esa-sceva/satcom-chunk-collection huggingface dataset:
# Interval Uncertainty Optimization Method for Electromagnetic Orbital Launcher
## 3 Uncertainty Analysis Based on Interval Theory
### Transformation of Uncertainty Optimization Problems  
The interval sequence relation \(\leq_{cw}\) has a more intuitive engineering meaning and good engineering practicality, so this paper chooses the interval sequence relation \(\leq_{cw}\)[28; 29]. For any design vector \(X\), the possible range of values of the objective function \(f(X,U)\) is  
\[f^{I}(X)=[f^{L}(X),f^{R}(X)]=\langle f^{c}(X),f^{w}(X)\rangle \tag{11}\]  
where  
\[f^{c}(X)=\frac{f^{L}(X)+f^{R}(X)}{2} \tag{12}\]  
\[f^{w}(X)=\frac{f^{R}(X)-f^{L}(X)}{2} \tag{13}\]Based on the interval order relation \(\leq_{cw}\), a judgment can be made: if the objective function has a midpoint \(f^{c}(X^{1})\leq f^{c}(X^{2})\) and a radius \(f^{w}(X^{1})\leq f^{w}(X^{2})\), then the design vector \(X^{1}\) is superior to \(X^{2}\). Then, the objective function in Equation (5) can be converted into  
\[\min_{x}\left(f^{c}(X),f^{w}(X)\right) \tag{14}\]  
where, for any \(X\), the midpoint and radius of the uncertain objective function can be calculated based on Equations (12) and (13). The objective function boundaries \(f^{L}(X)\) and \(f^{R}(X)\) can be obtained by  
\[\begin{cases}f^{L}(X)=\min_{U\in U^{l}}f(X,U)\\ f^{R}(X)=\max_{U\in U^{l}}f(X,U)\end{cases} \tag{15}\]  
Optimizing \(f^{c}(X)\) is equivalent to improving the \"average design performance\" of the objective function under uncertainty, while optimizing \(f^{w}(X)\) is equivalent to reducing the \"sensitivity\" of the objective function under uncertainty, thus ensuring design reliability.  
Thus, Equation (5) can be transformed into the following deterministic optimization problem:  
\[\begin{split}&\min_{x}(f^{c}(X),f^{w}(X))\\ & s.t.\quad X\in\Omega^{n}\end{split} \tag{16}\]  
where  
\[\begin{cases}f^{c}(X)=\frac{\min_{U\in U^{l}}f(X,U)+\max_{U\in U^{l}}f(X,U)}{ 2}\\ f^{w}(X)=\frac{\max_{U\in U^{l}}f(X,U)-\min_{U\in U^{l}}f(X,U)}{2}\end{cases} \tag{17}\]  
For subsequent optimization solutions, the linear weighting method is used to further transform  
\[\begin{split}&\min_{x}f_{d}(X)=(1-\beta)(f^{c}(X)+\xi)/\phi+ \beta(f^{c}(X)+\xi)/\psi\\ & s.t.\quad X\in\Omega^{n}\end{split} \tag{18}\]  
where \(f_{d}\) is the multi-objective evaluation function, \(0\leq\beta\leq 1\) is the multi-objective weight factor, \(\xi\) is the parameter that ensures that \(f^{c}(X)+\xi\) and \(f^{w}(X)+\xi\) are non-negative, and \(\phi\) and \(\psi\) are the regularization factors of the multi-objective function.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 5 Research Methodology
### Simulation Steps  
Here are the steps we used to simulate a new anomaly detection approach based on group learning to detect attacks in 6G communications using Python with multiple datasets:  
Figure 2: The anomaly detecting system’s construction.  
* We define the scope and goals of our simulation. Before we start running the simulation, we work on defining the scope of the simulation and the goals we want to achieve. Which included defining the types of attacks we want to detect, the performance metrics we want to improve, and the datasets we want to use.
* Collect and process the datasets: For our simulation experiment, we will need to collect and preprocess three datasets: NB2015, CIC_IDS2017, CICDDOS2019, and NSL KDD, which involves cleaning and pre-processing the data to remove any noise or outliers and converting the data into a format suitable for machine learning and deep learning. Which also needs to divide the data into training, validation, and test sets.
* Implement a group learning approach: the next step is to implement a group learning approach to detect anomalies in the connectivity networks, which involves selecting suitable basic models, such as decision trees, neural networks, or support vector machines, and combining them using an appropriate clustering method, such as packing, boosting, or stacking. Python libraries such as scikit-Learn or TensorFlow were used to implement and train these models.
* Train and validate the model: Once the group learning approach has been implemented, the model can be trained and validated using the three datasets, which may include splitting data sets into training, validation, and test sets and using cross-validation to evaluate model performance. Measures such as accuracy, recall, and F1 score were used to evaluate the model's performance.
* Improving the model: After validating the model, its performance was improved by adjusting the hyperparameters, choosing different basic models, using different clustering methods, and integrating the equations.
* Visualizing and interpreting the results: After refining the model, the results were visualized and interpreted to gain insights into the attack detection problem and the model's performance. This involved creating visualizations of the data and the model and interpreting the results in the context of the specific attack detection problem.
## 4 Simulation and Analysis
### Simulations  
In order to verify the performance of the proposed algorithm, Monte Carlo simulation is applied to evaluate the proposed algorithm effectively and comprehensively. To demonstrate the reliability and effectiveness of the novel carrier loop algorithm, comparative tests are performed. The feasibility of iterative estimation of the LM algorithm, the validity of the KF smoothing error and the superiority of the combination the MLE with the KF algorithm are verified, respectively.
## 8 Challenges of AI in Cognitive Sensing and the Way Forward  
### Challenges of AI for Cognitive Sensing Solutions  
The integration of AI techniques with various cognitive sensing solutions can revolutionize the data collection process in FloT. However, for this to happen, some challenges must be overcome for the full realization of AI potentials in CS. Some of these challenges include:  
Figure 11: Common CS tasks and the choice of AI Techniques used in the literature.  
Figure 10: Classification of AI techniques over for CS Solutions.  
1. _Modeling Difficulties_: IoT systems have the requirements of usability in real-time, low latency and high availability. Hence, in such systems, developing parameterized models for application or research purposes is difficult.
2. _Lack of Needed/Quality Data_: Poor quality of data or its lack thereof reduces the efficiency of predictive models or knowledge base used by cognitive systems. Such models are used for tasks like forecasting, analysis, decision making, etc. Thus, it becomes a problem when such tasks are carried out using faulty models.
3. _Irregular data usage pattern_: Cognitive systems use various planning and optimization models built based on their usage patterns to optimize resource consumption. Hence, developing such models become difficult when nodes receive irregular requests or commands from users/applications
4. _Resource Limitation_: The energy and computational requirement of some AI algorithms can sometimes be high for the resource-deprived nodes. Algorithms such as SVM, KNN, ANN are known to be computationally complex and using them on a resource-starved node becomes a difficult task.
5. _Improved Algorithms_: Currently, some algorithms are sensitive to data outliers while others lack accuracy and are prone to overfitting. Thus, using these algorithms without addressing the aforementioned issues might yield undesired results. Recent trends adopt the use of ensemble models to mitigate some of these issues.
6. _Data Privacy_: In IoT systems, data privacy emphasizes the proper handling of data in terms of consent, notice, and regulatory obligations. As a result, some of the data needed by AI algorithms might become unavailable due to privacy violation concerns or their outright sensitive nature. Apart from this, some of the data privacy-related technologies such as protection and encryption further complicate the data availability challenge for AI algorithms. Hence, a serious effort is needed from the research community on how to make these data available without compromising privacy and its associated concerns.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 5 Rockburst Prediction Methods  
Predicting rockbursts is crucial for preventing and controlling rock failure in underground openings or tunnels [139]. There are two types of rockburst prediction: long-term, used during the design stage, and short-term, used during the life of the project [140]. Short-term prediction mainly uses field monitoring methods, such as micro-seismic, electromagnetic radiation, drilling cutting, micro-gravity, and infrared thermal imaging. High-frequency microseismic events have been successfully captured at mines using a high-frequency microseismic device [141]. [PERSON] and colleagues observed that microcracking before rockbursts can be detected using microseismic monitoring [142]. [PERSON] was the first to propose utilizing the volume of drilled coal debris to predict rockburst risk in coal mines [143]. The volume of drilled coal debris has been proposed to predict rockburst risk in coal mines.  
Long-term rockburst prediction assesses the likelihood of rockbursts and relevant field conditions using various markers. These markers include the strain energy storage index (WET) proposed by [PERSON] [123] and the elastic strain energy density used by [PERSON] et al. [144]. Using this prediction method at the planning stage of future excavations helps to avoid rockburst hazards. However, the occurrence of rockbursts depends on various factors, such as ground stress, rock mass integrity, and strength, which have interrelated effects, leading to limitations in current engineering prediction systems. Figure 10 summarizes the comprehensive integrated method, after trials in Western China, using detailed in-situ investigations, rock mechanics experiments, and secondary stress calculation to predict rockbursts through qualitative and quantitative methods and single- and multi-factor predictions. This method will be discussed in detail in the following sections, along with the use and significance of stress and energy approaches in predicting rockbursts in underground excavations.
# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 2 Methods
### Channel Attention
#### 2.1.3 Channel Attention Based on a Scaled Dot-Product Module  
The scaled dot-product in the self-attention mechanism [26] is usually utilized in RNNs for NLP. To the best of our knowledge, no research has been undertaken using this mechanism in CNNs for SVD. Here, we amended our channel attention mechanism and integrated it into a CNN to learn the importance of the features. Figure 4 shows the module framework. F represents the feature maps with the c channels of images input to the module, whose height is h and width is w. F are the module outputs with the same size of channels. Four steps are involved in learning the channel attention.  
Figure 4: Channel attention based on a scaled dot-product module.  
First, we linearly project the input feature maps with dimensions reduced to h, and then feed them to the ReLU functions to obtain keys and queries:  
\[K=\sigma(FW^{K})\ \ \ \ \ \ Q=\sigma(FW^{Q}) \tag{5}\]  
where the projections are parameter vectors \(W^{K},W^{Q}\in R^{w}\), and \(K,Q\in R^{h\times c}\). This step increases the nonlinear characteristic of ReLU functions, and the following computation complexity is reduced by reducing the dimensions.  
Then, the attentions or the weights are computed by the scaled dot-product, followed by a _softmax_ function:  
\[A=softmax\big{(}\frac{Q^{T}K}{\sqrt{h}}\big{)} \tag{6}\]  
where \(A\in R^{c\times c}\) represents the correlation matrix of the channels, and \(h\) is the height of image.  
Next, an attention transform is conducted to obtain the attention vector:  
\[\alpha=\text{mean}(A(1-E),\text{dim}=1) \tag{7}\]  
where \(\alpha\in R^{c}\) and \(E\) is an identity matrix. \(A(1-E)\) aims at eliminating the relation between the channel and itself. The mean operation transforms the matrix into an attention vector.  
Finally, the output feature maps are achieved by imposing attention on the value \(V\):  
\[F^{\prime}=\alpha V \tag{8}\]  
where \(V\) is equal to \(F\). The output feature maps \(F^{\prime}\) can be treated as the features embedding the importance, and they can then be fed into the next layer.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 5 Experimental Results
### Position Analysis of the Aerial Manipulator  
To further validate the effectiveness of the proposed method, we recorded the position data of the operational aerial manipulator system, including the initial, grasping, and delivery states.  
As shown in Figure 16, the aerial manipulator took off and entered the target search state at approximately 2 m. During the iterative search for the target object, the positional information along the x, y, and z axes of the aerial manipulator fluctuated. At approximately 22 s, when the target object grasping was completed, considerable fluctuation occurred  
Figure 15: Comparison of the UAV attitude changes with and without manipulators.  
in the positional information along the x, y, and z axes. The height information along the z-axis was 35 cm, while the physical height of the aerial manipulator was 30 cm. Hence, the grasping height was within an acceptable operational range, confirming that the proposed method exhibits good control effectiveness.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Hyper-Parameters Analysis
#### 4.2.2 Output Feature Vector Dimensions  
In the same learning rate, the embedded output feature vector dimensions of the MLP head in the Vision Transformer network greatly affect the image retrieval accuracy. The embedded feature vector represents the encoding of the image. Therefore, appropriate feature vector dimensions can better compress and convey image information. A small output feature vector dimension may cause meaningful image information to become lost. By contrast, feature redundancy may occur with large feature vector dimensions, which may decrease the accuracy of image retrieval. Therefore, a reasonable feature vector dimension needs to be selected through hyper-parameter experiments. As shown in Figure 4, the model performances under different output dimensions are tested. The results illustrate that the best model performance is achieved when the output dimension is 1000.
## 3 Results and Discussion
### Rapid Measurement System
#### 3.2.3 Virtual Measurement System Repeatability and Accuracy Test
Because of the sampling interval effect, the count is 0 in the 0-0.01 mm range of error. The count for thickness errors more than 0.2 mm is 15, which is 47% of the total count. The count for thickness errors more than 0.35 mm is 7, which is 22% of the total count. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Measurement System Repeatability Study**} \\ \hline \multicolumn{1}{c}{**(Unit: mm)**} & \multicolumn{2}{c}{**Tooth Height**} & \multicolumn{2}{c}{**Repeatability Error**} \\ \hline \multicolumn{1}{c}{**No. of Tooth**} & **1 st** & **2 nd** & **3 rd** & **2 nd–1 st** & **3 rd–1 st** \\ \hline T1 & 3.044182 & 3.038063 & 3.065773 & \(-\)0.006 & 0.022 \\ T2 & 3.208313 & 3.196518 & 3.200348 & \(-\)0.012 & \(-\)0.008 \\ T3 & 3.204491 & 3.202126 & 3.20713 & \(-\)0.002 & 0.003 \\ T4 & 3.146439 & 3.148987 & 3.146469 & 0.003 & 0.000 \\ T5 & 3.147575 & 3.146599 & 3.150719 & \(-\)0.001 & 0.003 \\ T6 & 3.109627 & 3.107903 & 3.112473 & \(-\)0.002 & 0.003 \\ T7 & 3.154678 & 3.17173 & 3.153351 & 0.017 & \(-\)0.001 \\ T8 & 3.135544 & 3.136116 & 3.147705 & 0.001 & 0.012 \\ T9 & 3.122688 & 3.111023 & 3.118095 & \(-\)0.012 & \(-\)0.005 \\ T10 & 3.099098 & 3.108871 & 3.097031 & 0.010 & \(-\)0.002 \\ T11 & 3.184891 & 3.193581 & 3.190315 & 0.009 & 0.005 \\ T12 & 3.112991 & 3.117012 & 3.114571 & 0.004 & 0.002 \\ T13 & 3.076508 & 3.099327 & 3.08419 & 0.023 & 0.008 \\ T14 & 3.1362 & 3.134392 & 3.132622 & \(-\)0.002 & \(-\)0.004 \\ T15 & 3.141754 & 3.148598 & 3.155876 & 0.007 & 0.014 \\ T16 & 3.082542 & 3.08683 & 3.084183 & 0.004 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Measurement system repeatability test on tooth height (measurements were repeated thrice). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Measurement System Repeatability Study**} \\ \hline \multicolumn{1}{c}{**(Unit: mm)**} & \multicolumn{2}{c}{**Tooth Thickness**} & \multicolumn{2}{c}{**Repeatability Error**} \\ \hline \multicolumn{1}{c}{**No. of Tooth**} & **Test 1** & **Test 2** & **Test 3** & **2 nd–1 st** & **3 rd–1 st** \\ \hline T1 & 10.41844 & 10.73286 & 10.83447 & 0.314 & 0.416 \\ T2 & 32.7338 & 32.82142 & 32.87395 & 0.088 & 0.140 \\ T3 & 10.84539 & 10.62988 & 10.93286 & \(-  
The results show the impact of the decrease in sampling volume. The simple filtering method is only effective for measurement of the radial direction, and the increase in noise also increases the error in the measurement of tooth thickness.
## 3 Results  
### Isolation and Identification of the Microorganism  
Most of the colonies are round white or milky white, with clean margin, and a moist and smooth surface (Figure 1A). _Lactobacillus_ was preliminarily identified by Gram staining. Under the microscope, the strain shows a long-rod and short-rod shape without spores (Figure 1B). Electrophoresis shows no band in the negative control group. Then, 1500 bp was estimated by DNA marker (molecular weight 2000) (Figure 1C), indicating that the PCR sample was not contaminated, and 16S rDNA sequencing could be performed. The sequencing results demonstrated that the strain had 99% homology with the known fermentative _Lactobacillus_ in GenBank (GenBank No.: NC_004567.2). Consequently, the strain was a new _Lactobacillus_ strain, and was named CQPC05, and reserved in the China General Microbiological Culture Collection Center (CGMCC No. 14494).  
\begin{table}
\begin{tabular}{c c} \hline
**Gene Name** & **Sequence** \\ \hline \multirow{2}{*}{c-Kit} & Forward: 5\({}^{\prime}\)-CATAGCCCAGTAAAGCACAAAT-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-GAACACTCCAGAATCGTCAACTC-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{SCF} & Forward: 5\({}^{\prime}\)-TCAGGACTACGCCTGAAAG-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-AAGAGCTGGCAGCCGACTCA-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{TRPV1} & Forward: 5\({}^{\prime}\)-CCGGCTTTTGGGAAGGGT-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-GAGACAGTGGCATCCAC-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{GDNF} & Forward: 5\({}^{\prime}\)-GGGTAATGAGATTGGTAG-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-CTATGAGAATGCTGCCGAAAA-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{NOS} & Forward: 5\({}^{\prime}\)-CAGCGAAACGGACGGCAAGCA-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-TGACACGACCAGCCGGCAGGAT-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{GAPDH} & Forward: 5\({}^{\prime}\)-TGCCACCACAACTGCTTAG-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-GATGGAATGTTC-3\({}^{\prime}\) \\ \hline \end{tabular} c-Kit, stem cell factor receptor; SCF, stem cell factor; TRPV1, transient receptor potential cation channel subfamily V member 1; GDNF, glial cell-derived neurotrophic factor; NOS, nitric oxide synthase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.  
\end{table}
Table 1: Sequences of reverse transcription-polymerase chain reaction primers were used in this study.  
Figure 1: (**A**) Colony morphology, (**B**) Gram staining result, and (**C**) 16S rDNA agarose gel electrophoresis of PCR amplified product of _Lactobacillus plantarum_ CQPC05. M: 2000 bp DNA Ladder; 0: negative control group; 5: _Lactobacillus plantarum_ CQPC05.
## 4 Results and Evaluation
### Equipment Extraction  
The node sensors #A1 and #A2, along with the soil instruments connector to them, were removed from the field after 2 months of flawless operation. No operational degradation or physical damages were identified on any of these nodes or on the accompanying equipment during this period. Despite that, it must be noted that the process of digging up the instruments itself can be a serious cause of damage if the position of the equipment is not a priori known or there is a rush to unplug the instruments quickly. For this reason, specific attention should be paid during this process, as most of the soil equipment costs multiple times the price of the concentrating node. Figure 16 depicts characteristic instances of this digging-up process, corresponding (from the left to the right) to the main node, a soil instrument, and a battery enclosure unit, respectively.
## 4 Discussion
### Correlations Between In Situ SM, GPP, and Satellite Information  
The correlations between SM, EVI, and LSWI values (Figures 7 and 3) suggest that, although the LSWI has physically been shown to be related to vegetation water content and surface water, both indices showed similar behavior. Field data showed that SM is important for vegetation conditions and productivity. The stronger correlation between GPP and EVI values than LSWI values (Figures 10 and 6) can be explained by the fact that this vegetation index is related to biophysical conditions of vegetation that are more associated with GPP, like photosynthetic pigments and leaf area. In addition, some authors have reported that changes in leaf internal structure, cuticle thickness, and surface characteristics can produce variations in the LSWI unrelated to water content [66; 67]. This can be more thoroughly analyzed in future studies.  
Regarding thermal data, during wet pulses that increase soil water in deep horizons, vertical integration is expected; thus, soil evaporation can contribute to the cooling process. Likewise, the two vegetation layers can respond quickly to subsurface SM through transpiration [35]. The similar correlations at different depths (Figures 9 and 5) suggest the evaporative cooling effect on the soil-plant system due to increases in SM. In these savanna woodlands, there can be a combination of a layer of bare soil and two layers of vegetation, and the considered pixel size includes those three ground covers. Thus, the changes in surface energy balance associated with ET should explain the general pattern in the adjustments, where an asymptotic line for high SM and minimum LST values can be deduced. It should be noted that both sites contain a high-density Mulga woodland (76% cover). Thus, the consistent decrease in LST associated with increases in subsurface SM is mainly related to the effect on vegetation. It was not possible to analyze the differentiated effect of soil evaporation on LST from in situ data, given that SSM was not measured on bare soil in both stations.  
In general, a clearer pattern can be seen in SSM up to 60 cm in depth (Figure 9). This is consistent with [33; 36], who showed that between 65 and 70% of biomass root is found in the upper 30 cm of soil and high concentration of roots in the first 10 cm of the soil. Reference [68] reported that the remaining roots (_Acacia anewra_ has a dimorphic root system) can extract deeper SM, which would explain the influence of SM in deeper horizons. More noticeable thermal inertia can be deduced in case of high values of water content (\(\geq\)0.15 and 0.2 m\({}^{3}\)/m\({}^{3}\) at the surface and 10 cm in depth, respectively; \(\geq\)0.25 m\({}^{3}\)/m\({}^{3}\) at 60-100 cm depth in ASM) and, hence, ET. On the other hand, higher thermal amplitude is observed in dry periods, showing that LST depends on other factors. MOD11 LST showed a slightly higher correlation than afternoon overpass, suggesting that ET is better coupled during the morning, and, during the afternoon, the ET may be decoupled because of a mulching effect over SSM.