esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Fluoride sorption efficiency of vermiculite modified with HDTMA was investigated in this study. The optimised conditions of 2 g adsorbent dosage, 70 min contact time, solution pH of 4, and room temperature (25 \({}^{\circ}\)C) guaranteed 51.13% fluoride removal from an 8 mg/L fluoride solution with a maximum adsorption capacity of 2.36 mg/g. This suggests that HDTMA-VMT\({}_{0.2\text{M}}\) can be efficient for fluoride sorption. The adsorption isotherm conformed to both Langmuir and Freundlich isotherms but more to the latter, which indicated that multilayer adsorption took place on the adsorbent. The kinetics obeyed the pseudo-second-order reaction model, which predominantly denotes a case of chemisorption as the mechanism of adsorption while intraparticle diffusion was estimated to be involved in the attraction rate of fluoride onto HDTAM-VMT\({}_{0.2\text{M}}\) surface. The adsorption of fluoride onto HDTMA-VMT\({}_{0.2\text{M}}\) was also attributed to the CH\({}_{3}\) and CH\({}_{2}\) groups introduced to the clay mineral by HDTMABr. This study has shown that vermiculite modified with HDTMABr exhibits fluoride sorption properties, however, further studies should focus on increasing the efficiency of HDTMA-VMT by considering more effective pretreatment procedures as well as other cationic surfactants with higher electropositivity. Efforts should also be made to understand the behaviour of fluoride compounds with change in pH.  
The authors would like to appreciate the University of Venda, Research and Publications Committee (RPC) for funding this research.  
[PERSON] was responsible for the analysis and writing of the manuscript. [PERSON] conceptualised, supervised and controlled the quality of the manuscript while [PERSON] supervised and contributed to the writing of the manuscript.  
The authors declare no conflict of interest.  
Figure 14: Intraparticle diffusion plot of fluoride on HDTMA-VMT\({}_{0.2\text{M}}\).
# Photon Counting Statistics of a Microwave Cavity Coupled with Double Quantum Dots
## 1 Introduction
It is found that WTDs contain information about few-photon processes, which cannot easily be extracted from standard correlation measurements, and the large-deviation statistics of the photon current are helpful in understanding earlier results of heat-transport statistics and work distributions. These results can be generalized to a microwave cavity coupled with nanoscopic devices. On the other hand, a system of double quantum dots (DQDs) interacting through microwave resonators has been proposed, and it is thought that this setup can be used to entangle macroscopically separated electron transport, which has applications in nanoscale quantum information processing [31]. This also provides a new way to study light-matter interactions [32] and implement a QD laser [33]. Because semiconductor DQDs' energy level differences and electron-photon coupling strength are electrically tunable, the interaction of photons and electrons in DQDs has been studied extensively in the circuit QED architecture, where the charge dipole of DQDs is coupled to a microwave cavity. In this paper, the photon counting statistics of a microwave cavity coupled with double quantum dots will be investigated. The influences of DQDs on the WTDs and LD statistics of a microwave cavity will be studied. The differences between a microwave cavity coupled with DQDs and coupled with a thermal bath are discussed. The photon emission counting statistical characteristics of the microwave cavity coupled with DQDs can be tunable because of the electrical tenability of the DQDs' energy level differences and electron-photon coupling strength. The methods and results in this paper can also be applied to many similar quantum systems, such as opto-mechanics system and so on [34; 35; 36; 37; 38]. The paper is organized as follows. In Section 2, we present the model and theoretical formalism with a master equation, as well as WTDs and LD statistics. In Section 3, we study the effects of the driven microwave Rabi frequency, detuning between the microwave driving frequency and the DQDs energy-splitting and other parameters regarding the WTDs and LD statistics of photon emission. The conclusions will be given in Section 4.
## 5 Experimental Settings
### Choosing the Base Learners  
For our experimentation, we used paired learners of naive Bayes (NB) and random forest (RF) classifiers (also known as PL-NB and PL-RF). We chose these learners due to the following reasons:  
Naive Bayes has been widely used in the literature [13; 19; 23]. NB stores the distributions for every class and for every attribute given the class. It also stores the frequency counts of each value for symbolic attributes and the sum of the values along with sum of the squared values for numeric attributes. These distributions are estimated from the training data. NB computes the prior and conditional probabilities for each class, assuming that the attributes are conditionally independent. Afterwards, it uses the [PERSON] theorem to predict the most probable class as follows:  
Let the prior probability for each class \(H_{i}\) be \(P(H_{i})\).  
The conditional probability for each class is \(P(I_{j})/P(H_{i})\); here, \(I_{j}\) is the instance \(j\).  
Then, the most probable class H is \(argmax_{H_{i}}P(H_{i})\pi_{j=1}^{n}P(I_{j}/H_{i})\) These steps, involve adding and subtracting counts and values to/from the appropriate distributions, are easy and efficient to implement for NB.  
Figure 1: Concept of the proposed approach based on PL.  
Previous studies [1; 2] showed that RF outperformed other classification techniques. The random forest classifier consists of many decision trees operating as an ensemble. Each tree in the random forest makes a class prediction, and the class with the majority of votes becomes the model's prediction. The probability of making correct predictions increases as the number of uncorrelated trees are increased in the RF model. Random forest is an efficient algorithm for large datasets. It generates an internal unbiased estimate of the generalization error as the building of forests progresses.  
We also evaluated a few available learners including decision tree (DT), K-nearest neighbor (KNN), naive Bayes (NB), random forest (RF), and support vector machine (SVM) used in the defect-prediction literature. While predicting the defects in a software dataset, the accuracy scores reported by these learners are used to select two top-performing classifiers (NB and RF) for our experimental study.
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 2 Introduction of Research Object  
A typical industrial blower fan is discussed in our research. It is manufactured by TLT Cooperation, equipped in a thermal power plant in northern China, used for conveying air into furnace of boiler for combustion. The blades of blower fan are integrated with driving motor by shaft, and the shaft is sustained by bearings. While the motor is running, the blades rotate and air is injected into the furnace of the boiler. The structure for blower fan is shown in Figure 1, and the actual layout and location of measuring points for investigated blower fan are also exhibited.  
The Supervisory Information System (SIS) for the power plant records the parameters for blower fan in a 20-s sampling solution. In each sample, related parameters include: time stamp, current air volume, load of unit, vibration of bearings, temperature of bearings, current of driving motor, operation frequency of driving motor, and power of driving motor. The parameters are shown in Table 1. Particularly, the temperature values, including temperature of bearings and end-side bearings, are measured using platinum resistance temperature detector. Before variables selection for condition monitoring, the 2 out of 3 actions are accomplished, the absolute errors between each of the two temperature values are compared, and the average of temperatures whose absolute error is smallest is regard as the actual value of measurement, which would improve the reliability of the measurement.  
Except the parameters, the actual vibration alarms were also recorded in Supervisory Information System; while the vibration values exceed the thresholds setting ahead, the time stamp is recorded. The 7-day normal condition data are available for modeling. Meanwhile, actual failure alarms are acquired in our investigated blower fan, which is used for incipient failure detecting validation in our papers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Remark** & **Quantity** & **Unit** \\ \hline Load & Current Load of the Unit & 1 & \% \\ Vol & Current Air Volume of Unit & 1 & \% \\ I & Current of Driving Motor & 1 & A \\ P & Power of Driving Motor & 1 & kW \\ \(f\) & Operation Frequency of Driving Motor & 1 & Hz \\ t\({}_{1}\) & Temperature of Bearings & 3 & \({}^{\circ}\)C \\ t\({}_{2}\) & Temperature of End-Side Bearings & 3 & \({}^{\circ}\)C \\ v\({}_{x}\) & Vibration of Horizontal & 1 & mm/s \\ v\({}_{y}\) & Vibration of Vertical & 1 & mm/s \\ P\({}_{1}\) & Inlet Air Pressure & 1 & Pa \\ P\({}_{2}\) & Furnace Pressure & 1 & Pa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter of blower fan.  
Figure 1: Layout of the blower fan.
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 4 Results and Discussion
### Participants  
Participants were recruited on a voluntary basis. They were mainly gathered from academia. Volunteers from the scientific staff were gathered through a mailing list. An announcement was made in a WhatsApp group for Ph.D. students and a call was made through a Teams channel to attract masters' students. Other inclusion criteria included passing the Ishihara test, which all participants did, and having sufficient English proficiency to understand and interpret the instructions, provide informed consent, and complete questionnaires. All participants were offered a chance of winning an online shopping voucher as an incentive. A total of 20 subjects participated in the study, divided over 10 sessions of two subjects each. Participant ages varied between 22 and 47 with a median of 27.5 and an average of 28.4. A total of 30% of participants identified as female and 70% as male. A total of 65% of the participants were of European/White ethnicity, 20% Middle-Eastern, and 15% Asian. A total of 55% of participants had a background in computer science, 35% in electronics, 5% in mathematics, and 5% in ecology. A total of 15% of the participants indicated that they had never used VR before, while 65% had used it once. Totals of 15% and 5% of participants indicated they were quite or very experienced with VR, respectively. A total of 15% of participants assessed their own technological proficiency as low, 30% as medium, and 55% as high.
# Integration of the 3D Environment for UAV Onboard Visual Object Tracking
## 6 Discussion  
Besides the challenges arising from the specific characteristics for single visual object tracking from UAVs, the use of computer vision approaches onboard a UAV additionally faces the problem of finding an adequate compromise between computational complexity and real-time capabilities with extreme resource limitations on the platform. Although, being not in the scope of this paper, we explore in this section alternative design choices for integrating the current pipeline onto a UAV. Owing to the modular design of our pipeline, we can replace individual components with variants that are less cost-intensive.  
Regarding the _Visual Object Tracker_ component, both ATOM [27] and DiMP [28] have real-time capabilities but rely on a Graphics Processing Unit (GPU) for inferring the position of the object. To diminish the amount of space required and to increase the run time, the original feature extractor--i.e., ResNet-18, ResNet-50 [57]--could be replaced with MobileNet [63], which is specifically designed for embedded vision applications and mobile devices. Alternatively, it is possible to replace ATOM and DiMP with another type of tracker. Since both trackers update the appearance model of the object on-the-fly, they require more GPU capacities than other methods not updating the appearance model such as Siamese SOT [21; 23; 24; 25].  
In our work, we focused on using image-based scene reconstruction by leveraging a SfM-based method [40]. To attain real-time performance, a Visual SLAM-based method that can associate IMU formations [49; 50; 51; 52; 53; 54; 55] is preferred for robustly reconstructing the 3D environment on-the-fly. A concern for the sparse reconstruction might be the storage and the processing time needed when the UAV is observing a large area. To reduce the required storage space needed, the point cloud can be reconstructed or partially loaded, depending on the current UAV position in the scene [64].  
Since the multimodal representation of the probability density function is indispensable, for identifying distractors in the search area, a particle filter [56] is utilized in our detection-by-tracking pipeline. Thus, using a particle filter over other state estimators such as the Kalman filter [34] is crucial for the proposed pipeline, despite being computationally more demanding. Although numerous particle filter implementations do not perform well with a high number of particles, this is not necessarily a general limitation of the approach [65]. In an effort to reduce the computational time, authors from [66] elaborate faster methods for the resampling step compared to common resampling approaches.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Extraction
* Linear Discriminant Analysis (LDA): LDA aims to preserve the class discriminatory information as much as possible. It finds the linear combinations of features that best separate two or more classes of objects or events. The key point of LDA is that it maximizes the ratio of the between-class variance to the within-class variance in the dataset, ensuring maximum class separability. * Isometric mapping (ISOMAP): ISOMAP is a technique which aims to preserve the geodesic distances (shortest path distances on a manifold) between all points. It constructs a neighborhood graph and computes the shortest paths between all points, then applies Multidimensional Scaling (MDS) to these paths. The key point of Isomap is that it captures the underlying manifold structure of the data, making it useful for non-linear dimensionality reduction. While these techniques are primarily used for feature extraction, some variants or applications may also utilize them to inform a feature selection process.
## 2 Literature Review
### Imaging Geometries  
#### 2.2.1 Conventional Imaging  
There are two ways to produce an image. The first is the direct, or conventional, approach. Light from the sample is imaged directly by a lens. This is, of course, the principle of vision in nature.
## 4 Experimental Simulations
### Experimental Results and Analysis
#### 4.3.5 Comparison Experiments with Different Pooling Methods  
In this paper, we use a fusion of max pooling and average pooling to solve the problem of insufficient feature extraction ability of the model. To verify the effectiveness of the proposed method, this section sets up a comparison experiment of different pooling methods: under the same experimental conditions, the dataset is first processed by the preprocessing method mentioned in Section 3.1, and then the model is used to aggregate spatial information by three different methods: average pooling, maximum pooling, and fusion pooling, respectively. The model parameters are still used as those given in Table 1. The resulting detection accuracy is shown in Table 11. The bold text indicates the evaluation indicators of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Feature Selection Method**} & \multicolumn{5}{c}{**Evaluation Indicators (\%)**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{NSL-KDD} & Random Oversampler & 92.79 & 95.12 & 92.79 & 93.74 \\  & Random Undersampler & 84.83 & 93.26 & 84.93 & 87.04 \\  & SMOTE & 98.97 & 99.03 & 98.97 & 98.99 \\  & ADASYN & 98.35 & 98.83 & 98.35 & 98.57 \\  & ENN & 98.85 & 98.87 & 98.85 & 98.85 \\  & RENN & 98.02 & 98.06 & 98.02 & 98.03 \\  & ADASYN + RENN & **99.69** & **99.65** & **99.69** & **99.70** \\ \hline \multirow{8}{*}{UNSW\_NB15} & Random Oversampler & 81.90 & 82.27 & 81.90 & 79.33 \\  & Random Undersampler & 73.09 & 79.69 & 73.09 & 72.33 \\  & SMOTE & 83.51 & 81.08 & 83.51 & 82.71 \\  & ADASYN & 83.04 & 80.69 & 83.04 & 84.23 \\  & ENN & 80.12 & 78.98 & 80.12 & 77.49 \\  & RENN & 81.40 & 81.93 & 81.40 & 81.66 \\  & ADASYN + RENN & **86.25** & **86.92** & **86.25** & **86.59** \\ \hline \multirow{8}{*}{CIC-IDS2017} & Random Oversampler & 96.09 & 95.87 & 96.09 & 95.98 \\  & Random Undersampler & 17.24 & 81.48 & 17.24 & 17.46 \\ \cline{1-1}  & SMOTE & 92.13 & 92.13 & 92.13 & 92.03 \\ \cline{1-1}  & ADASYN & 97.38 & 97.41 & 97.38 & 97.36 \\ \cline{1-1}  & ENN & 95.20 & 95.51 & 95.20 & 94.74 \\ \cline{1-1}  & RENN & 97.05 & 97.13 & 97.05 & 97.01 \\ \cline{1-1}  & ADASYN + RENN & **99.65** & **99.63** & **99.65** & **99.64** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of different sampling methods.  
The reason for this is that Averagepooling is used to extract features by averaging the global range of features, while Maxpooling is used to extract features by taking the maximum value of the feature points in the domain. The experimental results show that fusion pooling effectively improves the model's ability to learn features, and the classification results are greatly improved.
### Case Study No. 2: CODESYS Linked with Node-RED and Microsoft Azure  
In the second case study (Figure 17), instead of using a free and open-source PLC editor, we decided to use a comprehensive automation solution called CODESYS, which provides us with a connection to Factory I/O using the modern OPC UA communication standard. OPC UA server will run through CODESYS runtime and Factory I/O will act as OPC UA client. For control purposes, we do not need an intermediary (middleware) in the form of Node-RED. However, we will use Node-RED for capturing data and sending it to the cloud and it will also be used to provide emergency stop and start of production line that can be made by the user through the cloud application. The cloud application will again be implemented using Microsoft Azure and the communication will be secured by MQTT protocol. CODESYS can be used for academic purposes, but the limitation is that the runtime can only run continuously for 1 h in free mode, which would be very limiting in production.  
The specification and behaviour of discrete-event system is the same as in the first case study.  
Figure 16: Flow in Node-RED ensuring the pause and start of conveyor belt.  
Figure 15: Node-RED data sending flow (air temperature).
## 3 Proposed System
### Demand Prediction Agents
#### 3.5.2 Model Selection and Employed Evaluation Metric  
The evaluation metric selected in this work is the Root Mean Square Logarithmic Error (RMSLE) Equation (1). Although there are many common methods for comparing the predicted value with the validation value, such as Root Mean Square Error (RMSE) or Mean Absolute Error (MAE). However, the metric employed in this problem is RMSLE as suggested by the Kaggle competition.  
\[\epsilon=\sqrt{\frac{1}{n}\ \sum_{i=1}^{n}(\ \log(p_{i}+1)-\log(a_{i}+1))^{2}} \tag{1}\]  
where \(\epsilon\) is the RMSLE value (score), \(n\) is the total number of observations in the dataset, \(p_{i}\) is the prediction and \(a_{i}\) is the actual response for \(i\), \(\log(x)\) is the natural logarithm of \(x\).  
The fundamental reason behind this kind of error is that using the logarithmic-based calculation ensures that the errors during peak hours do not dominate the errors made during off-peak hours. [PERSON] (2006) discusses why this error is employed in the competition.  
There are many Kaggle competitions where this metric is employed such us Grupo Bimbo Inventory Demand (2007) and Sberbank Russian Housing (2008). This metric is not included in Scikit-learn, so we had to implement it in order to use it as a scoring function.
## 6 Discussion
### Resource Usage Metrics
#### 6.4.2 Power Consumption  
Battery constraints are a central limitation in UAV operations [57]. Even modest increases in computational load can notably reduce flight times or necessitate payload compromises (e.g., smaller sensors). Although Genetic Algorithm-optimized architectures may converge faster during training, it remains critical to verify that inference does not incur unacceptably high power demands. Monitoring real-time current draw and voltage levels in representative flight controllers could quantify the trade-off between detection robustness and energy efficiency [58].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Saltmarsh** & **Bulrush Marsh** & **Shrubland** & **Grassland** \\ \hline \multirow{2}{*}{Fallow deer} & F(0.35,_A_) = 0.33 & F(1.5 \(\times\) 10\({}^{-6}\),4) \textless{} 0.01 & **F(1.61,_A_) = 30.79** & F(0.99,_A_) = 1.82 \\  & \(p\) \textgreater{} 0.24 & \(p\) \textgreater{} 0.92 & \(p\) \textgreater{} 0.0145 & \(p\) \textgreater{} 0.067 \\ \hline \multirow{2}{*}{Red deer} & F(1.9 \(\times\) 10\({}^{-5}\),_A_) \textless{} 0.01 & **F(1.24,_A_) = 30.18** & F(7.40 \(\times\) 10\({}^{-5}\),_A_) \textless{} 0.01 & F(3.92 \(\times\) 10\({}^{-5}\),_A_) \textless{} 0.01 \\  & \(p\) \textgreater{} 0.71 & \(p\) \textgreater{} **0.0016** & \(p\) \textgreater{} 0.54 & \(p\) \textgreater{} 0.70 \\ \hline \multirow{2}{*}{Space (manag,unit)} & **F(3.94,_A_) = 87.2** & **F(3.83,_A_) = 35.2** & **F(3.97,_A_) = 160.91** & **F(3.92,_A_) = 44.96** \\  & \(p\) \textless{} 2 \textgreater{} 10\({}^{-16}\) & \(p\) \textless{} 2 \textgreater{} 10\({}^{-16}\) & \(p\) \textless{} 2 \textgreater{} 10\({}^{-16}\) & \(p\) \textless{} 2 \textgreater{} 10\({}^{-16}\) \\ \hline \multirow{2}{*}{Time (year)} & **F(4.82,12) = 1.73** & F(2.8 \(\times\) 10\({}^{-6}\),12) \textless{} 0.01 & F(1.5 \(\times\) 10\({}^{-5}\),12) \textless{} 0.01 & F(9.9 \(\times\) 10\({}^{-6}\),12) \textless{} 0.01 \\  & \(p\) \textgreater{} 0.52 & \(p\) \textgreater{} 0.80 & \(p\) \textgreater{} 0.83 \\ \hline Adjusted R2 & 0.91 & 0.90 & 0.94 & 0.90 \\ \hline Deviance explained (\%) & 93.5 & 92.2 & 94.8 & 92.9 \\ \hline \end{tabular}  
* Interaction between rainfall and phenology.  
\end{table} 
Table 3: _Cont._The density of wild ungulates had either neutral or fairly weak effects on vegetation production (Figure 3). Red deer density had non-significant effects on the production of three vegetation types (saltmarsh, scrubland, and grassland) and showed a linear, positive association with butbrush marsh production. Fallow deer density showed non-significant effects on saltmarsh, bulrush marsh, and grassland production, and non-linear effects (asymmetric concave curve peaking at a moderate density, ca. 0.13 adults/ha, and decreasing above it) on scrubland production. (Note that the relationship between shrubland production and fallow deer density is positive for most values included in the dataset, and the saturation and decrease in the curve only take place for four exceptionally high values included in it).