esa-sceva/satcom-chunk-collection huggingface dataset:
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 4 Design and Implementation  
In this section, we will discuss the design and implementation of the CoAP Handler in the NAP of the POINT architecture. The design and implementation of the CoAP Handler take into account the various CoAP-specific communication scenarios (e.g., the CoAP observe, group communication) as presented in our earlier CoAP over ICN work [10]. The details of the algorithms supporting CoAP observe in the NAP is presented in [11]. The basic idea of our solution is that the CoAP Handler receives CoAP requests from CoAP clients (over IP), performs protocol translation from CoAP messages to ICN messages and forwards the requests to the CoAP server. The CoAP server generates a response which is forwarded to the CoAP clients following the reverse process. The ICN network is oblivious to the CoAP endpoints.
## 3 Implementation
### Multi-Criteria Optimization and Pareto Front  
As introduced in Section 2.1, we use a composite objective (cost) function (8). This can be also solved in a series of optimizations with varying weighting factors \(\alpha\), resulting in a set of optimal designs based on these weighting factor values. Each optimal design belongs to the Pareto optimal set, which is also referred to as a Pareto front. The Pareto front allows the worker or engineer to decide on the trade-off between the different objectives represented in the composite function, here smoothness and arc-length. Therefore, by exploiting this method, we avoid having to choose a value for the weighting factor \(\alpha\) a priori.  
The Pareto front is shown here as discretely approximated for each value of the weighting factor \(\alpha\). Due to differences in magnitude between the objective functions of smoothness and arc-length, a logarithmic distribution can be chosen to ensure proper spreading of the points and, therefore, a well-approximated Pareto front. The set of constrained optimization problems leading to the Pareto front then becomes:  
\[\min_{\mathbf{x}}\left\{F\left(\mathbf{x}\right)\right\} \tag{30}\] \[\text{where }F =\alpha_{i}F_{1}+(1-\alpha_{i})F_{2}\] \[\mathbf{x} =\left[\tau_{1}\quad\tau_{2}\quad\ldots\quad\tau_{N}\right]\] \[\text{such that }\sum_{i}^{n}\tau_{i}-t_{f}=0\text{ and }\tau_{i}>0\]  
where \(F\) is the cost function in (29) and \(\tau_{i}\) are the time intervals between via-points on the trajectory found via the variational problem. It should be noted that the equality constraint on time, requiring that the operation be completed in the desired amount of time, is implemented as two inequality constraints,  
\[g_{1} =\frac{\sum_{i}^{N}\tau_{i}}{t_{f}}-1 \tag{31}\] \[g_{2} =1-\frac{\sum_{i}^{N}\tau_{i}}{t_{f}}. \tag{32}\]  
The efficient second-order algorithm NLPQLP [30] is utilized to solve this optimization problem posed in (30)-(32). This optimization formulation, though, is general, and therefore, any gradient-based optimization algorithm can be applied.  
In Section 4, it will be shown that with a relatively low number of points, the Pareto front is well defined and can be used effectively by the worker or engineer to make design decisions. It should be further pointed out that this compromise decision is to be made by a worker, and therefore, more than two or three objectives are typically not able to be handled (unable to be readily shown graphically) and therefore not advisable.
## 3 Research Progress on Adsorption and Separation of Hydrocarbon Molecules
### Aromatic Hydrocarbons
#### 3.2.5 Chitosan  
As a natural polysaccharide, chitosan is a cheap adsorption material extracted from seafood shells and natural substances. Owing to its substantial hydroxyl functional groups, it is widely applied to PAHs adsorption [73]. Specific demands for different adsorbents can be satisfied by blending or crosslinking chitosan with other polymers. [PERSON] [74] synthesized a membrane composed of chitosan, polyvinylpyrrolidone (PVP), and carbon nanotubes (CNTs) with a specific surface of 253 m\({}^{2}\)/g. Moreover, carbon nanotubes with different functional groups were obtained by gamma-ray irradiation for NAPH adsorption. The effective removal rates were 93% and 97% at 120 min and 150 min, respectively. The rapid adsorption of aromatic hydrocarbons can be attributed to the strong \(\pi\)-\(\pi\) interaction between aromatic hydrocarbons (naphthalene used in this paper) and the surface of hydrophobic carbon materials.
# Impact of COVID-19 Mobility Changes on Air Quality in Warsaw
## 2 Materials and Methods
### Air Quality Data  
Air quality in Warsaw is measured by seven air quality monitoring stations that are part of the State Environmental Monitoring and belong to the Chief Inspectorate of Environmental Protection (GIOS). Of these, nitrogen dioxide (NO\({}_{2}\)) concentrations are automatically measured at three locations. The results are given as 1-h averages. These are the following stations:  
1. Warsaw-al. Niepodleglosci (centre of Warsaw) (Figure 1);
2. Warszawa-Kondratowicza (Targowek-north-east district);
3. Warsaw-Wokalna (Ursynow-south district).  
The station in the centre of Warsaw is a specialised station, the so-called traffic station, which reads concentrations of air pollutants coming directly from road transport. Stations in Targowek and Urysnow are so-called urban background stations, located at some distance from key sources of air pollutant emissions. In the research work, it was assumed that air quality data in 2020 would be compared to prior 3 years (2017-2019). This method has been used in previous studies for COVID-19 and air quality (29; 30).  
We used the \(t\)-Student test and the ANOVA test to identify statistically significant differences between the distributions of air pollutant concentrations. The results for which the significance level \(p<0.05\) were considered statistically significant.
# Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage
## 3 Results
### SAR Parameters and Their Correlation with Crop Height
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SAR Parameters**} & \multicolumn{2}{c}{**Maize Height (H)**} & \multicolumn{2}{c}{**Sunflower Height (H)**} & \multicolumn{2}{c}{**Wheat Height (H)**} \\ \cline{2-7}  & **H \textless{} 150 cm** & **H \textgreater{} 150 cm** & **H \textgreater{} 92 cm** & **H \textgreater{} 92 cm** & **H \textless{} 53 cm** & **H \textgreater{} 53 cm** \\ \hline Linear Polarization & & & & & & \\ C11\_Intensity (VH) & **0.80** & \(-\)0.01 & **0.31** & \(-\)0.06 & \(-\)**0.65** & 0.03 \\ C22\_Intensity (VV) & **0.81** & \(-\)0.20 & 0.17 & \(-\)0.19 & 0.62 & \(-\)0.01 \\ H-Alpha Decomposition & & & & & & \\ Entropy (H) & 0.53 & 0.20 & 0.01 & 0.20 & \(-\)0.61 & 0.06 \\ Anisotropy (A) & \(-\)0.54 & \(-\)0.23 & 0.00 & \(-\)0.18 & \(-\)0.65 & \(-\)0.05 \\ Alpha (a) & \(-\)0.52 & \(-\)0.19 & \(-\)0.05 & \(-\)0.17 & **0.67** & \(-\)0.07 \\ Radar Vegetation Index & & & & & & \\ RV1 & 0.53 & 0.21 & 0.11 & 0.18 & \(-\)0.65 & 0.03 \\ Intensity Ratio & & & & & & \\ VH/VV & 0.52 & 0.21 & 0.11 & 0.17 & \(-\)**0.66** & 0.05 \\ (VV – VH)/(VV + VH) & \(-\)0.53 & \(-\)0.21 & \(-\)0.11 & \(-\)0.18 & 0.65 & \(-\)0.05 \\ Intensity Arith. Oper.1 & & & & & & \\ VH – VV & 0.77 & \(-\)0.21 & \(-\)0.14 & 0.20 & 0.63 & \(-\)0.03 \\ VV + VH & **0.82** & \(-\)0.18 & 0.19 & \(-\)0.18 & 0.61 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coefficient of determination (R2) between crop height and Sentinel-1 SAR parameters.
## 2 Related Work  
### Traditional Methods  
The traditional methods are also referred to as ML methods. They are the traditional techniques for image processing. The traditional techniques are broadly classified as statistical-based, structural-based, spectral/filter-based, and model-based. The statistical technique is based on the pixel value distribution of the given images. The methods included in this technique are thresholding [6], gray-level [7], co-occurrence matrix [8], histogram of oriented gradient (HOG) [9], and local binary pattern (LBP) [10]. The structural-based technique is based on detecting the edges and skeleton of the images. This method includes the following techniques: edge-based [11], skeleton-based [12], and morphological-based [13]. The spectral/filter-based detection techniques are based on regions, texture and edges. Filtering is a process in which filter algorithms extract specific features during the transformation process. Several approaches are classified into spatial domain, frequency domain and spatial-frequency domain. The frequency domain methods, such as Fourier transform [14], help to eliminate the noise from an image and then process it. The shortcomings of the Fourier transform are determined by the Gabor filter [15], which provides an optimal location of defects in spatial and spatial-frequency domains. The wavelet transform [16] has a greater ability than the Gabor filter for defect detection. It detects the defect location horizontally or vertically and diagonally through wavelets, which are small waves of differing frequency in a limited time period. The model-based techniques are the combination of certain attributes of the aforementioned approaches. The models associated with this technique are the fractal model [17], Markov random field [18] and autoregressive model [19]. The fractal models provide information regarding irregular texture surfaces. The Markov random model is a combination of the above two approaches. It has performed texture analysis defect detection on fabrics. Lastly, the autoregressive model defines the textural features based on pixel linear dependency. The ML methods have two-step processes, which include feature extraction and classification. The above approaches are challenging for the real world due to several issues, such as the illumination of the surface, noise, background factor and environmental factor. Due to this, every time, we have to change the parameters according to the concern raised, which makes it difficult to adapt to real-time applications.
## 2 Materials and Methods
### Evaluation of Energy Indices  
The energy ratio (ER), energy productivity (EP), specific energy (SE), and neat energy (NEG) were calculated by Equations (2)-(5) employing the energy-equivalent inputs and outputs (Table 1) [29].  
\[\mathrm{ER}=\mathrm{E}_{\mathrm{out}}\ \mathrm{E}_{\mathrm{in}} \tag{2}\]  
\[\mathrm{EP}=\mathrm{Y}/\mathrm{E}_{\mathrm{in}} \tag{3}\]  
\[\mathrm{SE}=\mathrm{E}_{\mathrm{in}}/\mathrm{Y} \tag{4}\]  
\[\mathrm{NEG}=\mathrm{E}_{\mathrm{out}}-\mathrm{E}_{\mathrm{in}} \tag{5}\]  
In the above equations, \(\mathrm{E}_{\mathrm{out}}\) represents the output energy (MJ/ha), \(\mathrm{E}_{\mathrm{in}}\) (MJ/ha), and Y (kg/ha) represent the input energy and product yield, respectively. ER is a dimensionless value, while EP, SE, and NEG were reported in kg/ha, MJ/kg, and MJ/ha, respectively.  
Figure 1: The framework of the life cycle assessment (LCA).  
The required energy for agricultural activities can be classified into direct and indirect groups. In this research, direct energy includes the labor force, electricity, and diesel fuel, while seeds, fertilizers, pesticides, and machines are classified as indirect energies. Energy can also be categorized into two types (renewable and nonrenewable). While diesel, fertilizers, pesticides, electricity, and agricultural machines are nonrenewable energies, the human workforce and seeds can be regarded as renewable energies [29].
# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 3 Results
### Types of Sensors for Monitoring Natural Hazards  
As previously mentioned, sensors play a crucial role in data collection and monitoring of key variables in EWSs. Their ability to provide real-time data is essential for the timely detection and assessment of potential natural hazards, enabling authorities to take proactive measures to mitigate risks and minimise damage.  
Based on Table 3, which identifies the manifestations of natural hazards, an in-depth sensor analysis has been conducted to determine the types of sensors commonly used for data collection on each specific parameter.  
By evaluating the most effective sensor technologies for different hazard types, it is possible to enhance the efficiency and reliability of EWSs, ensuring that critical information is available for decision-makers in disaster management and emergency response. Types of sensors for different manifestations of natural hazards is presented in Table 4.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Natural Hazard** & **Parameter** & **Types of Sensors** \\ \hline \multirow{3}{*}{Floods} & \multirow{3}{*}{Water level} & Pressure, ultrasonic, radar, optical fibre, microwave–photonic, inductive, LiDaR, infrared, capacitive, resistive Photoelectric, thermo-resistive, optofluidic, pressure, ultrasonic, piezoelectric, capacitive, microwave, infrared, fibre optic Resistive, capacitive, dielectric, impedance, time domain reflectometry, frequency domain, standing wave ratio, neutron probe, weight, optical fibre \\  & & \\ \hline \end{tabular}
\end{table}
Table 4: Types of sensors applicable for measuring natural hazard manifestations [74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132].  
Figure 2: Flow chart of early warning systems.
## 2 Mathematical Model of the Horizontal System
### Modeling of Horizontal Thrust-Current Dynamics
If the current \(i_{c2}\) in coil Co2 flows clockwise, it is negative, ensuring that the horizontal control coil Co2 generates a backward thrust \(F_{t2}\). Then, the following is true:  
\[\left\{ \begin{array}{l}i_{cr}=i_{c1}\;i_{c1}\geq 0,i_{c2}=0\\ i_{cr}=i_{c2}\;i_{c2}<0,i_{c1}=0\end{array} \right. \tag{3}\]  
When the MRC accelerates forward, \(i_{c2}\) is zero, and at this moment, \(i_{cr}=i_{c1}\). When the MRC accelerates backward, \(i_{c1}\) is zero, and \(i_{cr}=i_{c2}\). Similarly, the thrust on the left-hand side of the MRC is as follows:  
\[F_{lt}=F_{t3}-F_{t4} \tag{4}\]  
According to the Ampere force calculation formula [2], the formula for calculating the left-hand side thrust \(F_{lt}\) is as follows:  
\[F_{lt}=nB_{t}Li_{cl} \tag{5}\]  
Figure 2: Schematic diagram of the horizontal driving force. In this formula, \(n\) is the sum of the turns of the left-front horizontal control coil and the left-rear horizontal control coil, with a total of 30 turns. \(B_{l}\) is the sum of the left-hand side air-gap magnetic fields. \(L\) is the length of a single-turn horizontal control coil in the air-gap magnetic field. \(i_{\rm cl}\) is the current of the horizontal control coils on the left-hand side of the MRC, composed of the current \(i_{\rm c3}\) of the left-front horizontal control coil Co3 and the current \(i_{\rm c4}\) of the left-rear horizontal control coil Co4. \(i_{\rm c3}\) and \(i_{\rm c4}\) flow in opposite directions. When looking at the two horizontal control coils Co3 and Co4 on the left-hand side from the front side of the MAR system, if the current \(i_{\rm c3}\) in coil Co3 flows counter-clockwise, it is positive, enabling the horizontal control coil Co3 to generate a forward thrust. If the current \(i_{\rm c4}\) in coil Co4 flows clockwise, it is negative, allowing the horizontal control coil Co4 to generate a backward thrust. Then, the following is true:  
\[\left\{ \begin{array}{l}i_{cl}=i_{\rm c3}\;i_{\rm c3}\geq 0,\;i_{\rm c4}=0\\ i_{cl}=i_{\rm c4}\;i_{\rm c4}<0,\;i_{\rm c3}=0\end{array} \right. \tag{6}\]  
When the MRC accelerates forward, \(i_{\rm c4}\) is zero, and \(i_{\rm d}=i_{\rm c3}\). When the MRC accelerates backward, \(i_{\rm c3}\) is zero, and \(i_{\rm d}=i_{\rm c4}\). As can be seen from Figure 1, the horizontal control coils on the left-hand side and the right-hand side are positioned far apart, and the magnetic field interference between them can be neglected. Thus, the thrusts at both ends of the MRC are not coupled.
## 2 System Architecture Framework
### Backend Nonlinear Optimization  
The backend optimization module employs classical local and global BA (Bundle Adjustment) optimization methods to reduce accumulated errors and improve localization accuracy. Local optimization uses a sliding window strategy to fine-tune keyframe poses and map point coordinates within the regional map. Global optimization combines IMU residual information to comprehensively optimize visual reprojection errors and inertial residuals, ensuring map consistency at a worldwide level. This optimization process effectively reduces error accumulation caused by unstable visual information in mining environments, providing a reliable foundation for dense mapping and global map stitching.  
Figure 1: System framework diagram. The red box in the data input section represents the sparse textured slope.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Communication Network Links for Telemedicine
#### 4.1.1 Point-to-Point Links  
Through the use of point-to-point connections and high-speed internet, small remote health centers can be connected to larger health facilities. By utilizing this specific type of telemedicine link, medical care can be remotely outsourced to specialists located in other locations, and connected referrals can be made to these specialists. This is particularly useful for medical facilities that are either understaffed or have a smaller patient population. Mobile patients have the opportunity to establish contact with specialists for telec consultations using this connection. Similarly, aged patients have the option of receiving palliative treatment in the comfort of their own homes while having their current health state remotely monitored. For telepsychiatry, teleradiology, and other urgent care services, point-to-point links are the norm. Figure 8 shows a typical network topology for point-to-point telemedicine connections.
###### Abstract  
The wear rate and dissipation energy during tension-torsion cyclic loading of steel wires with fretting contact in different environmental media were explored in this study. Hysteresis loops of tangential force versus displacement amplitude (_Ft-D_) and torque versus torsion angle (_T-O_), and their dissipation energies were obtained employing the self-made test rig. Morphologies of wear scars of steel wires were observed employing the white light interference surface morphology. The quantitative demonstration of the coefficient of cyclic wear of steel wire was carried out combining polynomial fitting, reconstruction of three-dimensional geometric model of wear scar and Archard's equation. The results show that _Ft-D_ curves reveal both decreases of the relative slip and dissipation energy in the order: corrosive media, deionized water and air. Increases of contact load and crossing angle caused overall decreases in the relative slip and dissipation energy, while the relative slip and dissipation energy both increased with increasing torsion angle. _T-O_ curves indicated the largest and smallest dissipation energies in cases of acid solution and deionized water, respectively. Increases of contact load, crossing angle and torsion angle caused increases in relative slip and dissipation energy due to cyclic torsional loading with fretting contact. The wear coefficient in cases of distinct environmental media decreased in this order: air, corrosive media and deionized water. Increases of the contact load, torsion angle and crossing angle all induced increases in the wear coefficient.  
steel wire; tension-torsion cyclic loading; wear rate; dissipation energy; quantitative demonstration
# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
## 1 Introduction
In [27], the difference between the estimated value and the measured actual value of the current was calculated to form a criterion. The test results showed that these methods performed well. However, the researchers did not consider the effect of measurement noise. In fact, measurement noise reduces the reliability of these methods. In [28], the magnitude of the positive-sequence current and its phase difference with the positive-sequence voltage were used to discriminate between faults and power swings. However, due to power swings, accurate phase measurements cannot be obtained, which may lead to misjudgment. In [29], the covariance of current signals was calculated to discriminate between power swings and faults. Although the results of different tests indicated that the method proposed in [29] performed well, its reliability and operational speed are limited by communication, as it needs current information for both sides. In [30], the application of Principal Component Analysis (PCA) for distinguishing between line faults and power swings was explored. Based on different fault and power swing cases, PCA was used to extract features and form criteria for classifying faults and power swings, thereby achieving differentiation between faults and power swings. However, the method proposed in [30] required a large number of fault and power swing cases. A novel method, based on the characteristics of three-phase voltage and current, that can accomplish the PSB and PSD tasks of a PSBU is proposed in this study. Given the ability of PCA to simplify data complexity while retaining key features, PCA is utilized to process three-phase current and voltage, thereby enabling a simpler and more intuitive extraction of current and voltage characteristics under different scenarios to form corresponding criteria. For the PSB function, PCA is used to reduce the dimensionality of three-phase voltage, and an index \(C_{\text{avr}}\) is proposed, based on the processed voltage data, to distinguish power swings and faults. For the PSD function, three-phase voltage and current data are processed by using PCA in the same way. Based on the processed voltage and current data, the index \(P_{\text{UI}}\) is proposed to detect a fault occurring during a power swing. In contrast to the method described in [30], which also employs PCA, the method proposed here does not necessitate extensive cases. The performance of the proposed methods for PSB and PSD functions are tested by using the simulation data obtained from test systems built in PSCAD/EMTDC. The test results indicate that the proposed method can distinguish power swings and faults reliably and quickly detect a fault during a power swing, being minimally impacted by noise. The remainder of the paper is organized as follows: PCA is introduced in Section 2. The proposed method is introduced in Section 3, including its basic principles and criteria and the flow charts of PSB and PSD. Simulation tests of the proposed method are shown in Section 4. Finally, conclusions are drawn in Section 5.