esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 6 Results and Discussion
### The Effect of Parallel Streams in HTTP  
The number of parallel streams used to fetch the objects in a web page can affect the load time significantly. Thus, we tested the download of multi-object web pages and compared it to single-object web pages of the same total size. The uniform structure was used for the multi-object web page and the total size of all web pages was set according to Table 3. The test used BBR congestion control for both the direct LAN link and the satellite link.  
The average page load times (PLTs) of uniform multi-object web pages and of single-object web pages are shown in Figures 7 and 8, respectively. In multi-object web pages, depicted in Figure 7, we can see that HTTP/1.1 has the longest PLT compared to HTTP/2 and HTTP/3 because it operates with one stream only. This is because of the head of line (HoL) blocking at the application layer where one object blocks the transmission of the other objects until it is fully received. Thus, the multiple objects are transmitted sequentially in HTTP/1.1 over a single TCP connection. To overcome the HoL issue, HTTP/2 uses multiple streams, one stream per object, where all objects are transmitted in parallel to prevent HoL blocking at application layer. This significantly reduces the PLT compared to HTTP/1.1 but it still suffers from HoL blocking at the transport layer because all HTTP/2 streams are multiplexed into a single TCP connection. On the other hand, HTTP/3, which relies on QUIC non-blocking multiplexing at the transport layer, produces much smaller PLTs, about 55% less than HTTP/1.1 and 30% less than HTTP/2.  
The situation is almost reversed when the page consists of a single object only where HTTP/2 performed worse than HTTP/1.1 as shown in Figure 8. This is because stream multiplexing is not applicable in this scenario and we are left with the bigger protocol overhead of HTTP/2, e.g., from TLS security and bigger header size, which increase the page load time. However, HTTP/3 still outperforms HTTP/1.1 and HTTP/2 in this scenario because of the nature of QUIC which reduces the initial connection setup time.  
The protocol overhead in HTTP/2 and HTTP/3 becomes the dominant factor in the performance over small RTT links such as the direct link scenarios shown in Figures 7a and 8a. With multi-object pages, the gain obtained from stream multiplexing helps offset the protocol overhead produced in HTTP/2 and HTTP/3 compared to HTTP/1.1. However, with single-object web pages, HTTP/1.1 performs better over small RTT links. We can conclude that the stream multiplexing provides the highest gain when operating over high RTT links such as satellite links.
## 5 Conclusions
To facilitate a more intuitive analysis and comparison of the data, as well as to eliminate environmental interference, the response value of each point \(r(i)\) is converted to its corresponding SMR value \(\hat{r}(i)\). This is accomplished by dividing \(r(i)\) by the average response value of the entire region:  
\[\hat{r}(i)=\frac{r(i)}{\frac{1}{n}\sum_{i=1}^{n}r(i)}. \tag{1}\]  
In the preliminary localization process, the computational method involves sorting spatial points in descending order, followed by smoothing to obtain \(\widetilde{r}(i)\):  
\[\widetilde{r}(i)=Smooth(Sort_{Desc}(\hat{r}(i))). \tag{2}\]  
Subsequently, the rate of descent between adjacent points is calculated. A slope threshold \(\theta\) is established, which is used to determine the corresponding response intensity threshold. Regions exceeding this response intensity threshold \(p_{output1}\) are then extracted for further analysis:  
\[p_{output1}=\{p(i):i\leq max\Bigg{\{}j:\frac{\widetilde{r}(j)-\widetilde{r} (j+1)}{d_{j}}>\theta\Bigg{\}}\}. \tag{3}\]  
The slope threshold \(\theta\) is an empirical value determined through experimentation. For all types of grains and containers, recalibration is necessary to ensure that all anomalous regions are included after the preliminary localization process. Notably, \(\theta\) does not exhibit a strong correlation with final localization results, making its determination relatively straightforward and applicable to the majority of cases. Extensive experimental testing has demonstrated that, given a constant volume of the anomalous region, an increase in grain moisture content corresponds to an increase in the number of points displayed. The aim is to localize grain with 15% anomalous moisture content (\(\theta\approx 0.01\)). Consequently, when measuring grain with other moisture contents, the region obtained after preliminary localization is typically much larger than the actual volume of the anomalous region. In the secondary localization process, nevertheless, experimental evidence suggests a high probability that the anomalous region is located within the merged area of these two methods:  
\[p_{2}=D_{max}\cup D_{vol},\Big{(}\{D_{max},D_{vol}\}\subseteq p_{output}\Big{)}, \tag{4}\]  
where the value of each point within \(p_{2}\) is \(r_{2}(i)\). Finally, the responses within \(p_{2}\) are arranged in descending order. Based on the predetermined volume of the area to be detected, the strongly responsive points corresponding to this volume size are  
\[p_{output2}=\{p_{2}(j):j\leq V_{N}\;and\;j\in Sort_{Desc}(r_{2}(i))\}, \tag{5}\]  
where, \(V_{N}\) represents the number of voxels calculated based on the volume of the area to be detected and the spatial resolution.
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 5 Enhancement of Security  
Shift Row is included in the AES algorithm analysis in Section 4.1. The encryption algorithm determines the number of bytes that should be shifted in each row of a matrix by a certain number, determined by the encryption algorithm. Anyone can descript Shift Row (=Inverse Shift Row) since it changes the bytes of each row of the matrix to a given integer. Because one of the decryption processes, inverse shift row, proceeds in the opposite direction of the Shift Row process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Total** & **Resources** \\ \hline Gates & 7518 \\ Depth & 3452 \\ Width & 96 \\ Qubits & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantum resource estimation per iteration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**File Size** & **Byte** & **Iteration** \\ \hline
500 KB & 512,000 & 32,000 \\
100 MB & 104,857,600 & 6,553,600 \\
0.5 G & 536,870,912 & 33,554,432 \\
1 G & 1,073,741,824 & 67,108,864 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Iteration by file size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**File Size** & **Python** & **Qiskit** \\ \hline
500 KB & 0.07 & 715,968 (estimated) \\
100 MB & 14.12 & 147,458,110 (estimated) \\
0.5 G & 72.77 & 739,875,225 (estimated) \\
1 G & 146.73 & 1,479,750,448 (estimated) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance time.  
Therefore, in this paper, unlike the existing algorithm, random numbers are generated so that the decryption method is not easily known, and rows can be moved according to the generated random numbers. The security of the system was improved by making the decryption method difficult to predict. We generated random numbers in three ways.
## 2 Materials and Methods
### Comparison between Experimental Data and DLVO Prediction
It should be noted that the magnitude of the zeta potential with a high mass ratio at CCC is significantly smaller than for bare CNHox and for CNHox coated with a low amount of LSZ. This phenomenon indicates the presence of additional non-DLVO repulsion. [PERSON] et al. [23] have stated that the saturated LSZ layer inhibits the aggregation of silica particles. [PERSON] et al. [14] have also proved that the existence of a \"BSA corona crown\" adsorbed on the surface of the colloidal particles can significantly inhibit the aggregation. In this experiment, the shift in CCC to a higher value may also be attributed to the same reason, that is, the existence of steric repulsion or a lower Hamaker constant between LSZ-saturated layers. However, comparing CNHox with silica, the former has a more complex structure than the latter, and there is still a need for more studies in this orientation. DLVO and non-DLVO interactions play important roles in the stability and assembly of colloidal particles [48]. Based on the classical DLVO theory, van der Waals attraction and electrical double-layer repulsion act together on the aggregation and dispersion of CNHoxs in the presence of LSZ. According to Figures 8 and 11, van der Waals attraction is dominant in the interaction between CNHox particles at high electrolyte concentrations due to the suppression of the electrical double layer. Meanwhile, electrical double-layer repulsion is dominant in the interaction at low electrolyte concentrations. Non-DLVO interactions cannot be ignored either. According to Figure 8, non-DLVO behavior can be found at 0.1 mM and 2.5 mM at a high mass ratio due to the charge heterogeneity on the CNHox surface. At a high mass ratio around the CCC, the calculated Hamaker constant is the smallest among other conditions, and the magnitude of zeta potential is also the smallest. This result proves the existence of non-DLVO repulsion.
# Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties
## 3 Solution Algorithms  
### Objective Function Processing  
In the above multi-objective model, the two sub-objective units are not consistent. However, for emergency management decision-makers, the time objective and cost objective should be considered together in the actual emergency vehicle scheduling process. Therefore, the percentage dimensionless method is introduced to streamline the subsequent calculation and eliminate the unit difference. By allocating the weighted value of the sub-objective, the problem is transformed from a multi-objective function to a total objective function. Assume that \(Z_{\text{C}}^{min}\) and \(Z_{T}^{min}\) are the nominal minimum of mobile emergency cost and the nominal minimum of mobile emergency time, respectively; the total objective function can be transformed into the following formula:  
\[minZ=100w_{1}(\frac{Z_{\text{C}}}{Z_{\text{C}}^{min}})+100w_{2}(\frac{Z_{T}}{Z _{T}^{min}}),w_{1}+w_{2}=1 \tag{15}\]
## 3 The Control Strategy of FMSS for Maximum Consumption of DG  
The above analysis of the consuming ability of DG in the distribution network after FMSS is connected illustrates the principle of FMSS promoting the consuming ability of DG in the distribution network. In this section, for large-scale systems, the DG that can be absorbed by each node under the rated capacity of FMSS can be solved through optimization algorithms after multiple nodes are connected to DG. Therefore, the FMSS real-time control strategy aiming at the maximum consumption of DG is realized.
## 2 Materials and Methods
### Evaluation Metrics  
Lateral spatial resolution calculated as the full-width-half-maximum (FWHM), signal-to-clutter ratio (SCR), and localization errors were used for quantitative evaluation. The SCR was the ratio of the maximum scatterer intensity and the mean clutter intensity in the image background; it is reported in decibels (dBs). In experimental and numerical studies, the localization errors were calculated with respect to the ground truth image, which was generated without an aberrator in front of the probe (not shown here) and the true coordinates of the scatterer. For the numerical study, the evaluation metrics reported for the PS method were calculated when the AP of each patch was estimated with the dataset generated with a homogeneous background, i.e., the most favorable scenario for the PS method. The FWHMs and SCRs reported in the text are mean values obtained for all the scatterers unless stated otherwise.
# Smart Manufacturing Scheduling Approaches--Systematic Review and Future Directions
## 4 Main Findings
### Manufacturing Scheduling Requirements
#### 4.1.11 Distributed Factories  
Although scheduling systems are mostly associated with the scheduling process within the factory, the scheduling process of supply chains has evolved. This increased the complexity of the problem even more, since the products may be assigned to different factories, which may be distant from each other, and the transportation across these facilities needs to be considered [40; 51].
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 6 Experimental Evaluations
### Results and Analysis
#### 6.2.3 Sensitivity Analysis  
This section conducted sensitivity analysis experiments on the MAD-XFP model. To test the impact of data noise on model performance, Gaussian noise can be added to the eigenvalues to simulate common random noise behaviors and see how they affect the model's performance, in order to examine the robustness of the proposed model. The experimental results are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{2-5}  & **Precision** & **Recall** & **F1** & **Accuracy** \\ \hline MAD-XFP & 0.999 & 0.939 & 0.968 & 0.998 \\ w/o OpenFE & 0.963 & 0.763 & 0.851 & 0.994 \\ w/o Optuna & 0.868 & 0.939 & 0.902 & 0.997 \\ w/o OpenFE and Optuna & 0.844 & 0.781 & 0.812 & 0.993 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Result of ablation study.  
Figure 7: Overall performance evaluation.  
The impact of noise on the MAD-XFP model performance can be visually observed through Figure 8. The model can maintain stable performance even at high noise levels, with an accuracy of around 85%, proving its good robustness.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 5 Applications and Prospects in Civil SHM
### Steel Structures  
Steel structures usually suffer yield or bulking damage from large deformations; hence, the target of strain monitoring of steel structures focuses on sensing large strain. [PERSON] et al. used carbon particles as conductive filler and epoxy resin as substrate to prepare a thin film for monitoring the integrity of steel structures. Results showed that the thin film was able to accurately measure the strain of the steel structure with a gauge factor of 25 and a sensing range of 0-0.6% strain, whereas the sensitivity of the sensor decreased significantly when the tensile strain exceeded 0.6% [137]. Compared with the traditional metallic foil strain sensor, this film strain sensor has a much higher sensitivity (25 >> 2) but a smaller sensing range (0.6% < 2%).  
[PERSON] et al. made a MWCNTs/vinyl resin composite strain sensor, which showed a relatively large detectable range of 20% strain. The sensor also has a linear relationship between deformation and electrical resistance within 1% tensile strain, whereas it has a nonlinear relationship in compressive strain [138]. The gauge factor of this sensor is about 2.6 (\(\varepsilon\) < 1%), which is similar to the metallic foil strain sensor. Besides, the durability of the sensor is unknown because the cyclic times of stretching and releasing in their experiment was just 10 times.  
[PERSON] et al. developed a strain sensor with CNTs as filler and epoxy resin as substrate for monitoring the deformation of steel structures. This sensor performed well within a 0.12% compression strain and 0.14% tension strain under low loading frequency and less cyclic loading times [139]. Nevertheless, the corresponding sensitivity (GF) and the sensor performance for a strain range larger than 0.14% are not given.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Materials** & **Maximum** & **Gauge Factor** & **Linearity** & **Cyclic Times for** \\  & **Filler + Substrate** & **Detectable Range** & **(Sensing Range)** & **(Strain Range)** & **Durability** \\ \hline
[125] & MWCNTs + Ecoflex & 300\% & 1.5 & Near linear & \\  & & & 0.5 (\(\varepsilon\) < 200\%), & Near linear & \\
[127] & CNT sheets + Rubber & 600\% & 0.14 (200\% \(<\)\(\varepsilon\) < 600\%) & (\(\varepsilon\) < 1\%) & \\
[130] & CNT + SPX & 80\% & 0.2\(\sim\)0.3 & Nonlinear & 270,000 (\(\varepsilon\) < 40\%) \\
[132] & CB + paper & 0.22\% & 647 (\(\varepsilon\) < 0.14\%), & Multi-stage linear & 70,000 \\  & & & 33 (\(\varepsilon\) < 0.22\%) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._The sensing properties of the above piezoresistive sensors are also shown in the first three rows in Table 4. It is indicated that the above sensors for the SHM of steel structures are still not absolutely advantageous over the traditional foil sensor, since they have either higher sensitivity but smaller sensing range, or a larger strain range but unknown durability. More work is required on the compatibility of multiple properties.
# Distribution of Volatile Compounds in Different Fruit Structures in Four Tomato Cultivars
## 2 Results and Discussion
### Differences in the Volatile Profiles among the Cultivars
#### 2.4.4 Principal Component Analysis (PCA) of Volatile Concentration of Different Tissues  
PCA was performed to analyze the variation of the 13 important volatile compounds in the four cultivars of tomato, and the results are shown in Figure 3. As shown in this Figure, the first principal component (PC1) and the second principal component (PC2) accounted for 58.3% (Figure 3a), 60.1% (Figure 3b), 62.8% (Figure 3c), and 63.4% (Figure 3d) of the total variance in \"Tasti-Lee\", \"Tygress\", \"FL47\", and \"Cherokee Purple\", respectively. PE and SC were separated from other tissues in \"Tygress\", \"FL47\", and \"Cherokee Purple\", which indicated that their flavors were different from the others (Figure 3b-d). SC and LS were separated from PE and SE, but were close to each other in \"Tygress\" and \"FL47\", which indicated that they possessed similar overall flavor (Figure 3b,c). Only LS could be separated from others in \"Tasti-Lee\", while on the contrary, all tissues could be separated from each other in \"Cherokee Purple\". Six volatile compounds, including hexanal, 3-methyl butanol, 2-methyl butanol, 6-methyl-5-hepten-2-one, 3-methyl butanal, and 2-methyl butanal were present in sufficient quantities so as to influence the tomato flavor.  
These volatile compounds were biosynthesized from various pathways in the tomato. Hexanal was generated from C18 fatty acids [30], which conferred \"grassy\", \"tallow\", \"fatty\" notes to the tomato. Also, 3-methyl butanol and 2-methyl butanol were biosynthesized via the removal of amino groups from amino acids by branched chain aminotransferase (BCATs). Subsequently, the aldehydes were produced via decarboxylation, which were then reduced to form alcohols [39]. _LeCCD1A_ and _LeCCD1B_ (carotenoid cleavage dioxygenases) cleaved the carotenoids to synthesize 6-methyl-5-hepten-2-one in the tomato [31]. Also, 3-methyl butanal and 2-methyl butanal, which imparted \"malty\", \"cocoa\", and \"almond\" notes to the tomato, were biosynthesized from the amino acids (Table 2).  
Figure 3: Principal component analysis (PCA) result of different tissues.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 5 Numerical Results and Discussions  
In this section, the performance of the proposed scheme is analyzed using numerical results obtained through Monte-Carlo simulations with \(5\times 10^{7}\) trials in some representative scenarios that consider different operating parameters (The base algorithms in Matlab\({}^{\text{\tiny{b}}}\) employed to generate the numerical results are available at this link [[https://drive.google.com/drive/folders/12](https://drive.google.com/drive/folders/12) qzMgK5 Qq5 PSbTMAnSvPYkXLRM-3 Tb6?usp=sharing]([https://drive.google.com/drive/folders/12](https://drive.google.com/drive/folders/12) qzMgK5 Qq5 PSbTMAnSvPYkXLRM-3 Tb6?usp=sharing) (accessed on 11 July 2022)).  
For simulation purposes, a MIMO system with \(A\) antennas at the BS and serving \(U\) users is assumed, where the transmitted symbols belong to a constellation with normalized mean power, i.e, \(\text{E}[s_{k}^{2}]=1\). In addition, it is considered that all users transmit simultaneously over the same frequency subcarrier from a wireless system operating over flat fading, i.e., there is no presence of ICI or ISI (See Section 2.1). For the Rician fading, it is assumed that \(2\sigma^{2}=1\), which from (3) implies that the mean power of the non-line-of-sight multipath has been normalized. The performance is analyzed in terms of the OP, the BER and the SOP of the MIMO system, in the last case considering the presence of a spy receiver (eavesdropper). More details related to the simulation parameters are presented in the description of each figure.
## 4 MMPD Performance Analysis
### High Phase Density Analysis in CDR Loop  
The purpose of improving MMPD is to improve CDR loop performance. Investigate the effect of high phase-detection density on CDR loop performance. By simplifying the CDR loop to analyze performance changes, the simplified model is shown in Figure 15.  
Figure 14: Comparison of average output voltage in linear region.  
TD is the phase-detection density determined by waveform sifting. K\({}_{PD}\) is the PD gain determined by the input jitter distribution. K\({}_{P}\) is the digital filter gain. K\({}_{DPC}\) is determined by the PI resolution.  
The open-loop transfer function:  
\[G_{L}(z)=\frac{\varphi_{out}}{\varphi_{in}}=TD\cdot K_{PD}\cdot K_{P}\cdot K_{ DPC}\cdot\frac{1}{1-z^{-1}} \tag{17}\]  
The open-loop gain K:  
\[K=TD\cdot K_{PD}\cdot K_{P}\cdot K_{DPC} \tag{18}\]  
The Closed loop transfer function:  
\[H(z)=\frac{\varphi_{out}}{\varphi_{in}}=\frac{G_{L}(z)}{1+G_{L}(z)} \tag{19}\]  
At a given jitter frequency, as the input phase \(\varphi_{in}\) amplitude increases, the bit error rate (BER) begins to increase as the phase difference \(\varphi_{in}-\varphi_{out}\) approaches 0.5 UI. Therefore, to avoid BER rising, the following conditions need to be met:  
\[\varphi_{error}=\varphi_{in}-\varphi_{out}<0.5\,\text{(UI)} \tag{20}\]  
Substituting (15) into (16), obtain:  
\[\varphi_{in}<\frac{1+G_{L}(z)}{2}\,\text{(UI)} \tag{21}\]  
The jitter tolerance expression G\({}_{jl}\)(z):  
\[G_{jl}<\frac{1+G_{L}(z)}{2}\,\text{(UI)} \tag{22}\]  
Increasing the phase-detection density increases the open-loop gain, which improves jitter tolerance, and the loop becomes more tolerant to jitter.  
According to the simplified loop equation, the index parameters of the design are given, as shown in the Table 5.