esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety Control for Satellite Formation Flying via High-Order Control Barrier Functions
###### Abstract  
In satellite formation flying, maintaining a predefined safety distance between the reference satellite and its accompanying satellites is critical to prevent collisions--a requirement that traditional control methods fail to satisfy. This paper proposes a novel safety control algorithm that enforces the relative position constraint as a formal safety condition. Specifically, we construct a high-order control barrier function (HOCBF) tailored to the dynamics of satellite formations, which defines a safety set that guarantees collision avoidance. By formulating and solving a quadratic programming problem embedded with the HOCBF, we obtain a safe controller that ensures the forward invariance of the safety set. Simulation results demonstrate that our controller effectively maintains the required safety distance and prevents collisions under various disturbance scenarios, outperforming conventional methods in terms of response speed and robustness. These findings confirm the practical advantages of the proposed approach for safe satellite formation flying.  
satellite formation; high-order control barrier functions; safety control Article
# Improving Water Efficiency in a Municipal Indoor Swimming-Pool Complex: A Case Study
## 3 Results
### Water Use Characterization
#### 3.1.2 Showers  
* With timer (N = 25): the current average flow rate was 9.96 L/min. However, it can be reduced to 5.7 L/min with more efficient showerheads. The cost of these showerheads is 15.39 \(\upmu\)C + VAT per unit (according to 2020 prices);
* Mixer (N = 6) and single-lever (N = 2): the current flow rate was 23.13 and 19.60 L/min, respectively. Nevertheless, the flow rates could be reduced to values between 5 and 7.2 L/min if more efficient models were substituted for the current showerheads. The cost of the more efficient models is 22.70 \(\upmu\)C + VAT per unit (according to 2020 prices).  
Considering that each user takes two to five minutes to shower (according to the survey data), the annual number of users, the average flow of each shower model, and the frequency of utilization of each model, the current average water consumption was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment/Activity** & **Consumption (m\({}^{3}\)/year)** & **Percentage (\%)** \\ \hline Flushing cisterns & 413.6 & 2.50 \\ Showers & 5751.5 & 34.72 \\ Taps & 1950.2 & 11.78 \\ Urinals & 4.3 & 0.03 \\ Pools & 4243.2 & 25.62 \\ Filters backwashing & 4197.6 & 25.35 \\ \hline Total & 16,560.4 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average annual water consumption in swimming pools by use category.  
estimated at 5751.5 m\({}^{3}\). Therefore, a reduction to 3704.8 m\({}^{3}\) (35.59%) would be expected with the showerheads' replacement.
## 3 Results and Analysis
### Improvement of the Runaway Oscillation Stability  
In general, at the start-up of pump-turbine, synchronization speed is reached at a small GVO degree, usually less than \(9^{\circ}\). If the stable runaway region of a pump-turbine is expanded, the unit can operate more safely. Now that we know that the runaway oscillation stability is affected by the backflows at the runner inlet, the stability can be improved by changing the development law of the BFVSs at the runner inlet. The authors redesigned the shape of the runner blade leading edge. As shown in Figure 15, the original runner has no lean at the runner blade leading edge, while the improved runner has a negative lean angle at the runner blade leading edge. Figure 16 show the comparisons of the dynamic characteristic between the improved and original cases during the runaway process at \(9^{\circ}\) GVO. As shown in the figure, a stable runaway process of the pump-turbine can be achieved by the improved runner. The changes of the transient macro parameters of two cases are very close until the BFVSs emerge at the runner inlet. The changes in radial velocity show that the BFVSs of the improved case only occur at the midspan of the runner inlet. As a result, the change amplitude of discharge change rate \(dQ/dt\) for the improved case is significant smaller than that for original case, and the dynamic trajectory of the improved case has negative slopes \(dT_{\text{ED}}/dt_{\text{ED}}\) at the no-load points, which satisfies the runaway oscillation stability criterion. Therefore, the runaway process is damped.  
Figure 14: Vortex structures in the flow passages at no-load points OP1 and OP2; (**a**) \(15^{\circ}\) GVO; (**b**) \(6^{\circ}\) GVO.  
Figure 15: The two runners. (**a**) Original runner; (**b**) improved runner.
# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Joint Extraction
#### 2.2.2 Joint Extraction Based on Sequence Annotation  
The method based on parameter sharing still relies on the result of entity recognition to construct the entity pairs, and then relationship classification is used, but there is no semantic relationship between some entity pairs, which introduces redundant information to the relationship classification task. To avoid the problem of information redundancy based on parameter-sharing methods, researchers have proposed a strategy to change the triplet annotation to achieve the joint extraction of entities and relations. [PERSON] et al. proposed a new annotation strategy to transform the entity and relationship joint extraction task into a sequence annotation problem, where the annotated sequence information includes the position information of entity words, type of entity relationship information, and role information of entities, which can identify entities and relationships simultaneously [52]. This method uses an end-to-end neural network model to extract the relational triples between entities, which reduces the effect of invalid entities on the model and improves the recall and accuracy of relationship extraction. The advantage of this model is that it deals with isolated relationships, but the method is not effective in identifying overlapping relationships, and the association between two corresponding entities still needs to be refined. Based on joint decoding, [PERSON] et al. proposed a deep neural network model for sequence-to-sequence-based learning called a hybrid dual-pointer network (HDP), which was designed to extract multiple-pair triples from a given sentence by generating hybrid dual-pointer sequences [53]. The performance of this method for entity overlap (one entity participating in multiple triads) is better than that for relationship overlap (multiple relationships in a pair of entities). The problem of relationship overlap is more complex, and its solution needs further exploration.
# The Feature Selection Effect on Missing Value Imputation of Medical Datasets
## 2 Literature Review
### Feature Selection  
Feature selection can be defined as a process of selecting a subset of relevant features (or variables) from a given dataset. Since real-word datasets usually contain some features that are either redundant or irrelevant, they can be removed without incurring much loss of information [36; 37]. In other words, feature selection can be regarded as a special case of dimensionality reduction, which aims to reduce the number of random variables under consideration by obtaining a set of principal variables. In particular, the difference between feature selection and dimensionality reduction is that the set made by dimensionality reduction does not have to be a subset of the original set of features. For principal component analysis, new synthetic features are made from a linear combination of the original ones, and the less important ones are discarded.  
In general, feature selection algorithms can be classified into three types of methods--filter, wrapper, and embedded methods [36]. One major type of filter method used to select important features is based on ranking techniques. Specifically, the input features are scored via a suitable ranking criterion and features that fall below a certain threshold are removed. Many statistical techniques belong to the filter type of method, including information gain and stepwise regression.  
The wrapper methods are based on using a predictor (or learning model) as the objective function to evaluate different feature subsets. The best feature subset is chosen, which is the one that can make the predictor produce the highest accuracy rate. Evolutionary compaction techniques, such as the genetic algorithm and particle swarm optimization methods, have recently gained much attention and shown some success [38; 39].  
The representative wrapper methods are the genetic algorithm and particle swarm optimization methods. However, the wrapper methods have a large computational cost for model training and in searching for the best subset.  
The embedded methods perform feature selection during the model learning process [39; 40; 41]. In other words, feature selection is incorporated into the classifier training process. Specifically, embedded methods not only measure the relations between the input features and the output features, but also search for features that allow better classification accuracy. One representative embedded method is the decision tree model, where the constructed tree contains a number of selected features (i.e., decision nodes) that can distinguish well between different classes (i.e., leaf nodes). Besides decision trees, there are some other types of embedded feature selection methods, such as \(l_{1}\)-regularization techniques, including LASSO (least absolute shrinkage and selection operator) [33] and \(l_{1}\)-SVM (L1-norm SVM) [42], and memetic algorithms [43].
## 3 Results
### Influence of the Amplitude of the Excitation Pulses  
Apart from the frequency, the power supply allows for an adjustment of the output amplitude of the current pulses. Thus, an attempt is made to evaluate the distribution of the plasma parameters for different pulse amplitudes as well. More precisely, two power settings, 85% and 100%, are compared, which correlate to current amplitudes of \(\sim\)0.85 A and \(\sim\)1.0 A, respectively. The trigger timeframe is set to \(I_{peak}\) and the distance from the nozzle to 1 mm. As before, the results compiled in Figure 5 represent averages of at least three measurements calculated according to the description at the beginning of this section.  
By reducing the pulse amplitude from 100% to 85% the mean electron number density decreases regardless the operating frequency, although a much more significant reduction of over 20% is observed when the discharge current is pulsed with 60 kHz. The reduction of \(\overline{n}_{e}\) for the lower operating frequency is in the range of about 10%. Interestingly, contrary to \(\overline{n}_{e}\), no change in mean electron temperature is observed when the amplitude is changed at 60 kHz, while it decreases by about 20% when operating at 43 kHz. In addition, a change in the current amplitude at the output of the power source entails a reduction in the average temperatures of heavy particles, almost 10% for a working frequency of 60 kHz and 14% for a frequency of 43 kHz. Moreover, it should be noted that the estimated \(\overline{T}_{h}\) values are higher when operating at 60 kHz for both power settings.
## 2 Data and Study Area
### Field Survey  
Ground surveys were carried out from 25 April to 2 May 2010, and 23 April to 3 May 2011. A handheld global positioning system (GPS) from Unistrong Science & Technology Co., Ltd, Beijing, China, with a positional accuracy of \(<\)5 m, was used to record the boundary of sampled fields. Based on the field surveys, samples for two classes, namely cropped and uncropped arable land, were obtained. Half of the samples were randomly selected as training samples, and the remaining half were used for the classification accuracy evaluation. The training and validation samples had no overlap.
###### Abstract
To address these challenges, this paper proposes an RMSprop-incorporated latent factorization of tensor (RLFT) model, with the primary research focus on incorporating an adaptive learning rate mechanism into the SGD training process. Specifically, the RLFT model enhances the baseline LFT framework by integrating the RMSprop algorithm, which employs exponential moving averages of squared gradients to dynamically adjust learning rates. This adaptive mechanism effectively suppresses gradient fluctuations and accommodates sparse gradients, thereby optimizing the convergence path for smoother and more efficient training. The proposed approach not only maintains data imputation accuracy but also significantly improves the convergence rate. The key contributions include the following:  
1. The incorporation of the RLFT optimization model for SHM data imputation, enabling rapid convergence with properly tuned hyperparameters;
2. A systematic analysis of missing rate impacts on model performance, with comprehensive RLFT algorithm design and analysis to guide engineering applications. The paper is organized as follows: Section 2 presents preliminary concepts. Section 3 details the RLFT model. Section 4 demonstrates the experimental results and analysis. Section 5 concludes the work.
# An Economical and Precise Cooling Model and Its Application in a Single-Cylinder Diesel Engine
## 3 Model Solutions
### Electric Cooling Model Solution  
For a steady case, the solution steps for the model of the electric cooling system are as follows:  
1. Matching the fan and water pump to the cooling system: \(\Delta p_{a}\) in Equation (4) should be equal to \(\delta p_{a}\) in Equation (7) to ensure that the fan works normally. This step is the same for the water pump. We combine these equations to obtain: \[q_{a} = q_{a}(n_{a}),\ \ P_{a}=P_{a}(n_{a})\] (8) \[q_{w} = q_{w}(n_{w}),\ \ P_{w}=P_{w}(n_{w})\] (9)
2. Calculating the heat dissipation of the radiator: Plug \(q_{w},q_{a}\) in Equations (8) and (9) into Equation (3) to obtain, \[Q_{c}=Q_{c}(\ \Delta T,\ q_{a}(n_{a}),\ q_{w}(n_{w})\ ),\] (10) By assuming that the heat rejection in Equation (1) is equal to the heat dissipation of the radiator, one obtains: \[Q(n,\phi)=Q_{c}(\ \Delta T,\ q_{a}(n_{a}),\ q_{w}(n_{w})\ ).\] (11) Recall that \(\Delta T=T(n,\phi)-T_{atm}\), where \(T(n,\phi)\) is the target temperature of the water jacket and \(T_{atm}\) is the atmospheric temperature. In Equation (11), only \(n_{a},n_{w}\) are unknown; thus, the fan speed \(n_{a}\) can be expressed as a function of the water pump speed \(n_{w}\), \[n_{a}=n_{a}(n_{w}).\] (12)
3. Obtaining \((n_{a},n_{w})\) and the total power consumption \(P\): \(P\) can be written as: \(P=P_{a}(n_{a})+P_{w}(n_{w})\) and combined Equation (12), \(P\) can be rewritten as a single-variable function of \(n_{w}\), thus minimizing the total power consumption; therefore, \(P\) can be written as: \[\frac{\mathrm{d}P}{\mathrm{d}n_{w}}=0,\] (13) Using this equation, \(n_{w}\) and then \(n_{a}\) can be solved, where \(P\) has a minimum value. At this point, the solution of the electric cooling model is complete.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 2 Deep Learning-Based Medical Image Analysis
### Segmentation  
One of the most difficult problems in medical image analysis is medical image segmentation, which involves separating the pixels of organs or lesions from background medical pictures like CT or MRI scans to provide crucial information on the shapes and sizes of these organs. Many researchers have proposed segmentation approaches for medical images using deep learning techniques. There are a few review articles [11; 59; 60; 61; 62; 63] on deep learning-based medical image segmentation, which shows that deep learning-based segmentation is now firmly established as a robust tool in medical imaging applications. Deep learning techniques are not only used for segmentation in 2D medical imaging but also these techniques are being explored for target volume automatic segmentation in three types of imaging modalities, i.e., CT, PET and MRI. A comprehensive review of deep learning-based retinal blood vessel segmentation methods for five years have been conducted [64]. And it presents trend analyses on the recently published literature and provides possible solutions for the identified gaps. The state-of-the-art work towards volumetric segmentation in three different imaging modalities is presented [65]. In the presented paper, various deep learning architectures related to volumetric segmentation are summarized and compared to the performance of different models using standard geometric evaluation metrics. Authors in [59] present their 3D deep supervision mechanism, using which they are able to perform volumetric segmentation tasks successfully conducted on liver segmentation from 3D CT scans and vessels segmentation from 3D MRI.  
There are few survey papers recently published related to deep learning-based medical image segmentations for various diseases. [PERSON] et al. [66] have conducted a survey for MRI brain tumor segmentation methods. In this review, the authors have highlighted the publicly available dataset related to brain tumor along with deep learning architectures used and tumor segmentation techniques. [PERSON] et al. [67] have surveyed more than 150 papers for extensively covering the technical aspects related to brain tumor segmentation under imbalanced conditions, different type of network architecture design and also multi-modality processes. The authors have also provided the future research possibilities in this direction. [PERSON] et al. [68] have presented a survey based on bottlenecks and strategies related to deep learning-based segmentation in medical images for musculoskeletal anatomical structures. They have analyzed 140 articles related to anatomical structures, network architectures, bioimaging techniques and various performance indicators.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 2 Materials and Methods
### Data Analysis
#### 2.7.2 Electrochemical Treatment Processes  
Statistical Analysis  
Statistical analyses were performed with IBMS SPSS for Windows, version 26 (IBM Corp., Armonk, NY, USA). The data normality was assessed by Kolmogorov-Smirnov, and Shapiro-Wilk tests, and by visual inspection of histograms. Removal in % was represented as mean \(\pm\) standard deviation. For each 3D treatment, comparisons between groups were made using the Mann-Whitney test, at a level of significance of \(p<0.05\).  
Parameters  
For all tests performed, the percentage of CBZ removal was calculated using Equation (6) [24]:  
\[\text{Removal }(\%)\ =\ \frac{C_{0}-C_{t}}{C_{0}}\times 100 \tag{6}\]  
where \(C_{0}\) and \(C_{t}\) correspond to the initial and final concentrations of CBZ (mg/L), and t is the time, which varied between 1 and 60 min, depending on the experiments.  
With the results obtained, the energy consumption (\(ECon\)) associated with each of the tests was calculated, using Equation (7) [43] and the current efficiency (\(CEf\)) applied, using Equation (8) [44]:  
\[ECon(Wh/g)=\frac{U\ I\ \Delta t}{(C_{0}-C_{t})\ V} \tag{7}\]  
where \(U\) is the potential difference (\(V\)), \(I\) is the current intensity (A), t is the contact time (h), \(V\) is the treated volume (L), and \(C_{0}\) and \(C_{t}\) are the initial and instant \(t\) concentrations of CBZ (g/L).  
\[CEf(\%)=FV\frac{C_{0}-C_{t}}{I\ t}\times 100 \tag{8}\]  
where \(F\) is the Faraday constant (96,500 C/mol), \(V\) is the treated volume (L), \(I\) is the current intensity (A), \(t\) is the processing time (s), and \(C_{0}\) and \(C_{t}\) are the initial and instant \(t\) concentrations of CBZ (mol/L).  
If target contaminants are present at low concentrations, a more appropriate parameter for estimating the energy efficiency of the electrochemical treatment may be the electric energy per order (\(ECO\)) [43]. \(EEO\) is a criterion that expresses the electric energy (in kWh/m\({}^{3}\)) required to reduce the concentration of CBZ by one order of magnitude in a unit volume of contaminated water, which is calculated by Equation (9):  
\[EEO\left(\text{kWh/m}^{3}\right)=\frac{Pt1000}{Vlog\left(\frac{C_{0}}{C_{t}} \right)} \tag{9}\]  
where \(P\) is the rated power of the system (kW), \(V\) is the volume of solution treated (L) at time \(t\) (h), and \(C_{0}\) and \(C_{t}\) are the initial and at instant \(t\) concentrations of CBZ (mol/L).  
Two kinetic models, the first-order model (Equation (10)) and the second-order model (Equation (11)), were used to adjust the experimental results of CBZ degradation:  
\[Ln\frac{C_{0}}{C_{t}}=k_{1}\cdot t \tag{10}\]  
\[\frac{1}{C_{t}}-\frac{1}{C_{0}}=k_{2}\cdot t. \tag{11}\]  
In these equations, \(C_{0}\) and \(C_{t}\) are the initial CBZ concentration and the concentration at time \(t\) (s) (mol/L), respectively; \(k_{1}\) (s\({}^{-1}\)) is the first-order rate constant; and \(k_{2}\) (L/(mol-s) is the second-order rate constant.
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 2 Related Work
### Single Front View Estimation  
With the advent of CNNs and large training datasets, significant advancements have been made in monocular human 3D pose estimation. Before HMDs became popular, researchers attempted to estimate human 3D poses using front (outside-in view) images of humans. With front images, there are two typical methods to regress 3D pose: (i) directly regressing 3D joint positions, and (ii) regressing 2D joint positions and then lifting them to 3D. Examples of the first method can be seen in the works of [PERSON] [24] and [PERSON] [25]. [PERSON] et al. [24] proposed a method that explicitly enforces bone-length constraints in 3D pose prediction by using a generative forward-kinematic layer. [PERSON] et al. [25] proposed an architecture that extracts the embedded features of the images by the CNN and then inputs them into the pre-trained decoder. In addition, [PERSON] et al. [26] introduced a 3D approach that regresses a volumetric representation of the 3D skeleton through a coarse-to-fine architecture. These methods [13; 24; 25; 26; 27; 28], which directly regress 3D pose, often face generalization issues, as they tend to overfit to the specific environments used by the benchmark datasets.  
The solution to this generalization issue involves performing the estimation within two subtasks: (i) 2D pose estimation and (ii) lifting the estimated 2D pose to a 3D pose [7; 11; 12; 29]. In the first subtask (for 2D pose estimation), a regressor extracts direct pose vectors [30] or heatmap-based 2D pose features [5; 31]. In the second subtask, a regressor lifts the 2D joint pose into the 3D joint pose. For example, [PERSON] et al. [11] estimated the 3D joint pose by combining information from the sparse dictionary of the 2D heatmap. [PERSON] et al. [7] predicted 3D pose by selecting the best 3D mesh model that matches its 2D projection. [PERSON] et al. [32] proposed a method that generated a plausible 3D human model from 2D heatmaps with a pre-trained probabilistic layer, and refined these heatmaps by combining 3D pose projection and the image features. Additionally, [PERSON] et al. [33] introduced a simple architecture that lifts 2D joint positions to 3D joint positions with low error rates [33].  
These two-step approaches, however, suffer from a lack of depth information, as they estimate 3D pose only using 2D joint information, leading to ambiguous results. Inaddition, their studies focused on networks using images from a front view, which are not compatible with the recent HMD-based VR systems that require a high level of portability without the need for additional installations, such as placing a camera in a room.