esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Dynamic Fog Service Placement Approach
## 7 Conclusions  
A new optimization method was introduced for an optimized service placement. A two-stage method uses the IMOPSO algorithm to find a Pareto optimal set of potential service placements. Meanwhile, the AHP algorithm makes a final choice among the potential placements using a judgement matrix of priorities. It gives an assessment based on the Eigen vector in relation to the considered criteria.  
The major novelty of this research is a new service placement orchestration method as a multi-agent system. Multi-agent systems are vulnerable to network attacks, and they may lack an integrated monitoring tool. Security issues were addressed by integrating a JADE-S add-on, which allows us to sign and encrypt agent communication messages. Monitoring classes were developed to track resources. In addition to this, there is no single central control unit to avoid a single point of failure. Decisions are made in a distributed and dynamic approach. Decisions can be made by a fog node that is close enough to a user and the outcome is therefore synchronized with other fog nodes. Distributed decision making contributes to resilience.  
Multiple tests were performed to determine prototype usability including dependency on the inertia weight and the number of criteria, stress tests, security package performance impact, and power consumption. These tests witness the usability of the method in field conditions with a security package with no additional obvious delay. Power needs are low and the maintenance of the system is cheap. The main limitation is a response time with low-resource fog nodes, but it is still applicable for a few fog node infrastructures. The infrastructure with high-resource fog nodes can be a few times larger.  
Conceptualization, N.S. and A.V.; methodology, N.S. and A.V.; software, N.S.; validation, N.S.; formal analysis, N.S.; investigation, N.S.; resources, A.V.; data curation, N.S.; writing--original draft preparation, N.S.; writing--review and editing, N.S.; visualization, N.S.; supervision, A.V.; project administration, A.V.; funding acquisition, A.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
[]Data Availability Statement: Dataset available on request from the authors.  
[]Conflicts of Interest: The authors declare no conflict of interest.
## 4 Experimental Verification
### Visualization of Training Set Classification Process  
In this section, the \(t\)-distributed stochastic neighbor embedding (T-SNE) technique is used to explore the classification process of small sample training data in the network model. From Table 5, it can be seen that the numbers 0 to 3 represent the types of bearing faults, respectively.  
Taking the training set of Working Condition 1 as an example, one-tenth of the data volume of the training set in Table 5 is the network input data, and the T-SNE dimensionality reduction results of some network layers are shown in Figure 16.  
It can be seen from Figure 16 that (1) the four health states of the bearing have been well distinguished after the FFT signal feature extraction of the training data samples, (2) the further feature extraction of the FFT data by the multi-scale first layer large convolutional kernel of the convolutional neural network and the subsequent multi-layer small convolutional kernel both further enhance the linear separability of the model for the fault features, and (3) the recurrent neural network GRU further processes the current sequence information, and the proposed network model structure improves the generalization capability of the network and the bearing fault diagnosis capability in complex situations compared with the WDCNN network.  
In conclusion, the combination of FFT signal processing and the improved network model proposed in this paper has a strong ability to extract indistinguishable feature information, simplify the bearing fault diagnosis problem, and improve the bearing fault diagnosis accuracy.  
Figure 16: Visualization of model training set classification process: (**a**) Initial training set; (**b**) FFT processing; (**c**) multi-scale wide convolution; (**d**) fourth convolution; (**e**) second GRU; (**f**) SoftMax; (**g**) WDCNN + Ori. (first-wide convolution); (**h**) WDCNN + Ori. (SoftMax); (**i**) WDCNN + FFT (SoftMax).
## Materials and Methods  
### Materials and Fabrication of Unit Tubes  
The investigated THAs were fabricated from high pure elemental powders of 93.10 wt% W, 4.83 wt% Ni, and 2.07 wt% Fe. The raw powders were mechanically mixed to form a homogeneous blend. The blended tungsten alloy powders were ball-milled for 5 h in a jar filled with 1.0-1.5-inch balls in an argon environment to break up the agglomerates, provide intimate mixing, and uniformly disperse the elemental powders. For the homogeneous distribution, the blended powders were crushed again using a Fitz mill. Then, the powders were subjected to cold isostatic pressing (CIP) in order to obtain denser green compacts [23; 24; 25; 26]. The assembly was pressurized hydrostatically in a chamber at 195 MPa for 20 s.  
In this study, unit tubes have been fabricated by diffusion bonding of semicircular tubes shown in Figure 1a. Semicircular tubes were produced by liquid phase sintering of THA green compacts having semicircular shapes [27]. The sintered semicircular tubes were subsequently joined by solid state diffusion bonding at 1427 \({}^{\circ}\)C for 3 h, as shown in Figure 1b [28]. The outer diameter, wall thickness, and length of the unit tube are 205.0 mm, 20.0 mm, and 208 mm, respectively. Then, diffusion bonded tubes were machined to unit tubes having target shapes, as shown in Figure 1c. The process sequence for fabricating the unit tube is summarized in Figure 2.
## Case Study
### Results and Analysis of LA Bidding Strategies
#### 6.3.3 Optimal Bidding Strategy  
The LA selects the strategy with the largest net income among all the bidding strategies as the optimal decision of bidding. The optimal bidding information of the two periods is shown in Table 6. During the period from 1:00 to 6:00, the optimal quantity of load increase of residents is 5.75 MW, and the net income of the LA is 2557.48 yuan. At this time, the average willingness of residents to respond is 0.9, which is within the range of response deviation margin. During the period from 16:00 to 20:00, the optimal quantity of load decrease of residents is 4.52 MW, and the net income of the LA is 3006.91 yuan. At this time, the average willingness of residents to respond is 0.9, which is also within the range of response deviation margin.  
Two scenarios are analyzed. One is considering residents' response willingness and simulating that some residents will participate in the next day's DR according to Table 6. The other is simulating that all residents will participate in DR without considering residents' response willingness; the conventional bidding strategies are shown in Table 7. During the period from 1:00 to 6:00, the conventional quantity of load increase of residents is 12.26 MW, and the net income of the LA is \(-\)59.25 yuan. At this time, the average willingness of residents to respond is 0.81, which is not within the range of response deviation margin. As a result, the LA is fined, which results in a negative net profit for the LA. During the period from 16:00 to 20:00, the net profit of conventional bidding strategy is far less than the net profit of optimal bidding strategy.  
The changes of the power curve after residents participate in DR under two scenarios are shown in Figure 18. Assuming that all residents participate in DR, the load curve changes the most, but the average response willingness of the residents is 0.74, which is not within the response deviation margin. Therefore, the risk of under-response or over-response in the next day is greater. On the one hand, this will reduce the revenue of the LA; on the other hand, it is not conducive to the power company to ensure the balance of power supply and load demand. Therefore, the LA estimates the residents'  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DR Period** & 1:00–6:00 & 16:00–20:00 \\
**Net profit (yuan)** & 2557.48 & 3006.91 \\
**Incentive unit price (yuan/kWh)** & 0.89 & 1.33 \\
**Bidding unit price (yuan/kWh)** & 1.34 & 2.00 \\
**Response quantity (MWh)** & 5.75 & 4.52 \\
**Average willingness** & 0.90 & 0.90 \\
**Compensation value caused for substandard response(yuan)** & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal bidding strategies considering the willingness.  
Figure 17: Different bidding quantity and compensation unit price in decreasing load period without considering the willingness.  
willingsness to respond, and then selects the residents with high willingness to response, which can reduce the risk of compensation the next day and achieve the greatest benefit.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 2 Problem Formulation
### Multiobjective Bus Lane Planning Model  
From the above discussion on the relevant constraints and objectives, we present a multiobjective bus lane planning model. For a given bus trajectory set \(Tr\), road network \(G=(V,\,E)\), and total construction budget \(B\), finding the desired bus lane requires the identification of a set of edges \(E^{\prime}\in E\) that have the maximum road utilization score and bus punctuality rate score. Mathematically, this problem can be expressed as a multiobjective planning model, as shown in Equation (10).  
\[\left\{\begin{array}{ccccc}Z:&\max\limits_{tr_{j}\cap E^{\prime}\
eq\varnothing }\sum\limits_{s\in tr_{j}}\pi^{\frac{L_{s}}{L_{min}}}\times\frac{L_{s}}{L_{ min}}&&(Z_{1})\\ &\max\sum\limits_{i\in E^{\prime}}R_{i},R_{i}=P\big{\{}\big{|}T_{i}-t_{ij} \big{|}>S_{i}\big{\}}&&(Z_{2})\\ s.t.:&\forall i\in E^{\prime},t_{i}\in T&&(C_{1})\\ &\forall i\in E^{\prime},f_{i}\geq F&&(C_{2})\\ &E_{c}\geq k_{c}&&(C_{3})\\ &E_{cnon}\geq k_{cnon}&&(C_{4})\\ &\sum\limits_{i\in E^{\prime}}c_{i}\leq B&&(C_{5})\\ \end{array}\right. \tag{10}\]  
Here, the data on road and vehicle parameters required by the proposed model, such as the traffic flow, number of buses in the lane, bus frequency, and road travel time, can be obtained via the GPS trajectory. By this way, it is even possible to specify traffic parameters with high spatiotemporal resolutions. For example, the GPS trajectories can be used to directly and efficiently calculate the traffic flows at the lane scale [14,30]. Moreover, compared with the traditional method of acquiring traffic parameters by historical experience, monitoring points, or numerical simulations, the use of GPS data can effectively cover all the road networks in a city [30,31]
## 2 Blind Source Separation Techniques
### Complexity Pursuit Algorithm (CP)
#### 2.3.3 Extracting a Single Signal
Once the modal responses \(\mathbf{q}(t)\) is extracted using CP, the frequency and damping ratio of each mode can be computed by applying Fourier transform and the logarithmic decrement technique, respectively.
## 3 Verification of the Proposed Method
### Calibration of Energy Partition Coefficient  
[PERSON] et al. [35] was conducted on side milling. For comparison, the complex pattern of trochoidal tool path described in Section 2.3 is not used in the verification. Instead, the simple pattern of side milling is given here and adopted. For a side milling with the cutting parameters listed in Table 2, the time of the cutter rotating one revolution is 0.0458 s, for the first interval \(t\in[0.0129,\,0.0229]\)s, the cutting edge cuts the material and the un-deformed chip thickness is \(h(t)=0.105\cdot\sin(7860.26^{\circ}\cdot t)\) in down milling. For the rest intervals \(t\in[0,\,0.0129]\)s, and \(t\in[0.0229,\,0.0458]\)s, the cutting edge leaves the material, \(h(t)=0\), until the next revolution.  
Using the proposed method mentioned in Sections 2.1 and 2.2, the temperature variation of a location inside the cutting edge is calculated. The coordinate of the location is (0.1, 0.6, 1.3), which is the same location of the bottom of the blind hole in [PERSON]'s research. Moreover, the temperature is measured here. The calculated temperatures in a period of cutter's two revolutions are plotted in Figure 8. In the calculation, three energy partition coefficients \(R_{1}\) are employed, which are 40%, 50%, and 60%. The maximum and minimum temperatures calculated based on \(R_{1}=[40\%,\,50\%,\,60\%]\) are (324.8 \({}^{\circ}\)C, 139.3 \({}^{\circ}\)C), (399.7 \({}^{\circ}\)C, 169 \({}^{\circ}\)C), and (474.6 \({}^{\circ}\)C, 197.8 \({}^{\circ}\)C), respectively.  
The temperature history of simulations and experiments presented by [PERSON] et al. [35] are shown in Figure 9a,b. Apparently, they matched well, and their maximum and minimum temperatures are (407.9 \({}^{\circ}\)C, 166.1 \({}^{\circ}\)C). Evidently, when \(R_{1}=40\%\) or \(R_{1}=60\%\), the temperature differences between the calculated values and the reference are larger than 10%. While \(R_{1}=50\%\), all the differences between the predicted and measured temperature values are less than 5%. Thus, \(R_{1}=50\%\) is an appropriate energy partition efficient under the specification in Section 3.1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(V\) & \(f_{z}\) & \(a_{e}\) & \(a_{p}\) & \(n\) & \(R\) \\
**m/min** & **mm/z** & **mm** & **mm** & **rev/min** & **mm** \\ \hline
214 & 0.105 & 21 & 2 & 1310 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cutting parameters adopted in [PERSON] et al. [35].  
Figure 8: Temperature histories calculated by the proposed method at \((x,y,z)=(0.1,0.6,1.3)\).
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 6 Conclusions  
RMCSat is a 3U CubeSat mission designed to assess the feasibility of obtaining space-based F10.7 solar flux measurements from LEO. As a technology demonstrator, it will enable the real-world assessment of the utility of space-based miniaturized solar microwave radiometry; however, it will also provide valuable dual-frequency comparative measurements to the existing F10.7 dataset. It does so by monitoring three microwave frequencies, 2695 MHz, 2785 MHz, 2815 MHz using the same calibration standard to allow for signal comparison and true scaling of measurements. Measurements of F10.7 are captured by two payload antennas both in and outside of the DRAO operation window with sufficient accuracy to complement existing operations and increase temporal and geographical coverage.  
The mission concept and preliminary design of the satellite described here demonstrate the feasibility to proceed to the next phase of development. The satellite subsystems and system budgets are viable. Elements of the next design phase include a more detailed assessment of the payload antennas and an investigation of achievable launch orbits within the desired mission timeframe. As the development of RMCSat proceeds, it continues to provide valuable hands-on space mission design, operations, and solar monitoring experience to both graduate and undergraduate university students at RMC.  
[PERSON] carried out the initial research. [PERSON] refined the mission parameters and wrote the paper. [PERSON] and [PERSON] supervised the research. All authors have read and agreed to the published version of the manuscript.  
Canadian Defence Academic Research Program.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 3 Results and Discussion
### Comparison of Spectral and Texture Information
ENT values for different classes appear in Figure 8. ASM, which measures uniformity of contrast, also changed with the four classes. This visual inspection of texture features further confirmed the usefulness of texture information. When considering the spatial resolution of the UAV image used for crop classification (i.e., 25 cm), GK3 and GK31 texture information represents 0.75 m and 7.75 m on the ground, respectively. The GK31 texture features are likely to represent the serial line patterns of crop cultivation well, consequently leading to superior OA. However, this is the particular result in the study area. If the spatial resolution of input images and the crop types change, the optimal kernel size of GLCM should be determined by considering spatial resolution, as well as cultivation patterns and crop characteristics such as size and shape.
## 4 Results and Discussion
### Fresh Concrete Properties  
#### 4.2.1 Slump  
The effect of glass sand substitution on the workability of fresh concrete is followed by measures of slump. Figure 8 shows a change in slump with glass substitutions for the sand component. It is shown that generally, there is a drop in slump with glass sand proportion in the mix. The slump dropped from 75 mm to well below 60 mm for mixes containing more than 15% substitutions reaching below 50 mm for 100% replacement of glass for natural sand. It could be stated that the workability would be reduced slightly for modest substitutions up to 30%, representing about a 22% loss in workability. The role of glass components in concrete mixes has generally been reported [17] to decrease workability. Although the glass particle sizes fall within the acceptable size limit required by standards, the faceted nature of the glass particles and the irregular shapes would provide more resistance to the dynamic response of wet concrete mix to load and thus affect its flowability. This would explain the significant drop in slump for mixes with a higher proportion of sand substitutions.
## 2 Analytical Models of Breakthrough Curves  
When conducting dynamic adsorption experiments in fixed-bed columns, the breakthrough curve is generated as a response. This curve demonstrates the interaction between kinetic and thermodynamic effects of the solute with the adsorbent along the column as the gas or liquid flows. Throughout the process, factors such as adsorption capacity, selectivity, adsorption rate, initial concentration, and fluid velocity can significantly influence the position and shape of the breakthrough curve [11].  
Figure 1 shows a schematic of the fixed-bed adsorption process, where the adsorbate (gas), with initial concentration C\({}_{0}\), transfers from the solution to the adsorbent surface. Initially, the concentration at the outlet is zero, but it gradually increases as the sites become saturated. The breakthrough curve, which describes this process, is obtained by relating the concentration of the component at the inlet and outlet of the column (C/C\({}_{0}\)) over time.  
The region of highest component removal, known as the mass transfer zone (MTZ), can vary with temperature, component concentration, flow rate, and mass transfer rates [18,19].  
The mass transfer region is typically limited by values between 0.95 and 0.05 of the relative concentration (C/C\({}_{0}\)). Breakthrough (t\({}_{\rm r}\)) occurs when the relative concentration reaches 0.05. If adsorption continues beyond this point, the outlet concentration increases until it reaches the inlet concentration (C/C\({}_{0}\) = 1.0), indicating bed saturation (t\({}_{\rm s}\)) [18].  
Modeling these curves is essential for predicting variables and determining optimal conditions in fixed-bed columns, whether through a theoretical model using mass transfer coefficients and diffusivity or an analytical approach involving experiments with columns of varying lengths and parameters [20,21]. Thus, for fitting to experimental data, the analytical breakthrough curve models of [PERSON], [PERSON], [PERSON], and [PERSON] were used.
## 2 Materials and Methods
### Fibers and Data Acquisition
#### 2.1.1 Category 1: Ge-Doped Fiber (UV)  
Often referred to as standard gratings, Category 1 FBGs are formed in a germanium-doped (Ge-doped) silica fiber using an inscription laser operating in the UV band, typically at \(\sim\)248 nm. The Type I inscription process exercises the germanium's reaction to UV radiation thereby increasing the refractive index of the doped silica in the regions where it is exposed to the incident laser. UV gratings are common in the fiber optic sensing industry. UV gratings have a typical operating temperature range \(<\)200 \({}^{\circ}\)C, above which the modulation of refractive index in the laser-exposed regions decreases, thereby erasing the FBG. In addition, previous research with UV gratings in high radiation environments indicated a potential sensitivity of the FBG wavelength to radiation. Research conducted by [PERSON] et al. [16] suggested that ionizing radiation interacted with the Ge-dopant and altered the reflected FBG wavelength and the FBG strength as if the UV inscription process were continuing. The radiation caused a change to the refractive index of the entire Ge-doped fiber and therefore attenuated the modulation of the original grating. This saturated the grating and thereby influenced its wavelength and reflectivity.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 4 The Goals and Challenges of Middleware Architectures for WSNs
### Managing Limited Battery Power  
With smaller, more compact sensors, the available battery power is always limited. The systems are designed to manage limited power by designing efficient processes and capabilities of the sensors. Mechanisms to ensure efficient power consumption are necessary for advanced wireless sensor networks.