esa-sceva/satcom-chunk-collection huggingface dataset:
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 4 Performance Evaluation and Discussion
### Number of Received Molecules  
Figure 8 shows the number of received molecules when the environmental temperature changes. As shown in Figure 8, the reaction becomes faster as the temperature is increased. This is because the value of the diffusion coefficient increases when temperature increases. Furthermore, it is observed that the rate stays the same after the temperature rise of over 30 \({}^{\circ}\)C, and the number of received molecules reaches the maximum at the temperature of 30 \({}^{\circ}\)C and then decreases. This is because there are two effects affected by the temperature. The number of received molecules are increased due to the temperature rise because the activity of the signaling molecules improves. On the other hand, the receptor of the receiver  
\begin{table}
\begin{tabular}{l l l} \hline \(L_{t}\) & Training sequence length & 1 bit \\ \(L\) & Data sequence length & 10 bits \\ \(T_{p}\) & Symbol interval duration & 0.3 ms \\ \hline \end{tabular}
\end{table}
Table 2: BER simulation parameters.  
Figure 6: Calculation examples of number of molecules received in diffusive molecule communication.  
Figure 7: Simulation configuration for performance evaluation.  
molecule has a limitation to receive molecules in a certain time period, so the number of received molecules should be saturated when a large number of signaling molecules come to the receiver in a short time period.  
Also, Figure 9 shows the number of received molecules when the temperature distribution is heterogeneous. At high temperature on the receiver side and low temperature on the transmitter side, the number of received molecules is increased compared to the setup with a homogeneous temperature of 35 \({}^{\circ}\)C. This is potentially due to the lower temperature on the transmitting molecule side, which reduces the reaction rate of the decomposition reaction. Conversely, when the temperature on the receiving molecule side was low, the number of received molecules decreased when compared to 30 \({}^{\circ}\)C due to the increased reaction rate of the decomposition reaction at higher temperature on the side of the transmitting molecule.  
The number of received molecules when the volume of the transmitted molecules changes is shown in Figure 10. The surface distance between the transmitting and receiving molecules was set as constant when the volume of the transmitting molecule was changed. The results confirmed that the number of received molecules tend to decrease due to increases in volume. It is considered that the number of molecules released from the position of \(r=1.0\) um decreased. This was realized by ensuring that the number of emitted molecules is the same as that in the case of a point source and by changing the volume by fixing the surface distance.  
Figure 8: Number of received molecules in consideration of temperature changes.  
Figure 9: Number of received molecules when temperature distribution is heterogeneous.
## 3 Proposed Methodology
### Feature Extraction  
Handling a large dataset with many variables can be computationally intensive and challenging. Feature extraction, a dimensionality reduction technique, breaks down large amounts of raw data into more manageable groups. Feature extraction refers to methods for selecting and/or combining variables into features, significantly reducing the amount of data that must be processed while accurately describing the initial dataset.
# Dynamic Stall Mechanisms of Pitching Airfoil: IDDES Study Across Different Mach Numbers
## 3 Aerodynamics at a Mach Number of 0.283  
### Aerodynamic Load Response at \(Ma=0.283\)  
The dynamic stall characteristics of a pitching NACA 0012 airfoil are computed and analyzed at a Mach number of 0.283. Figure 6 presents the cycle-averaged lift, drag, and moment coefficients, as well as the lift stall (L) and moment stall (M) points. The arrows in the figure denote the variation trend of airloads. In the pitching-up stage, the lift coefficient increases gradually and reaches a peak value at a lift-stall angle of 21.5\({}^{\circ}\), beyond which it drops rapidly. Drag divergence starts at an AoA of 17.6\({}^{\circ}\), and the drag coefficient rises to its maximum value at an AoA of 24.2\({}^{\circ}\). The moment coefficient remains stable until a moment stall angle of 23.7\({}^{\circ}\), after which it experiences a sharp decline. In addition, two notable load fluctuations labeled 'a' and 'b' are observed in the figure. Compared to the static airfoil, the pitching airfoil indicates a significant 9.7\({}^{\circ}\) delay in dynamic stall, with the stall angle shifting from 14.0\({}^{\circ}\) to 23.7\({}^{\circ}\). During the stall delay stage, the nose-down moment continues to increase at a gradual rate, exerting a critical influence on the aerodynamic damping of the pitching airfoil.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grid Resolution** & \(\alpha_{\text{ds}}\) & \(Cl_{\text{max}}\) & \(Cd_{\text{max}}\) & \(Cm_{\text{min}}\) \\ \hline Coarse & 23.8 & 2.24 & 1.0 & \(-0.50\) \\ Medium & 23.7 & 2.24 & 1.02 & \(-0.54\) \\ Fine & 23.7 & 2.24 & 1.01 & \(-0.53\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of results from different grids.  
Figure 5: Comparisons among experimental data, calculated results by [PERSON] et al. [23], and current numerical results for a modified VR-12 airfoil at \(Ma=0.5\): (**a**) _Cl_; (**b**) _Cd_; (**c**) _Cm_.  
Figure 7 illustrates the time evolution of aerodynamic force coefficients over five consecutive pitching cycles, along with their corresponding frequency spectra. The lift, drag, and pitching moment coefficients all exhibit similar periodic behavior, with their variations dominated by a common normalized frequency, specifically, the airfoil pitching frequency \(k\) = 0.151. Notably, the frequency spectra also reveal distinct harmonic components.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 5 Experimental Results  
In this study, we benchmarked four drift detection algorithms--DDM, EDDM, HDDMA and HDDMW--on a synthetic data stream dataset designed to evaluate the performance of these algorithms in detecting different types of drifts: abrupt, gradual, and incremental. Our findings were based on three criteria:detection performance, detection time, and detection delay.
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
## 2 Materials and Methods
### Adsorption Experiments and Kinetic Experiments  
Solutions of 100 mL of Cd\({}^{2+}\) and Ni\({}^{2+}\) were prepared from commercial stock solutions of 1000 ppm (Sigma-Aldrich, Saint Louis, MO, USA). An initial volume of stock solution was diluted in 70 mL of deionized water. The working solutions were prepared in triplicate for each concentration. The pH was adjusted to 6 with diluted solutions of NaOH (Fisher Chemical, Fair Lawn, NJ, USA) or HNO\({}_{3}\) (Sigma-Aldrich, Saint Louis, MO, USA). After the pH adjustment, the solution was brought to a volume of 100 mL with deionized water to obtain the desired concentration of metal. Each working solution was added to a plastic bottle of 120 mL for the experiment. Each bottle had 100 mL of metal solution and 0.5 g of adsorbent. The adsorbents were CA beads, CA-SCGs beads, or SCGs. The final metal concentrations of these solutions were 10 ppm, 20 ppm, 30 ppm, 60 ppm, 80 ppm, and 100 ppm.  
The bottles were placed in an orbital shaker at 30 \({}^{\circ}\)C, at 250 rpm. For adsorption experiments, samples were taken at 0 and 24 h. For kinetic experiments, samples were taken at 0 min, 30 min, 60 min, 90 min, 120 min (2 h), 180 min (3 h), 240 min (4 h), 360 min (6 h), 720 min (12 h), and 1440 min (24 h). Kinetic experiments were done only for the concentrations of 10 ppm and 100 ppm. The sampling procedure for SCGs was slightly different because the adsorbent is not a bead, but a powder. A sample was taken from the plastic bottle in the orbital shaker at the specific time, centrifuged for 5 min at 3200 rpm, and then a specific volume from the supernatant was taken. All the samples were diluted with 2% \(\%\) nitric acid and refrigerated prior to the analysis for conservation of the sample. The dilution factor was taken into consideration in all the samples. The samples were analyzed for Cd\({}^{2+}\) or Ni\({}^{2+}\) content by inductively coupled plasma optical emission spectrometry (Pelkin-Elmer ICP-OES Optima 8000, Perkin-Elmer, Billerica, MA, USA).  
The adsorption capacity of Cd\({}^{2+}\) or Ni\({}^{2+}\) at equilibrium (\(q_{e}\)) was calculated using Equation (1) [20]:  
\[q_{e}=(C_{0}-C_{e})V/W \tag{1}\]  
where \(C_{0}\) (mg/L) is the initial metal concentration, \(C_{e}\) (mg/L) is the equilibrium concentration; \(V\) is the volume of the solution of Cd\({}^{2+}\) or Ni\({}^{2+}\) ions (L) and \(W\) is the adsorbent weight added to the solution (g). The time of equilibrium was 1440 min (24 h).  
For the kinetic experiments, the adsorption capacity of Cd\({}^{2+}\) or Ni\({}^{2+}\) at time t (\(q_{t}\)) was calculated from Equation (2) [21]:  
\[q_{t}=(C_{0}-C_{t})V/W \tag{2}\]  
where \(C_{t}\) is the concentration of Cd\({}^{2+}\) or Ni\({}^{2+}\) at time \(t\).
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 3 Network Topology Optimization Method
### Performance Analysis  
In Algorithm 1, the network topology is optimized according to nodes' local structural parameters information. Hence, compared with other network topology optimization methods [21; 29] based on global topology information, the communication overhead of the proposed method is smaller. Meanwhile, due to the structural parameters information, the computational complexity of the proposed method is lower than other heuristic search methods [21; 29; 30]. The concrete analysis is as follows.  
In Algorithm 1, the maximum number of optimization adjustments is the communication links \(K\). In the \(b\)-th adjustment, deleting links or adding links are determined by the rules described above. Taking deleting links as an example, where node \(i\) receives initial deleting links information from its neighboring nodes, i.e., \(\text{del}^{\prime}_{N_{i}}\triangleq\left\{\text{del}^{\prime}_{j}\right\}_{ j\in N_{i}}\). Therefore, the communication overhead is \(\sum\limits_{b=1}^{K}\left(|\text{del}^{\prime}_{N_{i}}|+|\text{add}^{\prime} _{N_{p}}|\right)\), where node \(i\) (or node \(p\)) is the selected node in the \(b\)-th adjustment.  
In the \(b\)-th adjustment, taking deleting links as an example, where node \(i\) should calculate its initial deleting links information, and combine with the initial deleting links information of neighboring nodes to determine the communication link to be deleted. Hence, the computational complexity for determining the communication link to be deleted at node \(i\) is \(O\left(I^{2}\right)\). Therefore, the computational complexity of the proposed method is \(O\left(KI^{2}\right)\).  
The communication overhead and computational complexity of the proposed method in this paper and the WWKJ method proposed in [29] are given in Table 1. The WWKJ method optimizes the network topology based on the global network information, and performs an iterative search for feasible solutions, where \(T_{m}\) is the maximum number of iterations for each simulation \(m\).
## 4 Evolutionary Algorithms in VANETs
### Routing Protocols  
A mono-objective linear evolutionary algorithm is used in the Adaptive Message Routing (AMR) [41] technique to create end-to-end QoS routing. The two restrictions to maintain a constrained value, in the authors' opinion, are connection probability and hop. In this model, the vehicle network is depicted as a grid made up of fixed nodes and vehicles with RSUs at their core. Based on data from both the medium and the road, the RSU determines an optimal route for vehicles. The best path is calculated using the road identifiers (RIDs) that are present at each chosen intersection. Importantly, AMR is capable of dealing with network disruptions by allowing cars to hold and deliver signals while being prepared to forward them to the next legitimate vehicle.  
xChangeMobile [42] uses multi-objective linear GAs. xChangeMobile utilizes two routing protocols, VanetDFCN and ChunkXChange, which are optimized utilizing GA for inter-vehicle material return. The strategy seeks to address the data stream missed due to barriers along the highway, which in turn impacts communication. Ref. [43] determined the ideal vehicle data transfer protocol (VDTP) parameter settings. Additionally, this protocol transfers files in the transport layer. There are five development methods: Particle Swarm Optimization (PSO), Differential Evolution (DE), genetic algorithm (GA), Evolutionary strategy (ES), and simulation annealing (SA). Packet size, packet delivery, and retransmission time are the three parameters.
## 4 Results and Discussions
### The Fuzzy Delphi (FDM) Analysis
#### 4.1.2 The Second Fuzzy Delphi Analysis  
Through the pre-test analysis of the fuzzy Delphi expert questionnaire, the content was revised to three aspects, six criteria, and 16 design indicators. In this way, the post-test analysis of the fuzzy Delphi expert questionnaire was conducted again, hoping to make the construction of product design indicators for wearable devices more precise and critical. According to the 80/20 rule, the overall average of 7.046 was multiplied by 0.8 to obtain the expert consensus threshold value of 5.637, and those lower than that were discarded. The selection results revealed that the six evaluation criteria in the development of product design indicators for wearable devices all reached the threshold value, such that all were retained. Next, in the post-test analysis of the index evaluation of product design indicators for wearable devices, 80% of the total average expert consensus value of 7.252 was used as the threshold value, and the expert consensus threshold value of 5.802 was received. Those with values lower than that were eliminated, and a total of 16 evaluation indicators were retained.
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
## 3 Application to Authenticate Turmeric Powder
### Detection of Metanil Yellow Contamination in Turmeric Powder
The metanil yellow pixels have a similar spatial distribution in both the binary images. Figure 5: Raman spectral images of: turmeric and metanil yellow (**a**); turmeric-metanil yellow mixture samples (**b**). Out of ten thousand pixels that were acquired from 10% concentration sample, 80 detected metanil yellow pixels correspond to 0.80% of total pixels. Similarly, in 1% concentration sample, 0.08% of total pixels were detected as metanil yellow. The percentage of detected metanil yellow pixels were correlated with its actual concentration in the sample with a correlation coefficient of 0.99 (Figure 7). The result shows a linear relationship between the metanil yellow concentration in the sample and the percentage of its detected pixels.
## 3 Results
### eLUE Models Using MODIS
Therefore, the GPP_M2 model for obtaining GPP with a significative coefficient of determination (R2) of 0.91 is shown in Equation (11):  
\[GPP\_M2=-5.148+1.214\times(NDVI\times PAR_{TOA})+0.091\times T_{min}+12.859\times SWC \tag{11}\]  
The time series obtained for this model (GPP_M2) from January 2018 to August 2020 versus the tower GPP (GPP_T) is shown in Figure 7. As it is observed, the model slightly underestimated the spring GPP maxima in 2018 but it slightly overestimated the maxima in 2019 and 2020. In addition, the model overestimated the GPP minima during summer and during winter showed similar values to the GPP_T. The RMSE for all the time series is 0.94 g C m\({}^{-2}\) day\({}^{-1}\). The validation of the model was obtained using the year 2020 with an R2 = 0.75 and a RMSE of 1.42 g C m\({}^{-2}\) day\({}^{-1}\). Figure 8 shows the values of GPP_M2 versus the values of GPP_T:  
Figure 7: Time series obtained for the Gross Primary Production MODIS multiple linear regression model (GPP_M2) from January 2018 to July 2020 versus the tower Gross Primary Production (GPP_T) in the Mediterranean grassland of La Herreria.
# Advances and Future Perspective on Detection Technology of Human Norovirus
## 2 Detection Techniques
### Molecular Detection Methods
#### 2.3.1 Isothermal Amplification  
The LAMP could be used to detect different subtypes of HuNoVs [52]. The LOD was approximately \(10^{3}\) genomic copies/reaction tube. No cross reaction was observed amongst the subtypes. Based on this method, hydroxy naphthol blue dye was introduced into RT-LAMP to evaluate the results directly [53]. The sensitivity reached \(10^{3}\) genomic copies per reaction tube, and the coincidence rate was 94.83% compared with RT-PCR detection. Then, a one-step RT-LAMP was developed. The sensitivity was 10 times higher than that of traditional RT-qPCR [54]. The RT-LAMP had high sensitivity, low cost and easy operation, which was suitable for onsite rapid detection. Moreover, multiplex RT-LAMP can promote this technology.  
NASBA technology could be applied to detect NoVs. [PERSON] et al. first reported that the LOD was \(10^{4}\) RT-PCR-detectable units of NoVs RNA in a stool filtrate, and the detection time was 4-6 h [55]. [PERSON] et al. detected GI and GII HuNoVs in clinical stool samples [56]. Compared with RT-PCR, the sensitivity and specificity of NASBA were 100% and 80%, respectively. [PERSON] et al. reported a real-time molecular beacon NASBA to detect GII HuNoVs in stool samples, which was 88.5% consistent with the RT-qPCR results [57]. NASBA does not require high-quality templates, and this technology has low mismatch rates, easy operation and high specificity and sensitivity. It is suitable for the rapid preliminary determination of the cause of infection, which can provide early warning for the early control of HuNoV infection.  
RT-RPA is a new type of rapid real-time detection technology for HuNoV. The detection can be completed within 30 min at 40 \({}^{\circ}\)C, and the LOD reached 3.40 \(\pm\) 0.20 log genomic copies. Compared with the routine RT-qPCR assay, RT-RPA is not sensitive to inhibitors, and it can detect complex samples [58]. Moreover, given its short processing time, determining the cause of infection to control the spread of infection became easy.
## 2 Modeling of Initial Health Assessment Based on MSET
### Deviation Distance Measurement Based on Mahalanobis Distance  
The performance state of rolling bearings can be characterized by vibration characteristics, and the statistical distribution of the index data space in the normal state is different from that in the degraded or faulty state. By quantifying the degree of deviation, the degree of performance degradation of rolling bearings can be measured. The degree of deviation is positively correlated with the degree of performance degradation. The greater the degree of deviation of the current state indicator data from the health baseline, the greater the degree of performance degradation.  
Mahalanobis distance was developed by Indian statistician [PERSON] to measure the distance between a sample point and a data distribution. Multidimensional data can take into account the covariance between the dimensions, which can better reflect the true distribution of the data. The Mahalanobis distance expression is as follows:  
\[d(x,y)=\sqrt{\left(x-y\right)^{T}\sum_{X}^{-1}\left(x-y\right)} \tag{18}\]  
where \(x,y\) are two samples that follow the same distribution, which in this paper respectively refer to the observation vector \(X_{obs}\) and the estimation vector \(X_{est}\)\(\sum_{X}^{-1}\) represents the covariance matrix of the reference sample, where is the covariance of the historical memory matrix \(D\).  
The observation vector is composed of the root mean square value, the peak value and the kurtosis, representing the equipment health state from three dimensions. The data units and value ranges of the three dimensions are different, and the traditional Euclidean distance calculation of the distance between the two data will be affected by the different value ranges of the multidimensional data and the correlation of the non-independent distribution between the dimensions, thus affecting the accuracy of the distance measurement results. Different from the traditional Euclidean distance, Mahalanobis distance not only considers the dimension of each dimension of data but also the correlation between them, which makes Mahalanobis distance more effective in the processing of multidimensional data with correlation [11]. The Mahalanobis distance first rotates the data of each dimension according to the principal component to make the dimensions independent of each other, and then the distance is calculated after data standardization to make the measurement result more accurate.  
After calculating the distance of the observation vector deviating from the estimation vector through the Mahalanobis distance, input the subsequent health mapping function to evaluate the health state of the rolling bearing.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 8 Performance Improvement of IoB-DTN Based on Data Aggregation Mechanism
### Temporal Aggregation  
The aggregation period is the duration, defined in seconds, where the sensed values by various sensor nodes are generated earlier or later than the reference packet. We assessed three values of the aggregation period: 2 s, 5 s and 10 s.  
The average delivery rate of IoB-TA variant is shown in Figure 29. The first observation we make is the achievement of 100% as transmitting rate by using a large buffer. The use of a small buffer size and increasing the aggregation period slightly increase the delivery rate. Here, it is worth pointing out that the delivery delay results obtained using temporal aggregation are better than using spatial aggregation. In the same context, these outcomes influence the throughput, as depicted in Figure 30.  
The average delivery delays of received packets is shown in Figure 31. We note that the delivery delays increases progressively when rising the aggregation period. As expected, the use of a small duty cycle offers lower delays since data packets are delivered faster.  
As for the energy consumption of IoB with temporal aggregation, we also evaluate the two parameters cited before. The average protocol cost is illustrated in Figure 32. We remark that the number of transmitted packets in the network decreases by increasing the aggregation period. As for spatial aggregation, the rise of the aggregation parameter leads to having more packets aggregated which reduces the overall protocol cost in the network. It is important to note that the communication cost for IoB with temporal aggregation is twice that of IoB with spatial aggregation.  
Figure 28: Average number of aggregated packets per packet for IoB-SA.  
Figure 29: Average delivery rate for IoB-TA.  
The number of aggregated packets into the same packet for IoB with temporal aggregation is depicted in Figure 33. As for spatial aggregation, the use of the smallest period value (2 s) gives better performance since it leads to aggregating fewer data packets into a packet.  
Figure 31: Average delivery delay for IoB-TA.  
Figure 30: Average throughput for IoB-TA.  
Figure 32: Average protocol cost for IoB-TA.