esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Verification of Trajectory Tracking Control of a Quadrotor Carrying a Load
## 5 Conclusions  
In this paper, we address the problems of model uncertainty, modeling errors and external complex wind field perturbation simulation by taking the hanging system of a quadrotor as the control target. Our innovation is that, first, the load and quadrotor are analyzed as a whole. In the environment of model parameter uncertainty, modeling error and external complex wind field disturbance, an eight-degree-of-freedom mathematical model of the quadrotor load, including the pendulum angle, is obtained. Second, the neural network algorithm is added to the adaptive integral backstepping algorithm to achieve the fusion of multiple algorithms. Third, the new algorithm is applied to the eight-degree-of-freedom model of the quadrotor load, and the robustness and effectiveness of the proposed algorithm are demonstrated through simulation comparison with the adaptive integral backstepping algorithm and the backstepping algorithm. The quadrotor can be better stabilized in a jamming environment, enabling it to track the desired trajectory quickly and stably. The tracking control simulation of the 3D spiral trajectory is carried out in MATLAB/Simulink. The simulation results show that in an environment with external wind field interference, the quadrotor can reach the specified position quickly with less error, and the suspension swing angle is better than the adaptive integral backstepping algorithm and the backstepping algorithm by adding the neural network algorithm based on the original algorithm. It can be stabilized faster and reduce the influence of load swing, which effectively reflects the suppression of interference by the neural network and proves the effectiveness of the proposed algorithm. The safety of the quadrotor needs to be improved, so no practical experiments are conducted in this paper. This will also be the future research direction of the authors.  
Conceptualization, Y.F. and H.G.; methodology, H.G.; software, H.G.; validation, Y.F., X.H. and X.C.; formal analysis, Y.F.; investigation, X.H.; resources, X.C.; data curation, Y.F. and H.G.; writing--original draft preparation, H.G.; writing--review and editing, Y.F., X.H and X.C.; visualization, Y.F. and H.G.; supervision, X.H. and X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by \"the Nature Science Foundation of China\" (grand number 61976033, 51609033), \"the Nature Science Foundation of Liaoning Province of China\" (grand number 20180520005) and \"the Fundamental Research Funds for the Central Universities\" (grand numbers 3132021106, 3132020110).  
Not applicable  
The authors declare no conflict of interest.
###### Abstract
The different feature patterns can be extracted through convolutional operations by using different filtering mask weights and mask size assignments, such as \(3\times 3\), \(5\times 5\), \(7\times 7\), \(9\times 9\), \(11\times 11\), and so on. Hence, these extracted feature patterns can be used to employ studies of mammographic classification and to identify breast lesions. However, the fractional order-based masks require selecting suitable fractional order parameters (\(v\in(0,1)\)) and sizes of convolutional masks [41, 46, 47] to extract different aspects (horizontal, vertical, or diagonal edges) and useful features from the input images. The II process performs the spatial convolutions by using the summed area table (SAT) [43, 44, 45] to detect the line and diagonal edge features. The II-based convolutional process does not require the convolutional mask's parameters and sizes. Therefore, after the 2D spatial convolutional process, via image enhancement (to adjust the contrast and maintain the features), the possible lesion can be easily detected and located in a ROI with a specific bounding box, and the \"feature pattern\" can be easily picked out from the original mammographic image. Then, converting a 2D feature pattern into a \"1D feature vector\" by the flattening process, the multi-round 1D convolutional processes subsequently enhance the incoming feature vector as feature signals, which can also increase the significant characteristics for further feature extraction and classification applications. The proposed 1D convolutional operations use the simpler linear weighted mathematical sums to deal with the incoming subsequent feature signals and can remove unwanted noises. Additionally, the 1D kernel convolutional process can quantify the difference levels in feature signals for separating Nor from B and M classes. In real-time application, this simple architectural can be easy to implement for the intended medical purpose. In the classification layer, feed the 1D feature pattern into the input layer of gray relation analysis (GRA)-based classifier [48, 49], and the mammographic classification of breast lesions can be identified, including Nor, B, and M classes. In experimental validations, mammographic images were collected from the MIAS database [22, 23], including training datasets and testing datasets for training the classifier and verifying the classifier performances in clinical applications. With the K-fold cross-validation, the experimental results showed the promising classifier's performances for automatic breast lesions screening, with precision (%), recall (%), accuracy (%), and F1 score indices [39; 40; 41].
# Microbial Application to Improve Olive Mill Wastewater Phenolic Extracts
## 4 Materials and Methods
### Extraction of Phenols from OMW  
Four commercial adsorbent resins, Amberlite XAD-16 (Sigma Aldrich, Milan, Italy), Sepabeads SP-207 (Mitsubishi Chem. Co., Tokyo, Japan), Purosorb PAD900C (Purolite, Milan, Italy), and Hamilton C18 (Thermo Fischer Scientific, San Jose, CA, USA) were selected and tested. XAD-16 and SP-207 are styrene-divinylbenzene (S-DVB) copolymers; PAD900C is polydivinylbenzene polymer; and finally, C18 having Poly(S-DVB) matrix was used as analytical reference.  
Before their employment, the adsorbent resins were pretreated with 95% ethanol (food grade, Alcoolita, Italy), washed with water (HPLC grade, [PERSON], Italy), and then oven-dried at 70 \({}^{\circ}\)C up to constant weight. All adsorption experiments were conducted with determined quantities of activated dried resins. Then, 1 bed volume (BV) of each dry resin (about 20 mL) was loaded on a glass preparative column (length, 30 cm; i.d., 0.5 cm) connected with a peristaltic pump. After rinsing the resins with water, the OMW, pretreated as described in Section 4.1, was passed on resins until their saturation. Loading was stopped when the outflow OMW reached the same absorbance values and color of the loaded OMW. Before the desorption phase, each saturated resin was washed with 4 BV of water to remove water-soluble compounds. The adsorbed phenolic fraction of each resin was then recovered with 2 BV of a 95% ethanol/water solution (60:40, _v/v_). The collected desorbed fractions were finally concentrated after vacuum distillation of ethanol at 40 \({}^{\circ}\)C by using a rotary evaporator (Rotavapor RE111, Buchi, Cornaredo, Italy).
## 2 System Overview
### Ground Station Software  
Mission Planner software [22] was used to remotely monitor the flight missions of the quadrotor-based drone. Figure 5 shows a screenshot of the mission planner interface during one such mission. As is visible from Figure 5, a variety of data is accessible live via the telemetry link. This includes altitude, ground speed, battery voltage, battery current, and even the estimated battery capacity remaining. The right-hand-side of Figure 5 shows the drone on a map, whereas the panel on the left-hand-side shows all the information mentioned above. This software interface was used to gather all the data related to the experiments mentioned in Section 3.
## 4 The Proposed Two-Step Access and Handover Strategy  
In the overall strategy, users need to select one satellite to connect from a GEO satellite and several LEO satellites within the coverage. A GEO satellite has higher orbital altitude than LEO satellites, which will lead to a larger propagation delay, so it is not suitable for some services that are more sensitive to delay. Due to the rapid movement of LEO satellites, users may need to switch several times during the communication. Therefore, users need to decide whether to connect to the LEO satellites according to the services delay tolerance and the load status of the LEO constellation. If the user chooses to connect to LEO satellites, the appropriate LEO satellite should be selected according to other factors. So, we propose a two-step access and handover strategy, which consists of the network selection based on utility function and the LEO satellite selection based on the Importance-TOPSIS. Figure 2 shows the process of the two-step access and handover strategy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Grade**} & \multicolumn{2}{c}{**Maximum**} & \multirow{2}{*}{**Example**} & \multirow{2}{*}{**Satellite to Choose**} \\  & **Delay Tolerance** & & \\ \hline
1 & \(\ll\)1 s & Conversational voice, command & LEO Satellite \\
2 & 2 s & Video messaging, transactions & LEO or GEO Satellite \\
3 & 10 s & Streaming audio, download image & LEO or GEO Satellite \\
4 & \(\gg\)10 s & Fax, background & LEO or GEO Satellite \\ \hline \hline \end{tabular}
\end{table}
Table 1: Service Classification.  
Figure 2: The flowchart of the two-step access and handover strategy.  
As shown in the flowchart (Figure 2), suppose that a mechanical arm working on the sea surface needs to be remotely controlled. The satellites that can be connected to the mechanical arm include one GEO satellite and two LEO satellites. According to Table 1, the control command is in grade 1 service with high delay sensitivity, so the GEO satellite is not considered in the first step. The second step is LEO satellites selection. If the two LEO satellites are not saturated, they would be sorted by using Importance-TOPSIS, and the appropriate LEO satellite is selected for access or handover.
## 3 Research Methodology
### Relationship between External Variable and ATU  
The study clearly shows that the ATU (attitude toward use) is directly involved in the user's attraction to using a certain information system. Comfortability is a feeling of physical or mental ease, frequently categorized as an absence of hardship or how much something or somebody is comfortable. Thus, we can create a positive association between comfortability and smartphone adoption. For this hypothesis, we can predict that smartphone acceptance and adoption is influenced by gender and age differences.  
**Hypothesis 1**.: _Comfortability significantly influences the attitude towards smartphone acceptance, adoption, and service._  
[PERSON] and [PERSON] and [PERSON] et al. stated that social influence is an immediate factor in the intention to technology usage. Furthermore, they described that social impact is the level of how an individual trusts that significant others require them to utilize technology. A hypothesis can be created that the attitude to using a smartphone can be affected by social influence.  
**Hypothesis 2**.: _Social influence significantly influences the attitude towards smartphone acceptance, adoption, and service._  
The relationship of enjoyment with ATU is essential for older adults' intention toward smartphone adoption [28]. It represents an individual's amount of pleasure in utilizing a particular technology, besides any performance outcomes caused by technology or system use.  
**Hypothesis 3**.: _Perceived enjoyment has a significant influence on the attitude towards smartphone acceptance, adoption, and use._  
Fear of failure has a positive influence on attitudes towards actual smartphone usage. A persistent fear of failure makes people believe that they are not good enough to attempt certain activities. Therefore, older people can be influenced by fear of failure, which means that they would feel fear of failure or defeat and do not accept their weaknesses that directly affect smartphone acceptance and adoption. Thus, a hypothesis can be generated as follows:  
**Hypothesis 4**.: _Fear of failure (Atychiphobia) has a significant influence on the attitude towards smartphone acceptance, adoption, and use._[PERSON] stated that older people have different finger dexterity, working memory, hearing, and visual sensitivity compared to youngsters; they may have difficulties using phones with small text, screens, buttons, and with complex functionalities. Thus, the ability to use technology alludes to being aware of an innovation that is currently becoming popular, and is being promptly acknowledged in the market and industry [29]. We can create a hypothesis that technology awareness positively impacts smartphone users in older persons.  
**Hypothesis 5**.: _Technology awareness has a significant influence on the attitude towards smartphone acceptance, adoption, and use._
## 2 Advanced Oxidation Processes
### Sono-Based Technologies
Nevertheless, the drawbacks associated with sonochemical processes as the high energy requirement or poor mineralization may affect their choice and their use in pollutants degradation at full-scale. In fact, other AOPs with better performances and lower energy requirements can be used in wastewater treatments. An overview of parabens degradation by sono-based technologies is presented in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pollutant** & **AOP** & **Catalyst** & **Test Conditions** & **Results and Conclusions** & **Reference** \\ \hline BuP & Photosonochemical & (no catalyst) & \(\bullet\) & BuP concentration of 20 mg/L. & BuP removal: 61.3\%, 60.02\%, and 87.96\% for UV, US (best value), and US/UV (best value). & [37] \\  & & \(\bullet\) & 120 min reaction. & \(\bullet\) & 99.2\% best removal for  optimized conditions. & \\ \hline PP & Sonochemical & (no catalyst) & \(\bullet\) & 1 mg/L of PP in ultrapure water (UPW), bottled water (BW), ground water (GW), and wastewater (WW), 125 mg/L of biochar (BC) & & In 60 min, degradation was about 42\%, 55\%, 55\%, and 78\% for WW, GW, GW, BW, and UPW, respectively, using 20 W/L and BC. & [38] \\  & & \(\bullet\) & 1.52 mg/L of MP & \(\bullet\) & Degradation: 350 (about 99\% removal) \(>\) 620 (99\%) \(>\) 22.75 W/mL for different frequencies (200, 350, 620 and 1000 kHz). & Degradation: 350 (about 99\% removal) \(>\) 620 (99\%) \(>\) 200 kHz (85\%). & Degradation: 3
# BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition
## 3 Method
### Weights Transfer  
We divide the training into two stages and transfer the weights from the first stage to the second stage, and then use the proposed balanced loss function to balance the long-tailed problem.  
**Data Balanced Review.** In order to obtain a relatively balanced dataset, we propose the data balanced review method in [53]. For class-\(j\), \(D_{j}\) is the set of all its training samples. We denote the middle number of classes as \(m=C/2\) and obtain the first-stage training data \(D_{stage1}=\sum_{j=1}^{m}D_{j}\). In the second stage, we define the relatively balanced training set \(\hat{D}=\{\hat{x}_{i},\hat{y}_{i}\},\hat{y}_{i}\in[1,2,\dots,C]\) as:  
\[\hat{D}=\sum_{j=1}^{m}D_{j}[sam(n_{m+1})]+\sum_{j=m+1}^{C}D_{j} \tag{2}\]  
where \(sam(\cdot)\) is a random sampling method [54]. Figure 1 provides a more intuitive display.  
**Two-stage Transfer.** Knowledge transfer currently has a wide range of research and applications, and its main idea is to transfer knowledge-rich upstream tasks to knowledge-scar downstream tasks [55]. In the imbalanced long-tailed training set, which includes classes with sufficient knowledge and classes with insufficient knowledge, we use the Data Balanced Review [53] method to balance the dataset of downstream tasks based on the idea of head-to-tail transfer. Therefore, we use two stages to train our framework: According to Figure 1, we denote the training network for stage 1 as:  
\[\Theta^{*}=argmin_{\Theta}F\big{(}\Theta;D_{stage1}\big{)} \tag{3}\]  
At this stage, we use cross-entropy (CE) loss as the cost function denoted as \(L_{CE}\). Based on the \(\theta^{*}\) from stage 1 of training, we freeze the model parameters of stage 1 and add a new Fully Connected (FC) layer, and optimize FC layer weights \(\Theta_{fc}\) by minimizing the loss \(L\) throughout the entire training set \(\hat{D}\) to train the stage 2 network:  
\[\hat{\Theta}=argmin_{\Theta_{fc}}F\big{(}\Theta_{fc};\hat{D}\big{)}_{\Theta^{*}} \tag{4}\]  
Figure 2: Detailed introduction of the second stage. We freeze the model parameters of stage 1 and add a new Fully Connected (FC) layer to achieve the weight transfer of the model. In BWLM, \(L_{BFS}\) balances the focus on common and rare classes by combining \(L_{CB}\) and \(L_{BS}\) as the loss function. \(L_{BS}\) correctly directs attention of loss to the rare class through a posterior distribution, while \(L_{CB}\) reduces the redundancy of the common sample through effective samples. Afterward, weight decay is adopted to balance the classifier weights further.  
Through our exploration, the weight transfer method can improve the accuracy of knowledge-deficient classes, but the accuracy of knowledge-sufficient classes relatively decreases.
## 1 Introduction
Proppant refers to a granular material with a certain particle size and strength, which plays a role in preventing fracture closure and maintaining the long-term conductivity of oil and gas channels after being pumped into the formation with fracturing fluid, thereby improving the recovery efficiency [23; 24]. [PERSON] et al. [25] developed a fully coupled fluid flow and geomechanical model to simulate partially propped fracture closure and found that fractures with alternating propped-unpropped-propped cross-sections can produce high conductivity channels. [PERSON] et al. [26] considered the deformation and embedding of proppants in fractures and derived an analytical model from contact mechanics to describe the changes in fracture width and conductivity during production. Unlike hydraulic fractures, natural fractures usually have poor propping properties, and their mechanical behavior mainly depends on the cementing minerals and surface roughness. [PERSON] et al. [27] conducted production history fitting and production history prediction based on the phenomenon that the widths of hydraulic fractures in shale gas reservoirs change over time. The analysis found that, if the change in fracture width is not considered, the cumulative production will be overestimated. [PERSON] et al. [28] proposed an implicit simulator that integrates-proppant migration and production, considering the effects of proppant sedimentation, fracture closure and multiphase flow, and simulated and analyzed the effect of proppant particle size on proppant migration. The numerical simulation results showed that, in low-permeability and low-leakage formations, the proppant will settle at the bottom of the fracture during the post-fracturing shut-in period, which may lead to an increase in the tortuosity of the near-wellbore fracture and a deterioration in the connectivity between the wellbore and the hydraulic fracture network. [PERSON] and [PERSON] [29] proposed a semi-analytical model for the flow of gas-water two-phase fluids through fractures, using the material balance equation to obtain the average pressure in the fractures. The flowback model and production model were combined to propose a workflow for evaluating the fracture closure width. The flowback and long-term production data were used to estimate the fracture conductivity and width, and the fracture closure was quantitatively evaluated. This paper considers the creep properties of deep shale, the influence of proppant embedment and deformation on fracture closure, establishes a fluid-solid-thermal coupling model of the proppant-fracture-matrix structure and focuses on analyzing the influence of proppant parameters on the deep shale gas production process, providing guidance for subsequent proppant optimization. It must be emphasized that the proposed extraction optimization model exclusively targets the reconstruction of gas-bearing spaces and enhancement of seepage efficiency for existing hydrocarbons, rather than accelerating new hydrocarbon generation--a process fundamentally constrained by irreversible geochemical kinetics.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 2 Related Work
The positions of assisted nodes in COVEN algorithm must satisfy three conditions as follows:  
* \(P_{i}\) lies on the line that bisects the inner angle formed by \(V_{i}\) which is Voronoi vertex;
* \(P_{i}\) is located in the Voronoi polygon;
* \(d(S_{i},P_{i})=\min\{2R_{s},d(S_{i},V_{i})\}\). The frame of our paper is similar with the paper (2014). The general idea is to detect the coverage holes in every triangle, then dispatch nodes to heal the holes. What different from previous work is as follows. Firstly, compared with the existed approaches mentioned above, we provide one simpler method to estimate the coverage hole. Secondly, we work out the bound of assisted nodes number and provide the minute deployment rules for the assisted sensors.
However, in outdoor environments, such as those encountered in applications like autonomous driving or surveillance systems, adverse weather conditions inject noise into images, leading to blurriness, decreased visibility, or obstruction of crucial information about objects. As a result, the degradation of visual features in images reduces the accuracy of object detection. Adverse weather conditions in image recognition typically refer to phenomena such as rain, snow, fog, and dust storms. Given Taiwan's geographical location, encountering rainy seasons or typhoon climates throughout the year is common. Therefore, among adverse weather conditions, rainfall is one of the most severe factors affecting image recognition applications. Consequently, developing object detection models adapted to rainy weather conditions is a valuable research issue, not limited to Taiwan alone. This study will focus on the data-driven domain to evaluate rainy weather image generation and detection models, aiming to develop object detection models for rainy weather conditions. Here are the main contributions of this study: (1) To the best of our knowledge, this research marks the inaugural endeavor to formulate an object detection model tailored to rainy weather conditions using RTDETR. (2) By leveraging image augmentation and synthesis techniques to generate rainy weather noise, we augmented real data for model training, mitigating the constraints associated with the scarcity of authentic rainy weather images. This approach is particularly advantageous for bespoke detection models, enabling them to glean insights from diverse and intricate rainy weather imagery, thereby enhancing their generalization capability during deployment. (3) Through a comprehensive evaluation of rainy weather image generation methods and detection models, we discerned that the selection of a rainy weather image generation method should hinge upon the characteristics of the specific detection model. (4) RTDETR exhibits exceptional prowess in discerning object features from rainy weather images. Relative to YOLOv8, it manifests superior proficiency in training object detection models with heightened accuracy under rainy weather conditions. (5) Our findings underscore the efficacy of amalgamating a pre-trained RTDETR model with neural style transfer to generate rainy weather images for training purposes, delineating a robust and dependable data-driven methodology for crafting rainy weather object detection models.