esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 5 Experiments
### Azimuth Radiometric Variation  
Seven point targets are set along the swath edge, 4.5 km from the swath centerline and 1.5 km apart in the azimuth direction. After the correction with the reference EAP, the parameters of each target response, including the integrated energy, are measured. Table 2 summarizes the target response measurements. From the results, the relative radiometric variation reaches 1.69 dB, despite the correction with EAP. As previously mentioned, this high variation is caused by ignoring the 2D variation of the antenna pattern. The results conform to the effect analysis in Section 3.  
Accounting for the 2D change in EAP, the proposed method of antenna pattern correction is applied in the processor. The parameters of each target response are measured in the calibrated image, as shown in Table 3. Comparing Tables 2 and 3, the improvement in the relative radiometric accuracy is apparent, which means the proposed method can effectively correct the antenna pattern effect in the image with high precision. Results show that the azimuth radiometric variation is reduced from 1.69 dB to better than 0.1 dB, as shown in Figure 8. Meanwhile, the image quality parameters, such as azimuth peak-to-sidelobe ratio (PSLR) and integrated side-lobe ratio (ISLR), are slightly improved as a side effect.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 3 Effective Factors in the CO\({}_{2}\) Absorption by Nanofluid
### Effect of Nanoparticle Size  
Many different types of nanoparticles were used in investigations of CO\({}_{2}\) mass transfer efficiency enhancement by the addition of nanoparticles to aqueous solutions as Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), and TiO\({}_{2}\) nanoparticles [100; 101]. [PERSON] et al., achieved more than 200% mass transfer enhancement by the addition of 10 vol% of n-hexadecane nanoparticles with 65 nm size into a fluid. According to the results, the rate of mass transfer increases rapidly at low concentrations of nanoparticles, whereas its enhancement is slow at higher concentrations of particles (more than 6 vol%) [102]. [PERSON] and [PERSON], concluded that the addition of smaller Al\({}_{2}\)O\({}_{3}\) nanoparticles to a NaCl solution shows higher enhancement in the CO\({}_{2}\) absorption capacity of the fluid [92]. [PERSON] et al., used an agitated microreactor to show the superiority of a nanofluid containing mesoporous silica materials (MCM41) with a mean particle size of 250 nm compared to micro-sized silica particles (1.4 and 7 mm), in CO absorption by water [103]. A study of the literature shows that [PERSON] et al., were the only research group that studied the influence of nanoparticles on the mass transfer performance of nanofluids. They dispersed silica nanoparticles with average particle sizes of 30, 70, and 120 nm in water to reach a nanofluid with 0.021 wt.% nanoparticle concentration. Results showed up to 76% enhancement in CO\({}_{2}\) absorption of a nanofluid containing 0.021 wt.% nanoparticles, 24% of this enhancement occurred in the first minute of a total 8 min duration of the absorption process. These increases were 11% and 12%, respectively, for nanofluids containing K\({}_{2}\)CO\({}_{3}\) nanoparticles. The conclusion of their investigation was the contribution of small bubbles that exist in nanofluids in the enhancement of mass transfer [90]. [PERSON] et al. suggested that the improvement of volumetric mass transfer coefficient continues until the particle size reaches 60 nm, and a further increase in particle size does not affect the efficiency of nanofluid CO\({}_{2}\) absorption [96]. Generally, increasing the volume fraction of nanoparticles will increase the enhancement factor, while enhancement of nanoparticle size decreases this enhancement factor [47].
## 3 Performance Analysis
### Optimization of Transmission Power  
With fixed \(B_{1}\), \(B_{2}\), \(x\), and \(y\) values, transmission power optimization is formulated as  
\[\min\left(\frac{D_{1}}{B_{1}\log_{2}\left(1+\frac{P_{1}h_{0}}{B_{1} d_{1}^{2}(x,y)e^{nd_{1}}\sigma^{2}}\right)}+\frac{D_{2}}{B_{2}\log_{2}\left(1+ \frac{P_{2}h_{0}}{B_{2}d_{2}^{2}(x,y)e^{nd_{2}}\sigma^{2}}\right)}\right) \tag{4}\] \[subject\,\,to\,\,\log_{2}\left(1+\frac{P_{1}h_{0}}{B_{1}d_{1}^{2} (x,y)e^{nd_{1}}\sigma^{2}}\right)\geq\frac{D_{1}}{B_{1}E},\,\log_{2}\left(1+ \frac{P_{2}h_{0}}{B_{2}d_{2}^{2}(x,y)e^{nd_{2}}\sigma^{2}}\right)\geq\frac{D_ {1}}{B_{2}E}\]  
where \(D_{1}\) denotes the amount of data that user 1 must send to the UAV. \(D_{2}\) represents data that the UAV has to deliver to user 1. \(E\) represents the user's entire maximum energy. If(\(x,y,z\)) indicates the location of the UAV, the optimal transmission power of user 1, \(P_{1}\)*, can be calculated as  
\[P_{1}^{*}=-\frac{EB_{1}(w_{1}e^{w_{1}})^{\prime}}{D_{1}\ln 2}-\frac{B_{1}d_{1}^{2 }e^{xd_{1}}\sigma^{2}}{h_{0}} \tag{5}\]  
where \(w_{1}=-\left(EB_{1}{}^{2}d_{1}^{2}e^{xd_{1}}\sigma^{2}2^{-\frac{B_{1}d_{1}^{2 }e^{xd_{1}}\sigma^{2}D_{1}}{h_{0}EB_{1}}\ln 2}\right)/D_{1}h_{0}\). Given the UAV location, (\(x,y,z\)), and the bandwidth of the UAV, \(B_{2}\), the optimal transmission power of the UAV, \(P_{2}\)*, can be written as  
\[P_{2}^{*}=-\frac{EB_{2}(w_{2}e^{w_{2}})^{\prime}}{D_{2}\ln 2}-\frac{B_{2}d_{2}^{2 }e^{xd_{2}}\sigma^{2}}{h_{0}} \tag{6}\]  
where \(w_{2}=-\left(EB_{2}{}^{2}d_{2}^{2}e^{xd_{2}}\sigma^{2}2^{-\frac{B_{2}d_{2}^{2 }e^{xd_{2}}\sigma^{2}D_{2}}{h_{0}EB_{2}}\ln 2}\right)/D_{2}h_{0}\). Since problem (3)'s objective function is a lessening function with regard to \(P_{q}\), the optimal \(P_{q}\)* value is the maximum value in the feasible region (\(q\in 1\), \(2\)). Based on (3), we find that \(\log_{2}\left(1+\frac{P_{q}h_{0}}{B_{q}d_{2}^{2}e^{xd_{1}}\sigma^{2}}\right)/P _{q}\) is a decreasing function with regard to \(P_{q}\). Therefore, \(P_{q}\)* fulfills the following equation: \(\frac{D_{q}}{B_{q}E}=\log_{2}\left(1+\frac{P_{q}^{*}h_{0}}{B_{q}d_{2}^{2}e^{xd _{1}}\sigma^{2}}\right)/P_{q}^{*}\).
## 3 Similarity Analysis Approach
### Similarity Analysis with the Co-Occurrence Matrix  
This section explains mathematical notations used to describe the model. \(P\) is represented as an occurrence matrix with the order as \(m\times n\). The \(n\) denotes location (each building has its access point), and \(m\) represents a particular day. Let \(p_{ki}\) define the specific time in the \(k\)th row at the \(i\)th is the location of \(P\), where \(M\) denotes the occurrence matrix of user \(1\ldots n\), \(M\) is a matrix in the order of \(m\times n\). Let \(M_{ij}\) represent the time in rows, and \(j\)th the location of \(M\). For \(i\) is not equal to \(j\) when \(M_{ij}\) equals the number of co-occurrences of the time and location, \(I\) and \(j\).  
\[M_{ij}=\sum_{k=1}^{m}p_{ki}p_{kj} \tag{1}\]  
Suppose \(T_{j}\) denotes the total object occurrence or object co-occurrence at \(j\) (location).  
\[T_{i}=\sum_{k=1}^{m}p_{ki} \tag{2}\]  
Suppose the total object occurrence or object co-occurrence at \(j\) (location) is denoted by \(T_{j}\).  
\[T_{j}=\sum_{k=1}^{m}p_{kj} \tag{3}\]  
Direct Similarity Measure  
The \(F_{c}\) shows the ratio between the observed time of users \(i\) and \(j\), separately. The cosine angle is used between the \(i\)th and \(j\)th columns of the co-occurrence matrix of \(P\) for measuring.  
\[F_{c}=(M_{ij},T_{ij})=\frac{M_{ij}}{\sqrt{(T_{i},T_{j})}} \tag{4}\]  
In the above-given model, the concept of cosine similarity is used to show the similarity of patterns in the mobility traces of the users. The user's movement pattern is considered a vector and the angle between the vectors is inversely proportional to the similarity between the vectors, i.e., the mobility traces of the users. The inherent idea of cosine similarity is that the vectors point in the same direction and have a higher similarity irrespective of their Euclidean distance. Here, if two users follow a similar movement path, then they would have a higher similarity. The similarity is modeled as a probability in this context.
# Enhanced Methyl _Tert_-Butyl Ether Removal by Mixed Consortium: Performance and Adaptability
## 4 Conclusions  
This study was conducted to evaluate the degradation potential of co-cultures of three strains in a system contaminated with MTBE and other gasoline components. The conclusions are as follows:  
1. A mixed consortium was successfully constructed by mixing three pure strains, which increased the degradation efficiency by about 20% at a concentration of 30 mg/L. Furthermore, the synergistic system containing the mixed consortium degraded MTBE with a rate three times faster than any of its individual components at 10 mg/L, and showed a shorter, if any, lag periods during MTBE degradation. This confirms the feasibility of cultivating a mixed consortium, which can improve the efficiency and rate of biodegradation.
2. The addition of alkanes, BTEX or metals affected MTBE removal. Therefore, the effect of co-contamination of gasoline components is supposed to be considered in MTBE biodegradation.
3. Compared with pure strains, a mixed consortium improved MTBE removal in the ternary system and exhibited stronger resistance to metals. Furthermore, a mixed consortium improved the degradation efficiency at either low or high temperatures, showing a wider adaptability to environmental conditions. This supports the mixing  
Figure 5: Tolerance of strains to temperature and pH. The growth of strains (scatter chart) and the yield of degradation (bar chart) carried out on various (**a**) temperature and (**b**) pH. ** represents \(p<0.01\).  
of different pure strains for MTBE bioremediation in case of coexistence of other contaminants in practical applications.
* In addition to mixed bacterial cultures, other synergistic systems containing fungi-bacteria also deserve further investigation to develop an efficient biological system for MTBE biodegradation and mineralization.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app13042144/s1](https://www.mdpi.com/article/10.3390/app13042144/s1)]([https://www.mdpi.com/article/10.3390/app13042144/s1](https://www.mdpi.com/article/10.3390/app13042144/s1)), Figure S1: Time course for strains growth with the addition of (a) dodecane, (b) benzene and (c) toluene.  
All authors contributed to the conception and design of the study. Methodology, S.L. and W.Y.; software, T.H. and L.W.; validation, T.H.; formal analysis, T.H.; investigation, T.H.; resources, T.H.; data curation, T.H.; writing--original draft preparation, T.H.; writing--review and editing, T.H. and S.L.; supervision, W.Y.; project administration, S.L. and L.W.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant number 31300438), and the Natural Science Basic Research Plan in Shaanxi Province of China (Grant number 2018 JM3039).  
Not applicable.  
Data Availability Statement: Data from this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 1 Introduction
In addition, the reliable and efficient transmissions of WSA messages, as well as the ratio between CCHI and SCHI affect the throughput and utilization of SCHs. This paper focuses on the performance analysis of the IEEE 1609.4 multichannel MAC protocol including the broadcast of safety-related messages on the CCH and the transmission of non-safety messages on the SCHs under a VANETs environment. We enhance the existing work by taking the following aspects into model considerations. Figure 1: Frequency channel layout of a 5.9-GHz WAVE system. 1. We present two Markov chain models under both unsaturated and saturated conditions: one-dimensional Markov chain model for higher priority AC\({}_{0}\) and two-dimensional Markov chain model for lower priority AC\({}_{1}\) to analyze the real-time and reliability of safety-related messages broadcast and the WSA messages unicast on the CCH, respectively. Two M/G/1 queuing models are leveraged to derive more accurate analytical results. 2. We take virtual collision, Arbitration Inter Frame Space (AIFS) differentiation, Contention Window (CW), the retry limit, channel switch, and the difference among frame collision probability, frame failure probability, frame blocking probability, and channel busy probability into design consideration. 3. We calculate the Packet Reception Rate (PRR) and packet transmission delay by considering the internal virtual collisions, the collisions caused by the concurrent transmissions in the carrier-sensing range and hidden terminal range, and the packet error by fading channel. 4. We calculate the throughput of SCHs to analyze the CCH bottleneck problem and the utilization problem of SCHs, and then give the direction of design and research of multichannel in VANETs.
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 4 Formation Control Algorithm
### Ad Hoc Connectivity Force  
The loss of the ad hoc connection for a UAV could lead to a serious tragedy, which is not only the drastic reduction of the performance but the loss of the control for the UAV. In our formation control design, ad hoc connectivity force has the most dominant magnitude if the connections come to weaken, mostly by the path loss due to the distance. We call _neighbors_ that could be the next hop of a certain UAV in ad hoc network. With respect to the Euclidian distance to the next hop nodes, ad hoc connectivity force \(F_{adhoc}\) is expressed to  
\[F_{adhoc}=\sum_{H}\max\left(\exp(|\mathbf{p_{j}}-\mathbf{p}|-A)-1,0\right)( \mathbf{p_{j}}-\mathbf{p}) \tag{10}\]  
where \(H\) is referred to the set of the next hop nodes and \(A\) is named to the _ad hoc connectivity force threshold_. Designing \(F_{adhoc}\) as the distance to the next hop nodes is valid since the UAVs are flying above the flat area in the assumption stated in Section 3.1. \(F_{adhoc}\) is applied to the UAV only the UAV is far enough to the next hop nodes to jeopardize the ad hoc connectivity. The threshold \(A\) is determined with respect to the ad hoc communication range \(R_{adhoc}\). In our implementation, we observed that the performance is maximized when \(A=0.8R_{adhoc}\).
## 2 Materials and Methods
### FAA Determination  
Initially, 1 g sample was extracted with 50 mL of 0.01N hydrochloric acid for 30 min, shaken, and filtered. Then, 2 mL of the filtrate was accurately pipetted into a centrifuge tube, and 2 mL of 8% sulfosalicylic acid was added and allowed to stand for 15 min. The supernatant was tested after passing through a 0.22-\(\upmu\)m membrane. Sykam S-433D, including LCAK07/LI-type standard analytical cation exchange resin, was used to test 17 FAA in fermented pork loin ham. The injection volume was 50 \(\upmu\)L. The detection wavelength used in channel 1 was 440 nm, which was used to detect proline. The detection wavelength used in channel 2 was 570 nm, which was used to detect other amino acids. The infusion pump pressure was 0-4.2 MPa. The elution pump flow rate was 0.45 mL/min. The minhydrin derivatization pump flow rate was 0.25 mL/min. The separation column temperature was 37 \({}^{\circ}\)C, and the reactor temperature was 130 \({}^{\circ}\)C. Flow phases: A was citric acid-lithium citrate buffer at pH 2.9; B was citric acid-lithium citrate buffer at pH 4.2; C was citric acid-lithium citrate buffer at pH 8.0; and D was 0.5 mol/L lithium hydroxide regeneration solution. FAA were confirmed by comparing the retention times and peak areas of the particular amino acid standards (WAKO-H type) with those of the components present in the samples. The following amino acids were monitored: glutamic acid (Glu), alanine (Ala), lysine (Lys), leucine (Leu), valine (Val), glycine (Gly), arginine (Arg), phenylalanine (Phe), isoleucine (Ile), proline (Pro), threonine (Thr), serine (Ser), tyrosine (Tyr), histidine (His), methionine (Met), aspartic acid (Asp), and cysteine (Cys). Three parallel tests were performed on the sample.
# A Low-Phase-Noise 8 GHz Linear-Band Sub-Millimeter-Wave Phase-Locked Loop in 22 nm FD-SOI CMOS
## 2 System Architecture and Circuit Design  
The proposed type II sub-mm-wave PLL is given in Figure 1 and is composed of a balanced I/Q VCO, injection-locked divider (\(\div\)4) and \(\div\)64 CML frequency dividers, a phase/frequency detector (PFD), charge pump (CP), and a loop filter (LF). An external signal generator is used to provide a reference signal between 4.984 GHz to 5.234 GHz. This signal is then divided by eight using a \(\div\)2 CML divider chain to provide the PFD reference signal between 623.047 MHz and 654.297 MHz. The linear-tuning output frequency of the PLL ranges from 159.5 GHz to 167.5 GHz, corresponding to the VCO output frequency range.  
Figure 1: Block diagram of the proposed sub-mm-wave PLL.  
The output signal of the I/Q VCO is amplified by a three-stage CS power amplifier with a power gain of 10 dB, a maximum saturated power of 10 dBm, and OP1 dB of 7 dBm.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Comparative Analysis
#### 4.6.4 Comparison with Traffic Grouping-Based Detection Method  
In this section, the DBM-ENSec model is evaluated against four methods for detecting DCC that are based on traffic grouping. The four methods include (1) converting the feature data of DNS traffic into grayscale images, followed by the application of convolutional neural networks to learn and classify these images for the identification of DCC; (2) passively extracting DNS network data from network monitoring systems and employing machine learning algorithms to develop models that encapsulate the traffic behavior characteristics of network users for the detection of DCC; (3) detecting DCC through the integration of multi-head attention mechanisms and residual neural networks, which combine session statistical features with multi-channel session byte sequence features; and (4) identifying DCC by merging artificial intelligence models with a dynamic human-computer interaction mechanism, which generates diverse malicious traffic samples and incorporates user validation to enhance the accuracy and practicality of detection. The results of this comparative analysis are presented in Table 7.  
The experimental results indicate that the DBM-ENSec model surpasses traffic packet-based detection methods, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Fast Fourier Transform-based Multiresolution (FF-MR), and Ran  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Mold** & **Accuracy** & **Precision** &
\begin{tabular}{c} **Recall** \\ **Rate** \\ \end{tabular} & **F1 Score** \\ \hline \multirow{5}{*}{[PERSON], 2019 [5]} & SVM & 89.92\% & 90.13\% & 89.92\% & 89.85\% \\  & NB & 79.80\% & 84.15\% & 79.80\% & 79.61\% \\  & RF & 95.12\% & 93.35\% & 92.41\% & 92.88\% \\  & DT & 93.74\% & 91.42\% & 89.87\% & 90.63\% \\ [PERSON], 2021 [7] & LSTM & 98.23\% & 98.54\% & 97.98\% & 98.26\% \\ Shen, 2023 [8] & DSR-GAT & 98.77\% & 98.99\% & 98.54\% & 98.80\% \\ [PERSON], 2024 [10] & LightGBM & 99.01\% & 99.12\% & 98.77\% & 98.94\% \\
**Our Work** & **DBM-ENSec** & **99.92\%** & **99.92\%** & **99.91\%** & **99.91\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with domain name-based detection methods.  
dom Forest (RF), in the context of DCC detection. This model demonstrates considerable advantages in terms of accuracy, precision, recall, and F1 score.
## 3 Results and Discussion
### Measurement Results of the Classroom
#### 3.2.2 Results of the Second Method  
The measured 72 decay curves are presented in Figure 12.  
Based on Figure 12, we can observe that the moment of switching off the sound source is different for each measurement, but the decay curves should be arranged as a parallel shift with respect to each other. Naturally, this is not the case due to various factors such as measurement uncertainty, irregular shapes of the room or non-uniform structure of the partitions delimiting the room. These factors have impact on different reverberation times recorded for different measurement point arrangements (see Figures 2 and 3).  
Proceeding in the same way as in the case of laboratory tests, we determine the regression equation:  
\[L_{p}=-3.1712\cdot x+91.4848, \tag{18}\]  
Figure 11: Results of 72 reverberation time measurements—500 Hz.  
where \(x\) is the number of the point read out from the decay curve (Figure 13).  
The test probability for linear estimation is of the order of \(p\) = 0.05, which means that all regression parameters are statistically significant. In this case,  
\(L_{1}\) = 88.3 dB, \(L_{6}\) = 72.5 dB, \(\Delta L\) = 19.5 dB, \(u(L)\) = 0.11 dB.  
The average time increment is \(\Delta\tau=0.67\) s, while the mean standard deviation is \(\sigma_{\tau}=0.094\) s. When determining the standard uncertainty from (10), we obtain \(u(\tau)\) = 0.011 s.  
Ultimately, we have \(T_{500}=2.544\) s, \(u_{c}=0.046\) s, and hence \(T_{500}=2.544\pm 0.046\) s.
# Optimal Robust LQI Controller Design for Z-Source Inverters
## 6 Conclusions  
This paper addressed the optimal design of a robust LQI controller for a ZSI, considering uncertainties, disturbances, and nonminimum phase issues. The small signal and extended models were explicated using state-space averaging. The ZSI possessed a nonminimum phase characteristic with a real right-half plane zero, which imposed special constraints on the performance of the control system. While load and shoot-through duty-cycle were considered to be uncertain or time-varying parameters with known bounds, external disturbances were modeled as an output current source. The BA optimization algorithm was utilized to find optimal weighting matrices according to the ITSE performance index. The robustness of the proposed method was also investigated using maximum sensitivity criteria. To assess the performance obtained by the proposed controller, the results were compared with PI and SF controllers. Conducted simulation and experimental results demonstrated the efficiency and effectiveness of the proposed robust method in terms of both transient and steady-state.  
Conceptualization, A.A. and B.M.-I.; formal analysis, B.M.-I. and A.A.-M.; methodology, A.A.; software, A.A.; investigation, A.A.-M. and M.M.; supervision, B.M.-I.; validation, A.A.-M. and M.M.; writing--original draft, A.A.; writing--review and editing, B.M.-I., A.A.-M. and M.M.; funding acquisition, A.A.-M. and M.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.