esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Algorithms Used in the Study
#### 2.4.6 Artificial Neural Networks  
Artificial neural networks (ANNs) represent a computational architecture comprising artificial neurons, engineered to mimic the information processing mechanisms observed in the human brain, thereby enabling knowledge acquisition. These networks construct intricate input-output models capable of identifying intricate correlations within multi-dimensional datasets. Their ubiquitous use is found in a variety of engineering specialties [92; 93]. ANNs support a wide range of network architectures, including single and multiple layer networks. One instance is the feedforward back propagation neural network (FFBP), consisting of an input layer for receiving data, an intermediate hidden layer, and an output layer that provides outcome-specific insights in reaction to input data.  
This paper proposes a comprehensive integration between the GMDH features and the ANN network model. The group method of data handling (GMDH) algorithm finds applications in diverse research domains. For instance, the authors of [94; 95] employed GMDH for pore pressure analysis and permeability modeling, resulting in accurate permeability predictions. Additionally, GMDH has been utilized in rock deformation prediction, as demonstrated by [96]. In GMDH, the Kolmogorov-Gabor polynomial, referenced in [97], serves as a mathematical framework for understanding the relationship between input and output variables. For instance, when predicting \(E_{t}\) using input features like \(LL\), \(Fs\), and \(SR\), the mathematical Equation (4) is formulated as follows:  
\[E_{t}=a_{0}LL+a_{1}Fs+a_{2}SR+a_{3}LL^{2}+a_{4}Fs^{2}+a_{5}SR^{2}+a_{6}(LL\times FS )+a_{7}(LL\times SR)+a_{8}(Fs\times SR) \tag{4}\]  
where \(LL\), \(Fs\), and \(SR\) denote the input features representing liquid limit, sand percentage, and degree of saturation, respectively; \(a_{0}\), \(a_{1}\), \(a_{2}\),\(\ldots\), \(a_{8}\) are the coefficients; and \(E_{t}\) symbolizes the output, specifically the elasticity modulus.  
In the conventional ANN, five features (\(SR\), \(Fs\), \(e_{0}\), \(LL\), and \(\gamma_{d}\)) are utilized. In contrast, the proposed GMDH incorporates 20 features derived from the original five features to capture interaction effects. These features include both individual and interaction terms: \(SR\), \(Fs\), \(e_{0}\), \(LL\), \(\gamma_{d}\), \(SR^{2}\), \(Fs^{2}\), \(e_{0}^{2}\), \(LL^{2}\), \(\gamma_{d}^{2}\), \(SR\times FS\), \(SR\times e_{0}\), \(SR\times LL\), \(SR\times\gamma_{d}\), \(Fs\times e_{0}\), \(Fs\times LL\), \(Fs\times\gamma_{d}\), \(e_{0}\times LL\), \(e_{0}\times\gamma_{d}\), and \(LL\times\gamma_{d}\). The architecture of the suggested ANN-GMDH model, depicting the input characteristics, is illustrated in Figure 10B, while the conventional ANN model is represented in Figure 10A.  
Figure 10: Illustration of a graphical representation of the neural network models: (**A**) conventional ANN and (**B**) proposed ANN-GMDH.
## 5 Energy Management  
The Energy Management System of the VPP is made up of two main components: the Profile Generation Unit (PGU) and the Optimal Scheduling Unit (OSU).  
The PGU is responsible for the computation of the power profiles of all of the DER units and loads supervised by the VPP, including DG units, ESS units, and both controllable and uncontrollable loads (i.e., the aggregated power profile of all uncontrolled and unprogrammable loads). Each power profile is defined as the active power produced or consumed by the specific physical entity over a given time horizon (typically 24 h) with a system-defined time step (usually 15 min). For each physical entity, one or more profiles can be generated depending on the possible different configurations of the resource. Air conditioning systems, for instance, may have different feasible demand profiles, depending on the considered scenarios (i.e., comfort level, use of thermal storage, etc.), based on the given weather forecasts, expected room occupancy, and time schedules. For each power profile (which corresponds to a specific use of the device), a command profile (or schedule) is also determined, containing all of the control parameters required by the physical entity to meet the desired power profile. Power profiles are determined for all of the supervised entities by taking into account the users' requests, weather forecasts, strategic schedules and constraints (set by the system administrator), historical data (used by machine learning algorithms), and current measurements from distributed sensors (i.e., current weather data, room occupancy, state of charge of EES units, etc.).  
The OSU is responsible for the computation of the optimal working point of the VPP. It collects the power profiles and related schedules from the PGU, optimization constraints and objectives from the systems administrator, and DR requests from the DSO or IO (Price-Based and Incentive-Based requests in normal operating mode). After the optimal working point is determined, the related command profiles are sent to the local controllers by means of specific communication agents.
# Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
## 3 Fault Handling Method and Theoretical Discussion
### Time (\(t<t_{0}\))  
The capacitor power supply is activated, starting the system power supply. The STM32 microcontroller sends a signal to the optocoupler isolation circuit, which then conducts the thyristor T\({}_{0}\) and MOSFET VT\({}_{1}\) in the steady-state branch through the drive circuit, while simultaneously turning off the semiconductor switching devices in the power electronic branch. The short-circuit circuit (in parallel with the load), consisting of MOSFETs and optocouplers, is now turned off. The system enters its normal state, with the current path as shown in Figure 3a. At this point, the system's rated current value is \(I_{0}=U_{dc}/\left(R_{line}+R_{L}\right)\).
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 7 Current Situation and Development Trend
### Development Trend
#### 7.2.4 Intelligent Mobile Edge Computing  
As mentioned in [21], the BS in the center has a longer life cycle than in the corner or outside the domain. Moreover, the centralized processing mode represented by cloud computing is unable to handle the number generated by edge devices efficiently. Smart edge proposes that every edge device in the IoT can be equipped with data collection, analysis, computing, communications, and most importantly intelligence. The prominent advantages of the intelligent edge are to reduce latency, reduce bandwidth, reduce duplication, and improve security. However, at present, the intelligent edge also has the problem of resource limitation. Some intelligent edge computing methods can be considered to improve the QoS of wireless networks: (1) real-time use of a UAVs and an efficient means to obtain and analyze data, it is about the UAV task allocation problem, the swarm intelligence algorithm is very good at addressing this issue; (2) only connecting the most valuable data in the edge device to the cloud to reduce data transfer. Ideally, edge nodes send only the absolutely necessary information and make critical decisions as soon as they have access to critical data. For this purpose, appropriate scheduling tasks are needed by considering the priority assignment where SI algorithms are available.
###### Abstract  
Although underutilized, spirometry is essential in the diagnosis and management of chronic obstructive pulmonary disease (COPD) and asthma. This study aimed to investigate a mobile (i.e., transportable) lung function testing (LFT) services in two metropolitan and two rural clinics in Western Australia. Individuals attending a mobile LFT clinic in 2021 were invited to complete questionnaires at baseline and after 6-8 weeks. Questionnaires were completed by 59/74 (79.7%) respondents (mean age 62.5 \(\pm\) 14.2 years); most were female (35/59; 59.3%). A history of asthma was reported in 50.9% (30/59) and COPD in 18.6% (11/59) of respondents [13.6% (8/59) reported both]. At baseline, most (22/30; 73.3%) had asthma control test scores \(\leq\)19 (mean 16.6; range 8.0-25.0); at follow-up, 16/31 (51.6%) had scores \(\leq\)19 (mean score 18.0; range 6.0-25.0). Of the 11 diagnosed with COPD at baseline, the mean Clinical COPD Questionnaire and COPD assessment test scores were greater at follow-up (1.9 vs. 2.3; and: 10.3 vs. 14.7 respectively), reflecting worsening disease. Most participants (57/59; 96.6%) were satisfied with the LFT experience. The role of mobile LFT services to optimize the diagnosis and management of chronic lung disease and to minimize patient burden requires further investigation to improve short term patient outcomes.  
s +
Footnote †: journal:  
which provide a mobile (i.e., transportable) lung function testing service. Private laboratory testing potentially provides advantages over hospital or GP-based testing in terms of accessibility and quality of service. Mobile services are accessible in both metropolitan and rural areas of WA, offer a broad range of respiratory function tests, are generally conducted by qualified respiratory scientists with results reviewed and reported by respiratory physicians before being made available to the primary care physician to guide the patients' clinical management.  
The aim of this pilot study was to investigate the potential benefits and overall impact of a mobile respiratory function testing service in metropolitan and rural areas of WA. Specific objectives included discerning patient disease management before and after participating in the mobile lung function testing service and investigating GP perceptions of the benefit of the mobile lung function testing service.
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 2 Dataset and Target
### Identification and Characteristics of QP Emissions
However, due to the intricate morphology of the QP waves, direct training on all samples yielded poor accuracy (around 20%). After the meticulous screening of 3000 samples, we selected 827 images depicting various features indicative of QP phenomena. These 827 samples were manually annotated and served as the dataset for the object detection framework. Figure 3: The data were truncated to the latitude ranges of \(-69^{\circ}\) to \(-28^{\circ}\) and \(28^{\circ}\) to \(69^{\circ}\) to obtain reliable QP data by avoiding noise from EFD in the equatorial area. (**a**) A strong rising tone QP event observed in the southern hemisphere. (**b**) A weaker falling tone QP event observed in the northern hemisphere. (**c**) A very weak falling tone QP event observed in the northern hemisphere. (**d**) A QP event with both rising tone and non-dispersive types occurring simultaneously in the northern hemisphere. (**e**) A short-duration rising tone QP event under noise interference observed in the southern hemisphere. (**f**) A very prominent rising tone QP event observed in the northern hemisphere.
# Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
###### Abstract  
The extensive utilization of dyes across diverse industries has resulted in environmental pollution, leading to the degradation of water bodies. To prevent environmental contamination, the use of eco-friendly dyes and innovative processes for dye degradation is crucial. This study aimed to investigate the color removal process of cochineal dye (Dactyllopius coccus Costa) using the atmospheric pressure plasma jet (APPJ: Atmospheric Pressure Plasma Jet) technique. The dye extracted from the cochineal insect was treated with APPJ and the resulting color removal process was analyzed. Optical emission spectroscopy (OES) was used to investigate the plasma emission lines, and UV-Vis spectroscopy was used to monitor the color removal process. The results revealed that the decolorization of cochineal dye was a result of an oxidative degradation process caused by the interaction of the reactive species (NO\({}_{3}\)\({}^{-}\) and NO\({}_{2}\)\({}^{-}\)) generated by the APPJ plasma discharge with the dye molecules. This color removal process occurs in an acidic medium, leading to a pH change from 5.4 to 2.7. These pH changes can be attributed to fluctuations in the concentrations of reactive species such as nitrates and nitrites in the liquid phase. UV-Vis spectroscopy measurements showed that 90% of the cochineal color was removed within the first 10 min of treatment. This study enhances our understanding of natural color removal and provides insights into its mechanism, opening up possibilities for controlled modification and applications in various fields.  
oxide degradation; cochineal dye; APPJ technique +
Footnote †: journal: Applied Sciences
# Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution
## 2 Related Works
### Urban Logistics
(2019) presented effective branch-price-and-cut algorithms for four variants of the electric VRP with time windows (EVRPTW), which were defined according to the maximum number of recharges per route (single versus multiple) and the type of recharge (partial versus full). Therefore, the vehicles could be employed to meet the emission targets of delivery fleets or to serve restricted inner-city areas with noise and emission limits. More recently, [PERSON] et al. (2018) focused on developing a truck-bike mixture model to reduce operating costs for an existing truck-only service by replacing some of the trucks with bicycles and exploring the resulting effects in terms of reducing overall carbon emissions. The results showed that an almost 10% reduction in carbon emissions could be achieved. However, reducing the distance traveled by trucks and increasing the distance traveled by bikes resulted in a small decrease in fuel costs but a much greater increase in labor costs. Although urban logistics has been investigated in academia as described above, the subject is still evolving because of the continuous changes in urban environments and citizens' lifestyles. In particular, the increase in mobile shopping and improvements in technological developments might lead to another delivery mode, such as drone-based parcel delivery using the rooftops of buildings in urban areas.
## 1 Introduction
A manipulator device consisting of a master/slave configuration is very attractive for application in the nuclear power plant dismantling process, as operator access is limited due to the possible radiation and the high load of material handling that is involved. However, it is difficult to use force sensors to realize force feedback in highly radioactive environments, because the force sensor will break as time passes. This paper implements tele-operated bilateral control of a hydraulic manipulator using sliding mode control with sliding perturbation observer (SMCSPO) for sensor-less force feedback. It is an efficient and robust control algorithm that not only estimates the reaction force of the master and the slave, but also applies this estimated reaction force to the tele-operated bilateral control of the hydraulic manipulator of a 3 degree of freedom (DOF) master/slave robot. The reason for using a hydraulic manipulator is that its power-to-weight ratio is better than any other type of actuated robot at the expense of positional accuracy. In this research, the sliding perturbation observer (SPO) is implemented to estimate the reaction force of the slave without using any sensor. The tele-operated bilateral control scheme is implemented for efficient and accurate position and force tracking betweenin the master/slave configuration with visual feedback. In the bilateral controller, the difference of the reaction force of the slave manipulator and the operating force applied to the master manipulator is designed to target the impedance model. The reaction force of the slave is a result of the effects in the remote environment, while the operator force is applied by the operator (human) at the master manipulator. The experimental results of our studies confirm that the slave efficiently follows the position trajectory of the master system. The remainder of the manuscript is structured as follows: Modeling and dynamics of the hydraulic manipulator are described in Section 2; the theory of sliding mode control with sliding perturbation observer and the algorithm for estimating reaction force are presented in Section 3; the tele-operated bilateral-control strategy is described in Section 4; the details of the experimental setup are summarized in Section 5; the results are shown in Section 6; and concluding remarks regarding the study are provided in Section 7.
## 4 The Proposed Scheme: Architecture and Components
### The COVID-19 Layer  
In the COVID-19 layer, the geo-locations of COVID-19-affected areas can be tagged. In such locations, the role of governing bodies is to control the COVID-19 pandemic effect through specialized UAVs, i.e., different UAVs for monitoring, inspection, and social distancing. All UAVs can communicate with each other wirelessly to exchange data about geo-location coordinates, personal details, and contact histories. Each UAV consist of a wireless control unit that uses radio frequency to communicate with each other (e.g., sharing critical data). Formally, it operates with a wireless fidelity (Wi-Fi) protocol with a radio frequency ranging from 2.4 GHz to 5 GHz for data exchange, but as we are using 6G-enabled UAVs, they utilize higher operating frequencies (i.e., 102.1 GHz), providing a much more stable frequency range for data exchange. Due to 6G-based communication, UAVs can quickly share data with higher data rates and reduced latency, thus improving the performance of the entire system. Then, the monitoring UAV informs the sanitization UAV via the GS about the affected region where the sanitization is required. UAVs generally target regions labeled as red and orange zone (highly affected areas).  
The aim of social distancing UAVs (centrally controlled by the police department) is to ensure that people maintain the required distance from those affected by the virus. It is equipped with either thermal or infrared and color image cameras. It first traces the human bodies and then calculates the distance between them. Distance could be either Euclidean distance or Manhattan distance, where X and Y are the location coordinates of two persons. If the distance is less than the threshold value \(D_{th}\), then an automatic announcement \"_maintain social distancing_\" can be made through the UAV.  
The aim of sanitizing UAVs is to disinfect the COVID-19 high-affected areas at regular intervals to mitigate its effects and further spread. Sanitizing UAVs identifies the spot precisely through improved 6G communication, which has high-bandwidth channels. It  
Figure 4: 6G features for operating UAVs in the COVID-19 pandemic [40].  
can form high-capacity mmWave communication with GS for improved transmission. Moreover, spot location pictures can be transmitted at ultra-high-definition (UHD) and 4K resolution cameras supporting augmented and virtual reality. Thus, UAVs identify the spot correctly and accurately with less wastage of disinfectant. The function of a monitoring and inspection drone (healthcare drone) is to trace the humans first, then measure their body temperature and heart rate (the basic symptoms of coronavirus). If anybody is found suspicious, then the traced information can be forwarded to the concerned authority. Based on that information, a person can be quarantined for a stipulated period. The details of 3 FFs are presented as follows.
# Real-Time DTM Generation with Sequential Estimation and OptD Method
## 2 Materials and Methods
### Sequential Estimation in the Context of DTM Generation
 \tag{9}\]For independent observation results in sequences I and II, the following weight matrix can be written:  
\[\mathbf{P}= \begin{bmatrix}\mathbf{P}^{II}\big{|}0\\ --\\ 0\big{|}\mathbf{P}_{\mathbf{X}}\end{bmatrix}  \tag{10}\]  
where \(\mathbf{P}_{\mathbf{X}}=A^{T}\mathbf{PA}\) is the sequence I weight matrix and \(\mathbf{P}^{II}\) is the weight matrix defined for the new measurement results obtained in sequence II; 0 is zero matrix notation. The solution of the system of Equation (9) using the least squares method is the estimators:  
\[\hat{\mathbf{X}}^{(II)}=-\big{(}A^{T}\mathbf{PA}\big{)}^{-1}A^{T}\mathbf{P}\mathbf{L} \tag{11}\]  
The \(m_{0}^{2(II)}\) coefficient determined after sequence II is calculated from the following formula:  
\[m_{0}^{2(II)}=\frac{\left(\hat{\Phi}^{(II)}\right)^{T}\mathbf{P}\hat{\Phi}^{(II)}}{ n-m} \tag{12}\]  
If only new observations are added, dependence (8) can be written as follows:  
\[ \begin{bmatrix}\mathbf{v}^{II}\\ \mathbf{\Delta}_{\mathbf{X}}\end{bmatrix} = \begin{bmatrix}\mathbf{A}_{\mathbf{P}}^{II}\\ \mathbf{E}\end{bmatrix} \mathbf{X}_{\mathbf{P}}^{II}+ \begin{bmatrix}-\mathbf{L}^{II}\\ -\mathbf{X}_{\mathbf{P}}\end{bmatrix}  \tag{13}\]  
The solution to problem (13) based on the least squares method takes the form of Equation (9) with a weight matrix such as in Equation (10) and an \(m_{0}^{2(II)}\) coefficient determined as in Equation (12). The implementation of subsequent sequences is performed until the LiDAR measurements are completed. The calculations should also take into account the interrelationships between the measuring strips of the LiDAR, i.e., establish a sequence overlap or additional data. These issues will be considered in future research. The proposed concept allows for the adjustment of parameters at different stages of the method, to meet a specific user's needs: width of measuring strips during sequence loading, reduction criteria, such as the number of points after reduction or the tolerance range in the Douglas-Peucker algorithm, the degree of the polynomial model value, and an acceptable tolerance for the values \(\vartheta_{i}\) to accept or reject the model from the previous sequence. It is also possible to set all parameters at the input, so as not to interfere with the computational process while the processing is in progress. The approach depends on the user, their experience, and the type of data.