esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selection of HDEs  
At the core of the SBME technique is achieving equilibrium in target analyte diffusion and migration between the sample and extraction phases. Therefore, choosing an extraction solvent with a high affinity for the analyte is essential in the mass transfer and separation process. For this mission, HDEs were utilized as both the extraction phase filled in the lumen and impregnated in the wall pores of the HF microtube. Synthesized HDEs using various ratios of menthol-lauric acid (2:1, 3:1, 4:1, and 5:1) were tested. The results indicated that the HDEs with a ratio of 4:1 (menthol to lauric acid) showed the highest peak area for the three target compounds, especially for PRO and TES, as listed in Table 2. As the quantity of menthol increases, the viscosity of HDEs gradually decreases [26], thereby diminishing the mass transfer hindrance of the desired analytes into the extraction phase. Meanwhile, by increasing the molar ratio of menthol above 4:1, the peak area of PRO and TES started to decrease slowly. As a result, menthol-lauric acid (4:1 ratio) HDE was most appropriate for extracting the target hormones and was used for further optimization experiments.
## 3 Analysis of Experimental Data and Results
### Probabilistic Model Development
#### 3.3.2 Error and Uncertainty  
Uncertainties associated with the model have various sources which may be grouped as follows:1. Aleatory uncertainty: Also known as inherent uncertainty, aleatory uncertainty is a natural randomness of a quantity such as uncertainty in the material features. Generally, there are different factors during manufacturing of a material that cause random variation of the material properties from experiment to experiment [30]. To minimize aleatory uncertainty, test data of specimens from the same material lot (test samples coming from the same sheet of Al7075-T6 or Ti-6-4) were used. It should be noted that this physical variation is inherent to experiments and cannot be eliminated.
2. Epistemic uncertainties: This uncertainty is the result of limited information or incomplete information due to finite experimental data or a limited number of observed data points. The addition of experiments corresponding with each loading condition in the parameter estimation process can help reduce epistemic uncertainty bounds for the estimated model parameters. Two types of epistemic uncertainty are discussed below: 1. Measurement error: This is the error caused by imperfect measuring equipment and/or human observation errors. There are some inherent variations resulting from the method of observation. The image processing method used for measuring the specimen's crack length based on the high-resolution photography with the close-up lens carried some measurement errors due to difficulties in finding the crack tip in images.  
Some methods were applied to reduce the crack length measurement error. If the measurement value is known to be constant from one measurement to another and if the measurement can be made several times, information about the uncertainty of the measuring method can be obtained. In this case, this type of uncertainty can be reduced through averaging. It was attempted in this research to reduce the measurement uncertainty using the measurement data produced by two different individual testers on each data point. Since these measurements are mutually independent the mean value of the measured crack length was used for each data point.  
2. Modeling error: Besides the error of crack length measurement, filtration method and insufficient data, there is an important model uncertainty that must be captured. This model uncertainty is related to the formulation of the proposed probabilistic model. There might be some other sources of uncertainty that can be considered such as the uncertainty resulting from the de-noising technique. For example, the noise reduction method may filter out some crack growth related signals and contribute to the model uncertainty. Improvement can be made by exploring alternate methods of classification of AE-related data and filtration techniques.  
In Section 3.3.2.1, the AE model error is estimated, which expresses the sum of aleatory and epistemic uncertainties described above. Later in Section 3.4, the model is validated and the uncertainties are estimated.
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Types of Sensors
#### 3.1.2 Ultrasonic Sensors  
These sensors emit waves on a determined frequency and measure the distance between itself and the object that stands in its wave's path. Due to its effectiveness and precision, a great quantity of smart parking solutions have been proposed based on this technology [13]. These sensors are used to detect a parking lot status (free or occupied). There are different types of technologies that are used in previous works to transmit and process data generated from sensors. For instance, placing a sensor on a high position of the place and sending data to a raspberry through Wi-Fi [42]. Also, through a cable, such as in [55], or connecting it to an Arduino that is part of a XBee network [64; 65], it is possible to connect a sensor to a HTTP server that also allows to verify parking lot availability [48]. Similarly, a solution proposed the use of a wireless network called Xmesh [15]. Furthermore, along with the advancement of technology, new implementations involve LPWAN technologies such as LoRa that connect sensors for the transmission of the parking space status [72].  
Ultrasonic sensors are implemented one to one which means that there is one sensor for every parking slot. Additionally, it is important to mention that these sensors are low cost and their detection accuracy is high.
## 3 Mismatch Error Estimation and Calibration Method
### Four-Channel TIADC Memory Nonlinear Online RLS Estimation Method  
Aiming to address the shortcomings of the current offline LS error estimation method, this paper proposes a joint error estimation method with rough estimation by the offline LS method and accurate estimation by the online RLS method. The recursive initial valuesused in this section are derived from the values obtained from the LS method estimation in Section 3.1.  
For ease of understanding, the \(n\)th input signal of the \(m\)th ADC of the TIADC is redefined here as \(x_{m}(n)\), i.e.,  
\[x_{m}(n)=x(4n-4+m) \tag{22}\]  
Define:  
\[\begin{split}\phi_{m,n}^{T}=[1&\quad x_{m}(n) \quad x_{m}(n-1)\ x_{m}(n-2)\quad x_{m}(n)^{2}\\ x_{m}(n)x_{m}(n-1)\ x_{m}(n)x_{m}(n-2)&\quad x_{m}( n-1)^{2}\ x_{m}(n-1)x_{m}(n-2)\ x_{m}(n-2)^{2}\end{split} \tag{23}\]  
\[\Phi_{m,n}=\left[\begin{array}{c}\phi_{m,1}^{T}\\ \vdots\\ \phi_{m,n}^{T}\end{array}\right] \tag{24}\]  
\[P_{m,n}=\left(\Phi_{m,n}^{T}\Phi_{m,n}\right)^{-1} \tag{25}\]  
The \(n\)th actual output signal of the \(m\)th ADC of the TIADC is \(y_{m}(n)\); then, define:  
\[Y_{m,n}=\left[\begin{array}{c}y_{m}(1)\\ \vdots\\ y_{m}(n)\end{array}\right] \tag{26}\]  
Define \(\hat{\theta}_{m,n}\) as the \(n\)th recursive estimate of the \(m\)th ADC mismatch error; then:  
\[\hat{\theta}_{m,n}=P_{m,n}\phi_{m,n}^{T}Y_{m,n} \tag{27}\]  
Next, based on the current input signal \(x_{m}(n)\) and output signal \(y_{m}(n)\), use recursion to obtain a more accurate mismatch parameter for this ADC, and define:  
\[P_{m,n+1}=P_{m,n}-\frac{P_{m,n}\phi_{m,n+1}\phi_{m,n+1}^{T}P_{m,n}}{1+\phi_{m,n+1}^{T}P_{m,n}\phi_{m,n+1}} \tag{28}\]  
Then, recursively obtain the next estimate as:  
\[\hat{\theta}_{m,n+1}=\hat{\theta}_{m,n}+P_{m,n+1}\phi_{m,n}(y_{m,n}-\phi_{m,n }^{T})\hat{\theta}_{m,n} \tag{29}\]  
The RLS method has many advantages, such as good adaptability to non-stationary signals, fast convergence, and high accuracy and stability of estimation. Compared with the LS method, the single computing complexity is low and the amount of stored data is small. In addition, only the current state amount needs to be input, no time window needs to be added, and it can meet the real-time requirements.
## 2 Constituent and Process Sequence
### Parametric Influence of BG on HNT  
The influential test variables (pH grade of solution, concentration of BG initially, adsorbent quantity, and thermal changes) were considered utilizing group tests. For preparing the stock solution of BG (1000 mgL\({}^{-1}\)), water distilled twice was utilized. This was used to prepare concentrations of 25-200 mgL\({}^{-1}\). To numerous 250-mL Erlenmeyer flasks, 50 mL of prepared solution and a calculated quantity of HNT was added. Parametric variations included for evaluation were, pH (2, 4, 6, 7, 8, 10, and 12); BG concentration in beginning (25-200 mgL\({}^{-1}\)), and adsorbent amount (0.5-6 gL\({}^{-1}\)). Thermal influence on process of adsorption was investigated for three temperatures were selected with an underlying color centralization of 100 mgL\({}^{-1}\). Test solution under static thermal conditions was stirred for 180 min at a rotational speed of 165 rpm in an orbital shaker. BG color not adsorbed by HNT was separated by subject to high-speed centrifuging (3000 rpm) for a duration of 5 min. Centrifugation is reprocessed for 5-min durations till a clear solution is obtained. The equilibrium concentration centrifuged solution was not set in stone utilizing a Perkin-Elmer Lambda EZ-201 UV-vis spectrometer. To consider the impact in the scope of pH 2-12 the group tests were performed. To achieve the desired pH grade, 0.01-1 M HCl or NaOH was utilized. Solution concentration (100 mgL\({}^{-1}\)), kinetics were investigated at 303 K, 313 K and 323 K at time-variant conditions. Analyses were recreated threefold, and the mean qualities are considered.  
Figure 1: Brilliant green atomic structure.
# Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools +
## 2 Case Study: Detection of Bot Nodes Within Unknown Networks in Cloud Environments
An iterative training process or a _fine-tuning_ phase is suggested to refine the model based on a new subset of data. 3. _Big Data Analytics Layer_. This layer includes advanced big data analytics in order to handle large quantities of data sourced from the input layer. Here, behavioral triangulation is carried out for multiple nodes and employs _higher-level_ analysis to look for suspicious patterns. _Machine learning_ (ML) and _statistical inference_ techniques are also employed for _traffic pattern identification_ or _fraud detection_. 4. _Result Layer_: This is the last layer of the analysis, with the resultant outputs of the investigation being displayed. The resulting layer presents the last analysis, to reveal which nodes are part of a botnet and which nodes have shown to be clean. The findings can be used to inform the security system or to inform automatic counteractive measures directed at the infected nodes so as to contain the intrusion. The ecosystem of big data presented in this paper employs _network modeling_ and _statistical inference_ to detect botnet nodes present within Cloud environments, taking into consideration data overload and complex networked interactions among the nodes. This layered approach enables robust and scalable analysis, optimized for use in _high-traffic_ Cloud environments. The scalability of solutions plays a fundamental role, in fact, it is well understood that classic ML algorithms cannot be used _\"as they are\"_, but specialized solutions are needed to manage the _well-known_ 3V characteristics of big data (i.e., volume, _Velocity_, and _variety_), as highlighted in recent proposals from the active literature (e.g., [18]). This multi-layer structure of the framework enhances the adaptability and integration of advanced analytics tools to capture complex patterns in real-time, which makes it a resilient choice for evolving cybersecurity challenges in Cloud-based systems.
# Effect of Fuel Composition on Carbon Black Formation Pathways
## 3 Results and Discussion
### Carbon Black Nanostructure  
Carbon black nanostructure has previously been shown to be a function of its formation conditions [14; 15]. Recently, we demonstrated the impact of different fuels on soot nanostructure [9].  
Figure 3 shows high resolution transmission electron microscopy (HRTEM) micrographs of CT-CB versus FCC-CB. The CT-CB shows a disordered nanostructure within its particles. Lamellae orientation at the core of the particle is amorphous and unstructured. In contrast, FCC-CB particle cores lack visible disorder. Lamellae appear ordered, somewhat concentric, following the orientation of the perimeter lamellae that comprise its shell.  
Figure 3: HRTEM micrographs showing nanostructure of (**A**) CT-CB and (**B**) FCC-CB.  
Figure 2: TEM micrographs showing particle structure for (**A**) CT- and (**B**) FCC-CB black.  
Carbon black nanostructure can be quantified by lattice fringe analysis for lamellae length and curvature. Fringe analyses for the two carbon blacks are compared next.
## 3 Results and Discussion
### Human Health Risks Assessment
Carcinogenic risk to children from single exposure routes also falls within an acceptable range (Figure 4e,h). Only As poses a significant risk in certain areas for children. Regarding different exposure routes, the human health risks of ingestion is higher than that of inhalation (Figures 3d,h and 4d,h), and ingestion is the primary exposure route. Non-carcinogenic risks are higher in children compared with adults (Figure 3d,h), and  
Figure 4: Carcinogenic Risk (CR) of PTEs via ingestion and inhalation exposure pathways calculated for adults (**a–d**) and for children (**e–h**). In detail: (**a**) CR of each element through ingestion; (**b**) CR of each element through inhalation; (**c**) CR of each element through ingestion and inhalation; (**d**) TCR of combined elements through ingestion and inhalation. Figure 4e–h is similar to Figure 4a–d. The red dotted line reflects the safety threshold for carcinogenic risk. Dark solid circles represent outliers. this trend is similar for carcinogenic risks (Figure 4d,h). Children are potentially more susceptible to health risks and are more sensitive to PTEs than adults, and this is consistent with the findings of other researchers [20]. Due to the low oral reference dose and high oral slope factor of As (Table 3), As has high toxicity characteristics. The high concentration areas of As are mainly distributed in Longnan county (Figure 2), posing both carcinogenic and non-carcinogenic risks.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 1 Introduction
However, these protocols attempt to choose the route with shorter path instead of route with best quality path. Hence, these protocols lead to high power consumption in WBANs. Towards this end, this paper presents a green computing framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Here, it is noteworthy that literature did not consider multipath oriented path loss-oriented impacts while calculating link efficiency. However, our major novelty is on incorporating multipath path loss-oriented packet reception rate, and interference effect on link quality calculation along with distance and residual energy considerations. Our overall contribution in this paper can be summarized as follows:  
* Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. * Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. * A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. * The comparative performance evaluation has been carried out focusing on energy-oriented metrics under WBANs medical environments. Furthermore, the related previous work is presented in Section 2 of this paper, modeling detail of the proposed ELR-W framework is discussed in Section 3. Section 4 discusses simulation results and analysis, followed by Section 5 where the conclusion of this study and future direction are presented.
# 3 MSF: A Multi-Modal Adaptation of the 6 TiSCH Minimal Scheduling Function for the Industrial IoT
## 5 Simulations and Results
### Scenario 2: Three Nodes  
Figure 17 depicts the setup of the second scenario, in which two non-root nodes (#2, and #3) were positioned 50 m away from the root node (#1) along the x- and y-axes, respectively (meaning #2 and #3 were separated by \(\approx\)70.7 m). This scenario did not involve breaking any physical links. Instead, the network was simply left to operate under normal conditions during the entire 20 min simulation run (we ran 40 simulations of this scenario in total).  
For each slotframe number, Figure 18 gives an impression of the fraction, by slotframe handle, of the total number of used Tx links since the start of the network from the root's perspective (assuming the slotframe in which the root broadcasts its first EB is the slotframe with number 0, and so on). Put differently, the slotframe number indicates how many 3 MSF slotframes have elapsed since the root started advertising, and for any such point on the x-axis, the y-axis is divided according to the fraction of the total number of used Tx links (from a single given node to a single given neighbor) up to and including that point which were of a certain type.  
As you can see from the cumulative negotiated Tx link usage from #2 to #3, and vice versa, evolving to 0 (and being very small to begin with), the intended child-parent relationships were eventually achieved in all cases (and immediately in most).  
Additionally, the graphs show the probing Tx supercell to the preferred parent being used slightly more often than the negotiated Tx supercell(s). That is, due to the relatively low amount of upstream traffic in this very small network, there was only one negotiated Tx supercell to the preferred parent, and the probing mechanism comparatively needing more Tx opportunities on probing supercells to obtain fresh metrics for all interface-modes of the preferred parent hence had a noticeable impact on the ratio of probing versus negotiated Tx link usage towards it.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 7 Field Test  
A field test in an urban area was conducted to validate the simulation results obtained in the previous sections, and to evaluate the adaptive tracking method in the actual environment. The test system consists of a GNSS antenna connected to an acquisition board, a MEMS-IMU and a magnetic compass for initial alignment. The acquired data are post-processed in the laboratory using the  
Figure 23: The output of PD (**left**) and the corresponding PLI (**right**) for SV10 in Trajectory 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Position Error (m)**} & \multicolumn{3}{c}{**Velocity Error (m/s)**} \\ \hline
**Method** & **Adaptive Method** & **Method 1** & **Method 2** & **Adaptive Method** & **Method 1** & **Method 2** \\ \hline RMS & 8.9449 & 9.5028 & 9.5726 & 0.9431 & 1.9048 & 1.4951 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSs of the position and velocity errors in Trajectory 2.  
Figure 24: Position (**left**) and velocity (**right**) errors during the simulation in Trajectory 2.  
adaptive tracking method to derive a navigation solution. The experimental setup used is shown in Figure 25.  
The MEMS-IMU is the XW-IMU5220 from Beijing Starneto Technology Co. Ltd, (Beijing, China). The bias stability of gyro and accelerometer are 0.02\({}^{\circ}\)/s and 8 mg, respectively. The output frequency is 100 Hz. The measurement precision of magnetic compass is 1.5\({}^{\circ}\). GPS IF data is collected by an acquisition board, whose front-end bandwidth is 40 MHz, and it is driven by the TCXO referred above. The sampling frequency is 12 MHz. The experimental site is on the east side of Datum Road, in the Beijing Olympic Park. The car moves along a rectangular block for about 7 min. The sky plot is shown in Figure 26. The number of visible satellites changes frequently due to the blockage and attenuation of surrounding buildings and trees. The satellites 19, 11, 1, 32, and 24 are not always available during the experiment.  
Three tracking methods that are referred in Sections 5 and 6 are tested and compared in this part, which are adaptive method and the methods presented in [8; 15], respectively. The tracking performance of SV6 of these three methods is illustrated in Figure 27, and the position solutions are shown in Figure 28. It can be seen that the tracking quality and positioning accuracy of the adaptive method are better than the other two methods.  
Figure 26: Sky plot of the satellites in field test.  
Figure 25: Experimental setup used for the field test.  
In summary, the experimental results obtained from the field test validate the simulation results described in the previous sections. It can be seen that in a complex urban environment, the adaptive INS-aided PLL tracking method shows better accuracy and stability than the methods of adjusting the noise bandwidth presented in [8,15].
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions
### Scalability and Reliability  
As the number of connected vehicles continues to increase, ensuring the scalability and reliability of V2V communication systems becomes paramount. Future research should explore novel approaches for managing network congestion, optimizing resource utilization, and mitigating communication delays in large-scale vehicular networks. Additionally, developing resilient communication protocols capable of tolerating network failures and disruptions is essential for ensuring continuous and reliable V2V communication.