esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effectiveness of Responsive Loads  
To research the effect of SLs and HLs, three types of optimization model were established. Model 1 was to optimize the problem with SLs only. Model 2 was to optimize the problem with HLs only, and model 3 employed both SLs and HLs. A conventional model without responsive loads was also built to make a contrast. For the multi-objective optimization problems, the optimal compromise solutions are in Table 7, \(E_{W}\) represents the wind energy utilization and \(C_{GL}\) is the cost of system operation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Value**} & \multicolumn{4}{c}{**Crossover Probability**} & \multicolumn{4}{c}{**Mutation Probability**} \\ \cline{2-9}  & **0.2** & **0.3** & **0.4** & **0.5** & **0.4** & **0.5** & **0.6** & **0.7** \\ \hline Max run time(s) & 4612.5 & 4139.3 & 4243.2 & 4447.5 & 4467.3 & 4139.3 & 4388 & 4513 \\ Min run time(s) & 1563 & 1663 & 1456.7 & 1650.2 & 1546.2 & 1663 & 1469.4 & 1865.4 \\ Average run time(s) & 2579.3 & 2323.8 & 2395.5 & 2417.9 & 2387 & 2323.8 & 2378 & 2479.2 \\ Max hypervolume & 992.8 & 1140 & 1185 & 998.3 & 1025.6 & 1140 & 997.3 & 984.2 \\ Min hypervolume & 865 & 879.3 & 876.2 & 841.1 & 855.5 & 859.3 & 843.6 & 846.3 \\ Average hypervolume & 928.3 & 946.4 & 943.2 & 922.1 & 941 & 946.4 & 944 & 924.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Run time and hypervolume under different crossover probability and mutation probability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Value** & **Model 1** & **Model 2** & **Model 3** & **Conventional** \\ \hline \(E_{W}\)/MWh 9(MWh) & 41,172.5 & 39,721.4 & 41,743.5 & 39,181.7 \\ \(C_{GL}\)/\$ & 4,048,262.8 & 3,942,381.8 & 3,281,729.4 & 4,065,241.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of different optimization models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Value  
Obviously, compared with conventional type, all the three model types not only contribute to the increasement of wind energy utilization but also facilitate the decrease of system operation cost. This finding is reasonable. Despite the dispatch of responsive loads incurs expenses, the increased consumption of wind power will lead to the decrease of thermal units' output. And the cost saved by thermal units are more than the expenses of responsive loads. Thus, it can be concluded that the adoption of responsive loads can bring better effect. According to Table 4, it is not difficult to find that model 3 is the best one for it can lead to most increase of wind power utilization and most decrease of system operation costs. In other words, the effect of combining two types of responsive load is better than using the two types of load separately.
# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 5 System Design and Case Study
### The Fire and Smoke Intelligent Monitoring System Implementation  
Based on the system architecture design proposed in Section 5.1, this study constructs a data processing workflow, as shown in Figure 10a. The model initialization, preprocessing, and postprocessing methods are exported as dynamic link libraries (DLLs) for invocation by the front-end interface, with some DLL files illustrated in Figure 10b.  
Data acquisition and decoding are performed using OpenCV-Python and FFmpeg open-source software. Since mainstream cameras can provide images at 25 frames per second (FPS), which meets the image processing requirements of edge devices, there is no need to use frame extraction or delayed frame processing. The captured images are resized to 640 \(\times\) 640 before being input into the algorithm module. If an anomaly is detected by the algorithm module, an alarm is triggered, and the data are stored in a MySQL database before the monitoring process continues. The software architecture of the system is shown in Figure 10c.  
Based on the above system workflow logic, the Fire and Smoke Intelligent Monitoring System was ultimately developed, as shown in Figure 11. Specifically, Figure 11a displays the camera information interface, Figure 11b shows the monitoring log interface, and Figure 11c presents the system monitoring interface.
## 3 Results and Discussion
### Bitter Acid Profile of Hop Extracts Obtained under Different SFE-CO\({}_{2}\) Conditions  
As reported in Table 4, \(\alpha\)-and \(\beta\)-acids (soft resins) constituted the major portion of the hop SFE-CO\({}_{2}\) extracts, depending on process parameters from \(\sim\)72 to 92%. Non-polar solvent-soluble uncharacterized soft resins, hop essential oil components and waxy fraction could comprise the remaining 8-28% of the extract [2]. The percentage distribution of the individual constituents within the identified soft resins was as follows: adhumulone and humulone (25-36% of the total extract amount), colupulone (22-30%), columulone (20-25%) and finally, the sum of adlupulone and lupulone (13-15%). Considering extraction yields, 67.0-228.4 mg of these bitter acids was recovered from 1 g of hop pellets. The recovery of soft resins from hops gradually increased by 71%, increasing the P from 10 to 37 MPa (Table 4). \(\alpha\)-Bitter acids (humulones) comprised 54-64% of the total soft resins and were found in significantly varying amounts from \(\sim\)391.0 to 594.9 mg/g E, corresponding to the recovery range of 36.5-137.6 mg/g HP (Table 4). Comparing these data with the manufacturer-provided \(\alpha\)-bitter acid content in _Ella_ hops (13.4 g/100 g HP), recovery efficiency of SFE-CO\({}_{2}\) at 10, 12.5 and 15 MPa was 27, 76 and 98% of the declared content of humulones, respectively. However, higher P, up to 37 MPa, enabled substantial shortening of the process, from 300 to 80 min. The percentage of \(\beta\)-bitter acid (lupulones) in the soft resin was lower, \(\sim\)45% at 10 MPa, 36% at 12.5 and 15 MPa and 40% at 37 MPa. [PERSON] et al. [33] reported similar ratio of \(\alpha\)-/ \(\beta\)-acids (1.2-1.7/1) in ole oleoresis from _Nugget_, _Osorno_ and _Elizalde Lake_ variety hops. Humulone-rich (41%) antimicrobial extract was obtained from _Marynka_ hop pellets by SFE-CO\({}_{2}\) at 30 MPa/50 \({}^{\circ}\)C. [36]. It may be observed that the variations of SFE-CO\({}_{2}\) conditions had lower effect on the concentration of \(\beta\)-acids than \(\alpha\)-acids; the highest content of the former (345.0 mg/g E at 37 MPa) was only larger than the lowest one by 14% (302.9 mg/g E at 12.5 MPa). However, the recovery of \(\beta\)-acids was highly dependent on the process pressure: thus, up to 3-fold more lupulones (90.8 vs 30.5 mg/g HP) were recovered at 37 MPa than at lower pressures, which are most commonly used by the industry. Numerous beneficial bioactivities both in vitro and in vivo were reported for bitter acids-rich preparations [3]. In general, high TEACORAC of Ella hop extracts (Table 1) was consistent with the previous reports showing prevalent links between strong in vitro oxygen radical scavenging capacity and high soft resin, mainly humulones, content in various hop extracts [37; 38; 39].
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 4 The Optimization Algorithm: Ad-BBR
### Adaptive Sending Rate Adjustment Mechanism  
The adaptive transmit rate regulator employs a novel minimum RTT determination logic to track real-time RTT fluctuations and evaluates the link state based on this new minimum RTT value, thereby achieving more accurate adaptive transmit rate regulation.
## 5 Conclusions  
Aiming at current distributed scenarios with variable security environments, this paper proposes an RDH-ED scheme based on VTSIS. First, this paper identifies and demonstrates security problems with existing TCSIS and proposes VTSIS by improving the TCSIS. Then, the redundancy of VTSIS is used for image encryption and data hiding, which achieves high-capacity embedding and high security. Finally, the effectiveness of the proposed scheme is proven through many theoretical analyses and experiments. The results show that the proposed scheme has a higher ER than current state-of-the-art schemes at commonly used thresholds, and it has high security and complete reversibility without any preprocessing. Most importantly, this study expands the application of RDH-ED. However, there are several shortcomings in our scheme. In the secret sharing scheme, when one pixel of the original image is changed, only a few pixels of the ciphertext pixels are also changed. Therefore, it does not have diffusion properties, which results in our scheme being unable to effectively resist differential attacks. In future research, it can be combined with cryptographic grouping linking, cryptographic feedback mechanism, etc., to improve security.  
The authors confirm contribution to the paper as follows: conceptualization, C.J., M.Z. and F.D.; methodology, C.J. and M.Z.; validation, C.J. and X.Z.; formal analysis, C.J., M.Z. and F.D.; investigation, C.J. and X.Z.; data curation, C.J.; writing--original draft preparation, C.J.; writing--review and editing, C.J., M.Z. and F.D.; visualization, M.Z.; supervision, M.Z.; project administration, M.Z. and F.D.; funding acquisition, M.Z. and F.D. 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 numbers 62272478, 62102450, 61872384, 62102451, 62202496 and Social Science Funds of China (No. 2022-SKJJ-C-091).  
Not applicable.  
The data that support the findings of this study are available from the first author, [[PERSON]], upon reasonable request. The URL of the core code is as follows: [[https://github.com/Hackfreedom888/RDH-ED-based-on-RDH-ED](https://github.com/Hackfreedom888/RDH-ED-based-on-RDH-ED)]([https://github.com/Hackfreedom888/RDH-ED-based-on-RDH-ED](https://github.com/Hackfreedom888/RDH-ED-based-on-RDH-ED)) (accessed on 3 June 2024).  
The authors would like to thank the editor and anonymous reviewers for their valuable comments, which helped to improve this paper.  
The authors declare no conflicts of interest.
## 4 Experiments and Results
### ICA as Fragmentation-Wave Enhancement Tool  
In this new set of experiments, we discuss about whether the use of _multivariate transform ICA can enhance the presence of fragmentation wave in common ECG_. For this purpose, we applied the same processes described earlier on PCA. Namely, the signal is firstly filtered and detrended, then the beats are detected, then beat templates are created by averaging those well-correlated beats, and finally ICA is applied over these beat templates. One relevant difference between the present analysis and the previous one, is the fact that the output of ICA is not expected to be sorted. Hence, to better evaluate and work with this technique, we used the kurtosis of each of the ICA output as a sorting criterion, setting from higher to lower. Just as a reminder for the reader, we would like to mention that kurtosis is a very well-known indicator of Gaussianity [28]. As well as in the PCA part, we computed \(\rho_{RMSE}\) to compare the RMSE of the ECG with the RMSE of the independent component. In order to ensure proper order of ICA outputs, we designed an additional starting experiment. In this experiment we worked with 400 control cases and their respective fragmented replications, ICA was applied over the pairs control-fragmented beat templates, and outputs were sorted according to their decreasing kurtosis. Then, we computed the PCC between the independent components of the fragmented and control cases. Finally, we created the confusion matrix shown in Table 5.  
As it can be seen in the aforementioned table, the values in the diagonal are lower than in the PCA analysis, and in these cases the value of \(\epsilon_{disp}\) was 0.70, due to the fragmentation wave inducing larger changes in the independent components than in the previous case. After this analysis, we performed a careful visual inspection to detect which components were more affected by the fragmentation wave. We noticed that the use of the kurtosis in fragmented waves tends to group the independent components in two main groups, namely, the non-affected components, which appear in the first positions, and the affected components, which appear in the last positions. Considering the previous results, we designed as mentioned a replicable, structured, and coherent, statistical property, for a later computation of \(\rho_{RMSE}\) value and the ratio of enhancement described earlier. To do so, we first computed ICA over the pair control-fragmented waves and output is sorted according to kurtosis. Then, correlation was computed among the sorted fragmented and control components. Finally, sign inversion was applied when the absolute value of the highest PCC is negative, to ensure proper transformation taking into account that positive and negative solutions are both valid and that the standard process does not guaranty the same outcome when signals slightly differ. The distributions of \(\rho_{RMSE}\) for each independent component are shown in Figure 10a. As it can be seen, fragmentation wave
## 2 Notation and Background
### Notation  
Let \(X=[X_{1},\cdots,X_{C}]\in\mathbf{R}^{m\times N}\) be a set of \(N\) training samples in an \(m\) dimension with class labels \(Y_{i}\in[1,\cdots,C]\); here, \(C\) is the class number of the training samples and \(X_{i}\) is a matrix consisting of \(N_{i}\) training samples of the \(i\)th class. Suppose \(D_{0}\in\mathbf{R}^{m\times k_{0}}\) and \(D_{p}=[D_{1},\cdots,D_{C}]\in\mathbf{R}^{m\times k_{p}}\) make up the learned hybrid dictionary \(D=[D_{0},D_{p}]\) from the training samples \(X\), where \(k_{0}\) is the atom number of shared dictionary, \(K_{p}=\sum_{i=1}^{C}k_{i}\) denotes the atom number of class-specific dictionary and \(k_{i}\) is the atom number of \(i\)th class sub-dictionary. Let \(Z=[Z_{1},\cdots,Z_{C}]\in\mathbf{R}^{(k_{0}+K_{p})\times N}\) be the coding coefficients of training samples \(X\) over the hybrid dictionary \(D\); then, \(Z_{0}=[Z_{0}^{1},\cdots,Z_{0}^{C}]\in\mathbf{R}^{k_{0}\times N}\) and \(Z_{i}=[Z_{i}^{1},\cdots,Z_{i}^{C}]\in\mathbf{R}^{k_{i}\times N}\) represent the coding coefficients over the shared dictionary and the \(i\)th class sub-dictionary, respectively.  
According to [24], a row vector of coefficient matrix \(Z\) can be defined as a profile of the corresponding dictionary atom. Therefore, we can define a vector \(z^{r}=[z_{1}^{r},z_{2}^{r},\cdots;z_{C}^{r}]\in\mathbf{R}^{N\times 1}\) (\(r=1,\cdots,K\)) as the profile of atom \(d_{r}\) for all the training samples, where the sub-vector \(z_{c}^{r}=[z_{1}^{r},z_{2}^{r},\cdots,z_{N_{c}}^{r}]^{\mathsf{T}}\in\mathbf{R}^{N _{c}\times 1}\) is the sub-profile for the training samples of the \(c\)th class.
## 6 Results and Discussions
### Results of Introducing Confidence Levels  
The system operational cost increases sharply with the increase of the confidence level. Table 3 shows the operational costs of the proposed microgrid in different confidence levels, and Figure 3 is a three-dimensional graph revealing the relationship between operational costs and confidence levels.
## 2 Materials and Methods
### Research Methodology  
In this research, the oversampling methods have been applied due to the fact that the original dataset has a large imbalance between healthy patients and patients with Hepatitis C. The methods that were used to oversample the minority class/classes are:  
* Random Oversampling,
* Synthetic Minority Oversampling Technique (SMOTE),
* Adaptive Synthetic (ADASYN), and
* Borderline Synthetic Minority Oversampling Techniques (BorderlineSMOTE).  
Each dataset variation is then splitted on train and test dataset in 70/30% ratio where train dataset is used in GPSC with random hyperparameters search and 5 fold cross validation process. The entire schematic view of research methodology process is shown in Figure 1.  
As seen from Figure 1 there are two types of investigations conducted in this paper i.e., binary case and multiclass case. In the binary case, the hepatitis C, fibrosis, and cirrhosis patients are labeled with class number 1 and healthy with class number 0. Due to the large imbalance between class samples (a large number of class 0 samples vs. a small number of class 1 samples), the balancing methods (random oversampling, ADASYN, SMOTE, Borderline SMOTE) were applied which resulted in 4 balanced datasets. These datasets were used in GPSC with random hyperparameter search and 5-fold cross-validation to obtain symbolic expressions and the best of them in terms of the highest mean and standard deviation values of accuracy (\(ACC\)), the area under the receiver operating characteristic curve (\(AUC\)), precision, recall, and F1-score are shown.  
In the multiclass classification problem shown in Figure 1, the class was labeled as 0-Healthy, 1--Hepatitis C, 2--Fibrosis, and 3--Cirrhosis. The aforementioned balancing methods were used to increase the number of samples for classes 1, 2, and 3. The 4 balanced datasets were used in GPSC with random hyperparameters search method and a 5-fold cross-validation process to obtain symbolic expressions. However, since this is a multiclass case the OneVsRestClassifer was utilized alongside GPSC. So the final output of the GPSC with OneVsRestClassifier will be the system of 4 symbolic expressions where the first symbolic expression is used to detect healthy patients, the second for Hepatitis C patients, the third for patients with fibrosis, and the fourth equation for detection of cirrhosis patients. To evaluate these symbolic expressions the mean and standard deviation values of \(ACC\), \(AUC_{macro}\), \(Precision_{macro}\), and \(F1-score_{macro}\) were used. The macro option was used since it calculates metrics for each label and finds their unweighted mean which is ideal for balanced datasets.  
After the best symbolic expressions were obtained in each case the final evaluation was performed on the original imbalanced dataset to see if these symbolic expressions can detect Hepatitis C patients on real data.
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 1 Introduction
The relationship between the at-sensor radiance and the DN recorded in the ZY3-02 satellite image is identified. We can obtain several different radiometric calibration coefficients determined with different target regions, such as gray-scale permanent artificial targets or radiometric calibration tarps with nominal reflectance. A detailed discussion on the validation analysis of the comparison results between the different radiometric calibration coefficients is presented in this paper. The aim of our study is to obtain a reliable and high-accuracy radiometric calibration coefficient for the ZY3-02 satellite sensor.
## 3 Experiments and Analysis
### Visual Style Subset Visualization  
Figure 8 visualizes our visual style error validation method across content and style difference dimensions, using uniformly sampled examples. Based on the magnitude of these differences (large or small), we categorize them into four scenarios: small content/small style, small content/large style, large content/small style, and large content/large style. Visual comparison reveals that large content differences generally exhibit localized semantic variations (e.g., vegetation transforming into bare land, changes in building surroundings, uninteresting changes like parking lots). Conversely, large style differences tend to exhibit image-wide inconsistencies in radiation, weather, and phenology. Consequently, subsets exhibiting both large content and large style differences can be effectively identified. This method effectively partitions a CD dataset into two subsets for validating visual style errors, providing a quantifiable representation of this problem.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Encoder Stage**} & \multicolumn{3}{c}{**Decoder Stage**} & \multicolumn{3}{c}{**S2 Looking**} & \multicolumn{3}{c}{**LEVIR-CD+**} \\ \hline
**E1** & **E2** & **E3** & **E4** & **D4** & **D3** & **D2** & **D1** & **F1** & **IoU** & **F1** & **IoU** \\ \hline ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & 66.47 & 49.77 & 87.17 & 77.17 \\ ✗ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & 66.74 & 50.09 & 87.07 & 77.10 \\ ✗ & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ & 67.09 & 50.47 & 86.86 & 76.77 \\ ✗ & ✗ & ✗ & ✓� & ✗ & ✗ & ✗ & 66.51 & 49.82 & 87.17 & 77.25 \\ ✗ & ✗ & ✗ & ✗ & ✓ & ✗ & ✗ & 66.67 & 50.01 & 87.04 & 77.05 \\ ✗ & ✗ & ✗ & ✗ & ✗ & ✓� & ✗ & 66.31 & 49.60 & 87.00 & 76.99 \\ ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓� & 66.36 & 49.65 & 86.97 & 76.95 \\ ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & 66.47 & 49.78 & 86.54 & 76.27 \\ \hline ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & 65.79 & 49.02 & 86.45 & 76.13 \\ ✗ & ✗ & ✗ & ✗ & ✓ ✓ & ✓ ✓ & ✓ ✓ & **66.76** & **50.11** & **87.27** & **77.41** \\ ✓ & ✓ & ✓ ✓ & ✓ ✓ & ✗ & ✗ & ✗ & 66.29 & 49.58 & **87.20** & **77.30** \\ ✓ & ✓ & ✓ ✓ & ✓ ✓ & ✓ ✓ & ✓ ✓ & ✓ ✓ & **67.28** & **50.69** & **87.42** & **77.66** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effectiveness of representation consistency learning across encoder–decoder stages. ✓: LS/GS module activated, ✗: LS/GS module deactivated. Color convention: best, 2 nd-best, and 3 rd-best.  
Figure 8: Visual style error validation via content/style differences.
## 3 Results
### RMSE Estimation  
Table 2 shows the RMSE values between GPS-TEC and GIM, while Table 3 shows the RMSE values between the GPS-TEC and IRI-2016 models for the descending and minimum phases in the American, African and Asian longitudes. The RMSE is a statistical tool used to ascertain the level of accuracy of different models compared with observed measurements. Hence, in the error analysis, GPS-TEC values were set as the reference for comparison with the GIM and IRI-2016 models. In the American longitude, RMSE values between GPS-TEC and GIM (Table 2) and between GPS-TEC and IRI-2016 (Table 3) were generally the highest during the March equinox of the descending phase year of 2016 and the lowest during the June solstice of the minimum phase year of 2019. In the African longitude, the same RMSE behavior was observed at ADIS. The RMSE at CPVG was generally higher during all seasons than the RMSE obtained at ADIS. At CPVG, the RMSE between GPS-TEC and GIM showed the highest value during the March equinox (7.39) of the descending phase year of 2016, while the lowest value was recorded during the December solstice (1.75) of the minimum phase year of 2018. The RMSE between GPS-TEC and IRI-2016 at CPVG showed the highest value during the December solstice (7.34) of the minimum phase year of 2019, while the lowest RMSE values were recorded during the September equinox (2.01) of the descending phase year of 2017. Lastly, in the Asian longitude (PPPC), the highest RMSE between GPS-TEC and GIM was record during the September equinox (13.42) of the descending phase year of 2016, while the lowest RMSE value was recorded during the June solstice (2.01) of the minimum phase year of 2019. The RMSE between GPS-TEC and IRI-2016 was also the highest during the September equinox (6.64) of the descending phase year of 2016, while the lowest RMSE was observed during the December solstice (1.69) of the descending phase year of 2017.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station ID** & **Years** & **March Equinox** & **June Solstice** & **September Equinox** & **December Solstice** \\ \hline \multirow{4}{*}{SALU} & 2016 & 8.50 & 4.70 & 6.64 & 5.70 \\  & 2017 & 5.42 & 2.30 & 4.88 & 4.23 \\  & 2018 & 3.85 & 1.96 & - & 3.36 \\  & 2019 & - & - & - & - \\  & 2016 & 6.55 & 3.50 & 5.46 & 5.44 \\  & 2017 & 3.79 & 1.79 & 4.18 & 4.07 \\  & 2018 & 2.63 & 1.79 & 2.29 & 2.65 \\  & 2019 & 4.03 & 1.58 & 2.33 & 2.82 \\  & 2016 & 7.39 & 3.86 & 3.91 & 3.56 \\  & 2017 & 3.92 & - & 3.16 & 1.78 \\  & 2018 & 3.09 & 2.96 & 2.50 & 1.75 \\  & 2019 & 2.75 & 2.46 & - & 2.90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE between GPS-TEC and GIM.