that iron oxides were loaded on the surface of FeBC, which had more active sites than that of BC. The surface functional groups of FeBC also changed, facilitating the removal of Cr(VI). Adsorption experiments showed that the removal efficiency of Cr(VI) by FeBC was significantly improved from 4.57% to 99.5%.  
The adsorption efficiency was closely related to the mass modifier ratio, initial solution concentration and pH value. In addition, the presence of coexisting ions showed different degrees of inhibition. The isotherms and kinetics of Cr(VI) adsorption on FeBC were better described by the Langmuir isotherm and pseudo-second-order kinetic model, respectively. The study on adsorption mechanisms revealed that the Cr(VI) removal process by FeBC was accompanied by the electrostatic adsorption and the redox reaction of Cr. The reduction process was the main mechanism of the Cr(VI) removal by FeBC; Fe loaded on BC enhanced the Cr(VI) reduction process. This study will provide a theoretical basis for the Cr(VI) removal by FeBC; FeBC could be used as an efficient adsorbent for the Cr(VI) removal from aqueous solutions.  
Methodology, J.B.; formal analysis, Z.Z. and G.R.; data curation, K.L.; writing--original draft preparation, R.P.; writing--review and editing, A.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jiangsu Provincial Department of Education, grant number No. 202011460063Y.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & **pH** & **Total Cr (mg\(\cdot\)L\({}^{-1}\))** & **Cr(VI) (mg\(\cdot\)L\({}^{-1}\))** & **Ni (mg\(\cdot\)L\({}^{-1}\))** \\ \hline Before & 6.2 & 19.89 \(\pm\) 0.35 & 10.85 \(\pm\) 0.11 & 0.48 \(\pm\) 0.051 \\ adsorption & 7.8 & 1.32 \(\pm\) 0.07 & 0.35 \(\pm\) 0.076 & 0.27 \(\pm\) 0.021 \\ Standard limit & 6–9 & 1.5 & 0.5 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cr removal capability of FeBC from actual wastewater.
## 2 Materials and Methods
### Configuring and Implementing the System  
Currently, many cloud service platforms provide several free APIs related to chatbots, such as Google DialogFlow and Microsoft Azure Bot. However, among them, no chatbot was developed to investigate statements from victims of child sex crimes. Therefore, we  
Figure 1: Architecture of the fine-tuning learning model for intent classification and NER by the pre-trained models. A fine-tuning of the entire model as performed using the given statement data and the augmented data.  
developed our chatbot framework specializing in statement investigation. The chatbot developed here comprises the preliminary research, incident-related investigation, and termination phases according to the NICHD protocol. The initial investigation phase consists of a stage in which the chatbot explains the rules it should know from the child before the incident-related investigation phase begins and a stage in which the child freely describes their hobbies. In the incident-related investigation phase, the child makes a statement about the incident they experienced. In the termination phase, when the chatbot determined that sufficient critical information about the incident had been collected, it expressed gratitude for the child's statements, informed about the possibility of future interviews, and ended the investigation. Figure 2 presents the system configuration related to the incident-related investigation phase implemented in full-scale investigative interviews. The incident-related investigation phase was divided into three stages: (1) training the classifier and recognizer models using the transcript (yellow box in Figure 2); (2) identifying intent and entities in sentences based on trained models (blue box in Figure 2); and (3) the question generation part based on NICHD protocol to collect objective statements using classified intents and recognized entities (green box in Figure 2). The objective of our chatbot was to impartially gather extensive information about the event from the child's statements. Hence, the primary focus of this study was to assess the efficacy of intent classification, NER, and the construction of the training model within the chatbot system.  
6.1 Training Model for Intent Classification and Entity Recognition from the Interviewee's Statements  
The chatbot was meticulously designed with a slot-filling structure, enabling it to generate questions by identifying and analyzing crucial incident keywords while also classifying the intent of interviewees based on their statements. A model capable of continuous intent classification and entity recognition was necessary to converse with interviewees continuously. Consequently, a high-performance BERT-based model from the NLP domain was employed and interview transcripts supplied by the Supreme Prosecutor's Office of the Republic of Korea were utilized for model training. Furthermore, to enhance classification and recognition performance, a well-established data augmentation method was implemented.
## 2 Mathematical Formulations
### Freezing Model
#### 2.2.1 Model of Phase Change  
The source terms owing to the phase change in the VOF, mass, momentum, and energy equations are handled using [PERSON] model ([PERSON], 1979) [35; 45; 46], which is a physical based model for phase change and it can be employed in conjunction with the VOF multiphase model.  
The source term in the mass conservation equation can be defined from [PERSON]'s work [35]. If \(T_{s}>T_{sat}\) (ice dissolving into water), the rate of mass transfer for ice phase can be expressed as:  
\[\dot{m}_{sl}=\lambda_{sl}\alpha_{s}\rho_{s}\ \frac{T_{s}-T_{sat}}{T_{sat}} \tag{8}\]  
For the current solidification process, the process of ice dissolving into water can be neglected. Thus, \(\dot{m}_{sl}\) can be treated as zero in this work. Furthermore, if \(T_{l}<T_{sat}\) (water freezing into ice), the rate of mass transfer for water phase can be written as:  
\[\dot{m}_{ls}=\lambda_{ls}\alpha_{l}\rho_{l}\ \frac{T_{sat}-T_{l}}{T_{sat}} \tag{9}\]  
where \(\dot{m}_{sl}\) and \(\dot{m}_{ls}\) are the mass transfer rates from ice to water and from water to ice, respectively, whose units are kg/(m\({}^{3}\)s). The \(\lambda\) is the time relaxation parameter of phase change with the unit of 1/s, which would be presented in detail later, \(\alpha\) represents the volume fraction, \(\rho\) represents the density (kg m\({}^{-3}\)), and \(T\) represents the temperature (K). The subscript \"\(s\)\" and \"\(l\)\" represent solid (ice) and liquid (water). \(T_{sat}\) is the freezing point and it is 273.15 K for phase change between water and ice. Therefore, the source terms of the governing Equation (4) are calculated as:  
\[S_{m_{i}}=\left\{\begin{array}{ll}\dot{m}_{sl}-\dot{m}_{ls},&\ \ \ \text{for\ water\ phase}\\ \dot{m}_{ls}-\dot{m}_{sl},&\ \ \ \ \text{for\ ice\ phase}\end{array}\right. \tag{10}\]  
Then, the source term of the volume fraction for each phase is described as:  
\[S_{\alpha_{i}}=\frac{S_{m_{i}}}{\rho_{i}}. \tag{11}\]Furthermore, the momentum source can be written as:  
\[S_{u_{i}}=\left\{\begin{array}{ll}\dot{m}_{sl}\overset{\rightarrow}{u}-\dot{m} _{ls}\overset{\rightarrow}{u}_{s},&\text{for water phase}\\ \dot{m}_{ls}\overset{\rightarrow}{u}_{s}-\dot{m}_{sl}\overset{\rightarrow}{u} _{l},&\text{for ice phase}\end{array}\right. \tag{12}\]  
where \(\overset{\rightarrow}{u}_{l}\) and \(\overset{\rightarrow}{u}_{s}\) are the fluid velocities of water and ice. Therein, the velocity of ice phase is treated as zero. Finally, the source term in the energy equation can be calculated as:  
\[S_{h_{i}}=\left\{\begin{array}{ll}\dot{m}_{sl}H_{L},&\text{for water phase}\\ \dot{m}_{ls}H_{L},&\text{for ice phase}\end{array}\right. \tag{13}\]  
where \(H_{L}\) represents the latent heat induced by phase change between water and ice.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Training Process
#### 3.1.3 Model Uploading  
Upon reaching the predetermined accuracy, each participating client promptly sends the model parameters to a certain edge server with available bandwidth. Let \(\beta_{ij}\) denote the ratio of the bandwidth of edge server \(j\) allocated to client \(i\). The achievable rate of uploading model parameters of client \(i\) can be expressed as follows [14]:  
\[r_{ij}=\beta_{ij}B_{j}\log_{2}\left(1+\frac{p^{(T)}\abs{h_{ij}}^{2}}{N_{0}} \right), \tag{3}\]  
where \(h_{ij}\) is the channel gain between edge server \(j\) and client \(i\), \(N_{0}\) is the noise power, and \(p^{(T)}\) is the transmit power of the clients. Then, the uploading delay of model parameters can be expressed as \(\tau_{ij}^{comm}=\frac{\Gamma}{r_{ij}}\), where \(\Gamma\) is the size of the model parameters to be uploaded.
## 3 Results  
### Selected Studies  
A total of 1585 papers were found, 1580 from four databases, and five manually identified through other sources using search terms. As many as 1548 studies were included following the elimination of duplicates (\(n=37\)). After reviewing the abstracts, we removed 1511 articles and retrieved 37 papers for full-text analysis. We excluded ten studies; eight studies lacked specific insoles, one discussed artificial intelligence, and one analyzed insole cost effectiveness. We included 27 articles for the final analysis. Figure 1 describes the flow diagram for the selection process of studies included in this review. Figure 2 describes the published paper on PPP reduction strategies. Figure 1: Flow diagram for study selection. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Walking Speed**} \\ \cline{3-8}
**Region** & **Clinical Finding (Reference)** & \multicolumn{3}{c}{**DM**} & \multicolumn{3}{c}{**Non-DM**} \\ \cline{3-8}  & **4** & **4** & **6** & **8** & **10** & **12** \\  & **km/h** & **km/h** & **km/h** & **km/h** & **km/h** & **km/h** & **km/h** \\ \hline
**Forefoot (QPA)** & \(\bullet\) & Plantar pressure in three types of inside given to DM people [55] & 217 & – & – & – & – & – \\  & \(\bullet\) & Insoles on plantar pressure redistribution [48] & 221 & 183 & – & – & – & – \\  & \(\bullet\) & Insole material and hardness in different plantar sites [70] & – & 53 & 46 & – & – & – \\  & \(\bullet\) & Analysis of peak pressure during walking and running with insoles [72] & – & – & – & 278 & – & 302 \\  & \(\bullet\) & Medial arch support reduces peak impact and loading [73] & – & – & – & – & 261 & – \\  & \(\bullet\) & Biomechanical running shoes with different cushioning technologies [74] & – & – & – & – & – & 315 \\  & \(\bullet\) & The walking speed and footwear on plantar pressures [71] & – & – & 110 & – & – & – \\ \hline  & **Mean** & 219 & 118 & 78 & 278 & 261 & 308.5 \\ \hline
**Midfoot (QPA)** & \(\bullet\) & Plantar pressure in three types of inside given to DM people [55] & 95 & – & – & – & – & – \\  & \(\bullet\) & Insoles on plantar pressure redistribution [48] & 100 & 116 & – & – & – & – \\  & \(\bullet\) & Insole Material and Hardness in Different Plantar Sites [70] & – & 28 & 32 & – & – & – \\  & \(\bullet\) & Analysis of peak pressure during walking and running with insoles [72] & – & – & – & 161 & – & 177 \\  & \(\bullet\) & Medial arch support reduces peak impact and loading [73] & – & – & – & – & 150 & – \\  & \(\bullet\) & Biomechanical running shoes with different cushioning technologies [74] & – & – & – & – & – & 124 \\  & \(\bullet\) & The walking speed and footwear on plantar pressures [71] & – & – & 50 & – & – & 124 \\ \hline  & **Mean** & 97.5 & 72 & 41 & 161 & 150 & 150.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The peak plantar pressure in preferred walking speed in people with DM and different walking speeds in non-DM people.
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 4 Results and Discussion  
The first and expected result that was consistent throughout all simulations is the increase in the BER as the fiber was longer. We examined four different fiber lengths, as described in Figure 3, that correspond to different values of the CD parameter \(D\cdot L\left[\frac{\text{PS}}{\text{mm}}\right]\). A different wavelength will lead to a different \(D\)[16], which corresponds to a slight change in fiber length. Figure 3a shows that in 3.5 km (lowest subplot), the histogram of the signal before the equalizer (\(\psi(kT)\)) is completely distorted and does not correspond to the original voltage levels of the symbols. According to Figure 3b, even with the DFE, the BER is around 0.02.
# The Effects of Polyphenol-Rich Black Elderly on Oxidative Stress and Hepatic Cholesterol Metabolism
## 3 Results
### Effects of BEE on the Genes Involved in Biliary Cholesterol Efflux  
Next, we measured the genes responsible for the cholesterol efflux in HepG2 cells. Hepatic NPC1L1 reabsorbs the cholesterol from bile to the liver, whereas ABCG5/G8 plays an essential role in hepatobiliary cholesterol secretion. The expression of NPC1L1 was dose-dependently and significantly (\(p<0.05\)) decreased by BEE. Furthermore, significant induction (\(p<0.05\)) of ABCG5/G8 was observed in BEE-treated HepG2 cells. For the genes involved in bile acid secretion, the mRNA levels of CYP7A1 and ABCB1 were significantly (\(p<0.05\)) increased by BEE treatment. The protein level of NPC1L1 was noticeably reduced in BEE-treated HepG2 cells. Moreover, the protein levels of transporters for biliary cholesterol secretion, i.e., ABCG5 and ABCG8, were noticeably increased in BEE-treated HepG2 cells (Figure 3).  
Figure 1: Expression of genes for cholesterol biosynthesis and absorption in BEE-treated HepG2 cells. Cells were treated with 50 or 100 \(\upmu\)g/mL of BEE for 24 h. The gene expression was measured by qRT-PCR analysis. Data are expressed as relative expressions to control. Different letters indicate significant differences (\(p<0.05\)). Values are means \(\pm\) S.E.M. n = 6.  
Figure 2: The proteins for cholesterol biosynthesis and absorption in BEE-treated HepG2 cells. The cells were treated with 50 or 100 \(\upmu\)g/mL of BEE for 24 h. The representative image of the Western blot from two different experiments is shown. \(\upbeta\)-actin was used as a loading control.
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 2 Background and Related Work
### Distributed Processing Framework  
In this paper, we utilize two open-source solutions, Apache Storm [5; 6] and Apache Kafka [7; 8], to construct the proposed stream purification platform. Apache Storm is a distributed parallel processing framework for handling continuously generated stream data. The Storm cluster consists of a single master called [PERSON] and multiple workers called Supervisors. This cluster generally manages and shares state information between Nimbus and Supervisors through the open-source distributed coordinator Apache Zookeeper [10; 11].  
To perform actual stream processing in the distributed Storm cluster structure, we need to implement algorithms following the programming model shown in Figure 2. The figure includes three logical concepts used in implementing tasks in Storm: Spout, Bolt, and Topology. Firstly, a Spout is a component that connects to the original source that generates the data stream, converts the input stream into tuples, which are the data processing units of Storm, and passes them to the next stage. Secondly, a Bolt is a component that receives tuples from a Spout or a previous Bolt and performs actual processing according to the implemented logic. In Storm, the number of parallel Bolts executing the same logic can be set during the configuration. Thirdly, a Topology, which is composed of multiple Spouts and Bolts, defines the entire operation process of Storm and includes all processes from the input to the output of the stream.  
The second open-source framework, Apache Kafka [7; 8], is a distributed message queuing system designed to reliably send and receive stream data in a large-scale stream environment. Apache Kafka consists of producers, consumers, and brokers, and like Storm, it uses Apache Zookeeeper for cluster management. Producers and consumers are client applications where producers play the role of publishing messages to the Kafka cluster, and consumers play the role of subscribing to the desired data from the Kafka cluster. Brokers manage the messages sent by producers through topic channels.