esa-sceva/satcom-chunk-collection huggingface dataset:
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 7 Simulation Results
### Online Identification  
The performance of the proposed online identification method is assessed by the basic metrics defined in Equation (9). Further, the identification results are displayed for the two proposed protocol tamper attack cases: the tampered capacity of N1 mode 3 GPP access and the tampered sequence number.  
The first aspect is the detection performance, which is evaluated using three key metrics: the precision, recall, and F1 score. Figure 13 shows the detection performance in the hijacking UE capability and sequence number desynchronization attacks. The detection performance of the proposed intrinsic identification method is compared with that of the CNN and LSTM methods. In the hijacking UE capability case, the accuracy, recall, and F1 score of the proposed intrinsic scheme reach 96%, 1, and 98%, respectively. Compared with those of the CNN [38] and LSTM [33] methods, the precision is improved by 84% and 26%, respectively; the F1 score is increased by 46% and 14%, respectively; and the recall remains constant. The trends for the sequence number desynchronization case are similar to those for the hijacking UE capability case, where the proposed intrinsic scheme outperforms both the CNN and LSTM methods.  
Figure 12: Attention map for a UPF DoS attack.  
Figure 13: _Cont._The second aspect is the identification performance, as shown in Figures 14 and 15. The detection matrix is calculated using the bitwise XOR of the attack-free reference matrix (predicted by the identification model) and the test input matrix. Nonzero elements (logical '1') in the resulting matrix indicate anomalous signal fields, which are then mapped back to their original signal domains through matrix inversion. The anomalous sections of the images are clearly highlighted in the two attack cases, hijacking the UE capability and sequence number desynchronization. This proves that the proposed detection scheme provides intrinsic explanations, and the corresponding detection and identification results are presented in the figures. The highlighted sections can be converted into protocol signal sequences. Thus, the anomalous signal sequences can be identified.  
Figure 14: Identification of hijacking UE capability attacks.  
Figure 13: Detection performance of online identification method.
# Height Control System for Wind Turbines Based on Critical Wind Speed Calculation
## 5 Experimental Tuning of PI Controller Parameters
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{T_{i}}\) **(ms)** & **Integration Time (\(T_{i}\))** & **Peak Value LMN** & **Transition Time (s)** \\ \hline
10 & 93 & \(\approx\)30 & Oscillatory \\
50 & 59 & \(\approx\)17 & Oscillatory \\
100 & 45 & \(\approx\)13 & Oscillatory \\
200 & 40 & \(\approx\)25 & Exponential \\
500 & 40 & \(\approx\)65 & Exponential \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Study of the PI regulator. Figure 10: Proportional regulator at \(K_{p}\) = 2.5. Figure 11: PI regulator at \(T_{i}\) = 10 ms.  
Figure 12: PI regulator at \(T_{i}\) = 50 ms.  
Figure 13: PI regulator at \(T_{i}\) = 200 ms. In Figure 11 to Figure 13, the green line (SP_INT) is the setpoint (Set Point, SP_INT), the red line (PV_IN) is the current value of the controlled parameter (Process Variable, PV_IN), the yellow line (LMN_OBJECT) is the value of the control signal generated by the PID regulator to reach the setpoint (SP_INT), and white line (LMN) is the overall control signal of the system. In these figures, it shows the system's response. The experimental results allowed for the determination of the optimal PI controller coefficients for the rotor-lowering system of the Onipko wind turbine in strong wind conditions. Based on the experimental data, the following parameters were chosen as optimal for the PI controller: a proportional coefficient \(K_{p}\) of 2.5 and an integration time \(T_{i}\) of 200 ms. These parameters provide an optimal balance between system stability and response speed, minimizing oscillations and ensuring a fast transition process. The accurate determination of these parameters significantly enhances the reliability and efficiency of the wind turbine rotor-lowering system at high wind speeds, preventing potential equipment damage and extending its lifespan. Thus, the system can operate effectively, minimizing the risk of accidents and ensuring stable electricity generation even in extreme weather conditions.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 3 Construction of Guaranteed Cost Strategy with Perfect Information
### Time-To-Go Calculation
#### 3.2.2 Time-to-Go Calculation Based on the Hybrid Method  
Although the homotopy method is good at global convergence, it is not good at convergence speed. In many cases (when the initial value is appropriate), [PERSON]'s method can also achieve good convergence. Thus, we propose a hybrid method combining the homotopy method and [PERSON]'s method, which takes into account the validity and rapidity of the algorithm. The specific steps are given as follows:  
**Step1.** Take the time-to-go calculated at the last moment in the confrontation as the initial value \(\theta_{0}\) at the current moment.  
**Step2.** Set the maximum iteration number \(N\), and calculate the time-to-go using [PERSON]'s method, i.e., \(\theta_{i+1}=\theta_{i}-F(\theta_{i})/F^{\prime}(\theta_{i}),\ i=0,1,\cdots\). If convergence can be obtained within the maximum iteration number, the operation will end and the time-to-go will be obtained. Otherwise, go to step 3.  
**Step3.** Calculate the time-to-go by using the homotopy method. Use Equation (25) for calculation and Equation (26) for correction. Repeat the iteration until \(p=1\) is satisfied, and the time-to-go is obtained.  
Since we use the time-to-go of the previous step as the initial guess value of the current step, the initial value is appropriate in most cases. The time-to-go of the current step can be found quickly by using [PERSON]'s method, which is second-order convergence. The homotopy method will be used only at the beginning moment or at a few special moments when the time-to-go suddenly changes (as shown in Examples 2 and 3), and the convergence speed is relatively slower.  
It should be noted that the time-to-go calculation using the hybrid method is based on the premise that the range and bearings of the spacecraft are measurable. In the case of bearings-only measurements, the time-to-go cannot be calculated by this method.
## 3 Experiments
### Experimental Design  
In order to verify whether the improved algorithm outperforms the original algorithm in terms of optimization seeking performance, we have conducted a systematic experimental design aiming at evaluating the performance of the improved algorithm in terms of processing speed, optimal solution, and robustness, etc., and the following is a detailed description of the experiments. To present the differences among various experimental groups in key aspects more clearly, Table 2 is specifically created.  
Experiment A: Greedy algorithm. A locally optimal solution is obtained based on the rule that column covers the maximum number of rows.  
Experiment B: EM algorithm experiment. We use the greedy algorithm to obtain a locally optimal solution and then repeat the local search process m times to generate the initial population. This local search process uses the local search strategy mentioned in Section 2.3.3 of this paper. At this point, after generating a new solution, there may be some redundant columns, so a column pruning process is added. The specific process is to traverse all the columns, if the solution obtained after deleting the column can still cover all the rows, the column is considered to be redundant and the column is deleted.  
Experiment C: Tent chaotic map to construct initial population experiment. The method can both fully utilize the traversal advantages of chaos and produce more uniform particles.  
Experiment D: Improved EM algorithm experiment. Based on experiment C, in order to optimize the experimental results and ensure that the particles in the population move towards the optimal solution every time, we introduced the idea of DE algorithm in the process of moving particles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **United States** & **Hebei 1** & **Hebei 2** & **Inner Mongolia** \\ \hline Subset Number & 1786 & 2202 & 511 & 2150 \\ Element Number & 104, 245 & 49,297 & 13,798 & 64,843 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Size of datasets for the study area.  
Figure 4: Examples of Gaofen-1 and Gaofen-2 images. (**a**) Gaofen-1 image. (**b**) Gaofen-2 image.  
Experiment E: Comparative Reference Experiment. We respectively use the ant colony optimization algorithm (ACO), the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO) to conduct the optimal screening of remote sensing data for the United States, Hebei 1, Hebei 2, and Inner Mongolia regions, so as to further evaluate the performance and effect of the algorithms in this study.  
In experiment BCD, we set the iteration number to 50 and the initial population number to 15 to remain unchanged, and the value range for different deletion column proportion parameters is 0.1-0.5 (0.1, 0.2, 0.3, 0.4, and 0.5). The control parameter pop of different threshold \(\alpha\) ranges from 0.7 to 0.95 (0.7, 0.8, 0.85, 0.9, and 0.95).
# QiOi: Performance Isolation for Hyperledger Fabric
## 6 Discussion  
**Providing performance isolation in distributed environments:** We evaluated QiOi in a host server because this paper focuses on providing performance isolation for Fabric components running in a host server. However, we believe that QiOi is also extensible to distributed environments where each Fabric component runs in different host servers. For example, QiOi can be implemented as a plugin for Kubernetes [43], the most popular container orchestration platform. When Kubernetes creates the Fabric components as containers, QiOi can send requests to assign _CPU share_ values to corresponding Fabric components. Since Kubernetes supports cgroups features that can reserve or limit computing resources for containers, QiOi can exploit the functionality of Kubernetes to assign the _CPU share_ values to the Fabric components.  
Figure 12: Impact of QiOi on Fabric running with Spark containers together.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Throughput** & **Latency** & **Peer** & **Orderer** \\  & **(tps)** & **(s)** & **CPU Utilization (\%)** & **CPU Utilization (\%)** \\ \hline _Alone_ & 787 & 1.27 & 22.3 & 5.4 \\ _w_/_QiOi_ & 785 & 0.93 & 22.0 & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of Fabric performance and average CPU utilization.  
**Providing performance isolation for multiple tenants:** When QiQi runs with Fabric in a cloud data center, it should offer multi-tenancy support. Fabric utilizes channels to offer multi-tenancy in a Fabric network. Channel [8] is a private subnet in a Fabric network, which conducts confidential transactions between two or more specific network members. Hence, each tenant is allocated in different channels while the ordering service is shared by all channels in the Fabric network. Peers can belong to multiple channels and have ledgers for each channel. Furthermore, only components in a channel can participate in processing transactions of the corresponding channel and can access the ledger of the channel. We believe that QiQi can support multiple channels by extending the architecture. First, for performance monitoring, QiQi can monitor the actual performance of each channel by analyzing the transaction logs of peers in different channels. In addition, QiQi can control the _CPU shares_ of the peers belong to different channels by managing per-tenant performance policy. The per-tenant performance policy means that QiQi can only control the _CPU shares_ of the tenants that achieve lower performance than the isolation threshold.
## 3 Results and Analysis
### Data Analysis Results  
#### 3.2.1 Logarithmic Interpolations  
We compared all curves plotted for the fixed parameters {W/B ratio = 0.4, T = 25 \({}^{\circ}\)C} and the different SR-BJPA proportions (= [0%, 5%, 10%, 20%, 30%]) (Figure 1).  
With an increase in the number of days, the CS values measured for these five samples can all be almost perfectly fitted by the logarithmic function. This relationship is true while varying the SR-BJPA and replotting each curve for other T and W/B ratio settings. To examine the logarithmic relationship, Figure 2 displays a plot for another extreme: T = 75 \({}^{\circ}\)C and a W/B ratio = 0.6.  
Because the logarithmic relationship between #days and CS can be asserted in either case, here, we only discuss two important insights. The first one is that the data points deviate more from the logarithmic curve in the latter case when the curing temperature and W/B ratio are both high. Nevertheless, the question about which factor led to this phenomenon requires further investigation. The second finding is that at the 28 th day of curing (#days = 28), with either setting of T and W/B ratio and regardless of SR-BJPA, the CS of a sample (i.e., the data point) will usually deviate from the logarithmic curve used to interpolate it.  
Then, in Figure 3, we partition all of the 45 samples equally into three groups:  
* A group with W/B ratio 0.4;
* The other group with W/B ratio 0.5;
* Another group with WB ratio 0.6.  
Figure 1: CS development with BJPA at different ratios (part I).  
Figure 3: The CS development trends in all samples with W/B ratio 0.4.  
Figure 2: CS development with BJPA at different ratios (part II).  
Each group has 15 samples, with three options for T (curing temperature) and five options for SR-BJPA (the substitution ratio of BJPA). Since the CS values of each sample is recorded at the six different ages (#days), each group contains 90 entries of data. The results are visualised in Figures 4-6, respectively.  
As shown in these figures, the decaying trends in CS are shown along with the increase SR-BJPA, and these trends can be compared in overall across the different curing temperatures of T and/or across the different curing ages of #days. These visualised results not only support the direct observations made above but also provide a 'fast lookup' for later discussions.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Shear Stress on Membrane Surface by Non-Bubbling Techniques  
Submerged HFs also are amenable to fouling control by methods that do not involve bubbling, as discussed below.
# Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
## 5 Discussion
### Data Analysis for Dataset 3
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **MCC** & **KAPPA** & **F1 Score** & **AUC** & **FDR** & **FNR** & **FPR** & **NVP** \\ \hline Decision Trees & 0.9234 & 0.9200 & 0.9200 & 0.9200 & 0.7600 & 0.7600 & 0.9200 & 0.8800 & 0.0800 & 0.0800 & 0.0800 & 0.9200 \\ K-Nearest & 0.9051 & 0.9100 & 0.9100 & 0.9100 & 0.7000 & 0.7000 & 0.9100 & 0.8400 & 0.0900 & 0.0900 & 0.9100 \\ Logistic & 0.8896 & 0.8900 & 0.8900 & 0.8900 & 0.6300 & 0.6000 & 0.8900 & 0.7600 & 0.1100 & 0.1100 & 0.8900 \\ Gaussian Naive & & & & & & & & & & & & \\ Bayes & 0.8070 & 0.8100 & 0.8100 & 0.8100 & 0.6000 & 0.5500 & 0.8100 & 0.8600 & 0.1900 & 0.1900 & 0.8100 \\ Neural Network & 0.9186 & 0.9200 & 0.9200 & 0.9200 & 0.7600 & 0.7500 & 0.9200 & 0.8800 & 0.0800 & 0.0800 & 0.9200 \\ Quadratic & & & & & & & & & & & & \\ Discriminant & 0.8528 & 0.8500 & 0.8500 & 0.8500 & 0.6700 & 0.6400 & 0.8500 & 0.8900 & 0.1500 & 0.1500 & 0.8500 \\ Analysis & & & & & & & & & & & & \\ SVM & 0.9243 & 0.9200 & 0.9200 & 0.9200 & 0.7600 & 0.7600 & 0.9200 & 0.8700 & 0.0800 & 0.0800 & 0.9200 \\ AdaBoost & 0.8238 & 0.8200 & 0.8200 & 0.8200 & 0.3500 & 0.3200 & 0.8200 & 0.6300 & 0.1800 & 0.1800 & 0.1800 & 0.8200 \\ Gradient & 0.8266 & 0.8300 & 0.8300 & 0.8300 & 0.3500 & 0.2300 & 0.8300 & 0.5800 & 0.1700 & 0.1700 & 0.1700 & 0.8300 \\ Boosting & & & & & & & & & & & & \\ Random Forest & 0.9258 & 0.9300 & 0.9300 & 0.9300 & 0.7700 & 0.7700 & 0.9300 & 0.8800 & 0.0700 & 0.0700 & 0.9300 \\ Max Voting & 0.9254 & 0.9300 & 0.9300 & 0.9300 & 0.7700 & 0.7700 & 0.9300 & 0.8700 & 0.0700 & 0.0700 & 0.9300 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Result of Dataset 3 using the Machine Learning Algorithms and Ensemble learning methods. Figure 6 gives a clear graphical representation of the Evaluation metrics and the algorithms and the ensemble learning using Dataset 3. The Bar graph represents each of the ensemble learning models and the results they produced across each of the Evaluation metrics used. Figure 5: Graphical representations of Evaluation Metrics against Machine Learning Algorithms and ensemble learning for Dataset 3.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 4 Experimental Evaluation
### Evaluation Metrics  
To compare our methods objectively with existing methods, we evaluate our models with four popular metrics such as METEOR (Metric for Evaluation of Translation with Explicit Ordering) [32], BLEU (Bilingual Evaluation Understudy) [33], ROUGE-L (Recall Oriented Understudy of Gisting Evaluation) [34] and CIDEr (Consensus-based Image Description Evaluation) [35]. The results can be computed using the Microsoft COCO server [36]. The higher score of these metrics, the higher quality of the generated captions.
## 2 Materials and Methods
### Image Acquisition and Analysis  
MRI examinations were performed via 1.5 T Magnetom aera (Siemens Healthcare, Erlangen, Germany) with a devoted head and neck coil. The MRI acquisition protocol included pre- and post-contrast scans. An axial fat-saturated, echo-planar, imaging-based DWI with two different b-values (b 50-800 s/mm\({}^{2}\)) was acquired. The apparent diffusion coefficient (ADC) values of the parotid and sub-mandibular glands before and after RT were calculated by positioning three regions of interest (ROI) within the salivary gland parenchyma in three contiguous axial sections (Figure 2). Time/intensity curve (I/t), area under the curve (AUC), and K(trans) values of the parotid and submandibular glands before and after RT were generated by using IntelliSpace software version 9.0 (Philips, Amsterdam,The Netherlands) from the native DCE-PWI images by drawing a ROI including the largest gland section possible (Figure 2). Before the sampling, a ROI was automatically placed on the internal carotid artery to obtain an arterial input function curve, defined as the contrast concentration in the vessels feeding the tissue at each point in time during the contrast passage. The vessels, lymph nodes, and cystic areas within the salivary gland parenchyma were excluded on both DWI and DCE-PWI analyses. The ADC, I/t, AUC, and K(trans) values of the trapezius muscle were also obtained as control parameters.  
Figure 1: Patient inclusion/exclusion flowchart. RT = radiotherapy, MRI = magnetic resonance imaging, DWI = diffusion-weighted imaging, DCE-PWI = dynamic contrast-enhanced, perfusion-weighted imaging.  
The following morphologic, DWI, and DCE-PWI features were assessed:  
1. T2 signal intensity (SI) hyper-, iso-, or hypointense with respect to the muscle signal of the parotid and submandibular glands before and after RT;
2. SI hyper- or hypointense of the parotid and submandibular glands before and after RT on DWIb800 images;
3. Mean ADC values of the parotid and submandibular glands before and after RT (ADC pre-post) on DWI sequences;
4. Mean AUC and K(trans) values of the parotid and submandibular glands before RT (AUCpre, K(trans)pre) and after RT (AUCpost, K(trans)post) on DCE-PWI sequences;
5. Ratio between AUC values of parotid and submandibular glands before and after RT (AUCpost/pre);
6. Ratio between K(trans) values of the parotid and submandibular glands before and after RT (K(trans)post/pre).  
The MRI acquisition parameters are shown in Table A1 in Appendix A.
## 4 Discussion
### Spatial Prediction of STN Content  
The STN content maps predicted by the three methods had similar distribution patterns and exhibited strong spatial variability. Agro-ecosystems along rivers that are largely affected by humans had higher STN content. Agriculture in this study area relies on irrigation, which is mainly from groundwater or rivers (Heihe River). [PERSON] et al. (2018) conducted SOC prediction in the HRB and found that the mid-stream farmland ecosystem had a relatively high SOC content. In addition, the agro-ecosystem in the southeast had a slightly higher STN than the northwest. This result can be explained by the relatively high rainfall and lower-than-average temperatures in the southeastern part of the study area. In contrast, other areas of the study had lower STN levels, especially in desert areas without vegetation cover. Previous studies have confirmed the spatial relationship between STN and vegetation ([PERSON] et al., 2019; [PERSON] et al., 2019). For example, [PERSON] et al. (2019) used the RF method to predict STN content in northeastern China, and found that areas with dense vegetation cover had higher STN. Similar findings were also reported in the STN mapping studies by [PERSON] et al. (2019) and [PERSON] et al. (2019).
## 3 Results and Discussion
### Sensitivity Analysis
#### 3.2.3 Influence of the Breach Lateral Evolution Module  
This study primarily conducts a sensitivity analysis of the lateral evolution module by comparing and assessing the impact of the double linear expansion module and the enlargement module on the peaking discharge Q, breach bottom elevation Zd, and breach breadth B. As shown in Figure 16a,b, the lateral evolution module has minimal impact on the bottom elevation Z and the final width B of the breach, but significantly affects the rate at which the width B of the breach expands. The lateral evolution model accelerates the rate at which the breach breadth develops. Figure 16c demonstrates that it significantly affects the discharge mechanism of the breach, resulting in a higher peak discharge and a delayed arrival time.  
Figure 16: Comparison of sensitivity analysis results of lateral evolution module of Tangjiashan barrier dam: (**a**) breach bottom elevation; (**b**) breach width; and (**c**) outflow discharge.  
Figure 14: Comparison of the simulated results with different erodibility parameters for Tangjiashan barrier dam: (**a**) breach bottom elevation; (**b**) breach width; and (**c**) outflow discharge.  
Figure 15: Comparison of sensitivity analysis results of seepage module of Tangjiashan barrier dam: (**a**) breach bottom elevation; (**b**) breach width; and (**c**) outflow discharge.