esa-sceva/satcom-chunk-collection huggingface dataset:
# Microstructural Changes Influencing the Magnetoresistive Behavior of Bulk Nanocrystalline Materials
## 4 Discussion  
### Is the Change in Resistance a Magnetic Effect?  
Indeed, there is an improved GMR-effect upon annealing in Cu-Co. The RT relative resistance at high magnetic fields--in relation to the resistance at zero field--is smallest for the Cu\({}_{55}\)Co\({}_{45}\) annealed at 300 \({}^{\circ}\)C. The same tendency holds true for smaller Co contents. Please note, the higher GMR-effect found for the Cu-Co sample of medium composition is due to the achievable magnetic field not being high enough to saturate material of low ferromagnetic content.  
For the determination of GMR, the change in resistance within the magnetic field is set in relation to the resistance at zero magnetic field (Equation (1)). Thus, the improved GMR-effect could also be due to a lower overall resistivity upon annealing. [PERSON] et al. [37] tried to disentangle magnetic contributions and contributions from microstructural changes upon annealing (e.g., from stress relief, recovery processes). This was done by employing Equation (2)  
\[\frac{\rho_{mag}(H)}{\rho_{0}}=\frac{\rho(H)-\rho(H=0)}{\rho(H=0)}\bigg{(}1+ \frac{\Delta\rho_{T}}{\rho_{0}}\bigg{)} \tag{2}\]  
uncovering the magnetic contribution to the GMR. Therein, \(\rho_{0}\) is the resistivity before annealing, the first factor on the right side the typical form for deriving GMR (see Equation (1)) and the second factor containing the relative change in resistivity upon thermal treatment at the temperature \(T\). Application of Equation (2) on the experimental data yields the same behavior for the magnetic contribution as it was found for the GMR itself. Namely an increase with increasing annealing temperature, followed by a drop in \(\rho_{mag}/\rho_{0}\) above 300 \({}^{\circ}\)C. Thus, as it was also judged in [37], this behavior can mainly be associated with a magnetic effect.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 6 Investigating Reinforcement Learning-Based Routing Protocols  
In this section, we review the state-of-the-art RL-based routing methods based on the suggested categorization in this paper.
## 2 Materials and Methods
### Histological Processing and Histomorphometric Quantification  
After mechanical testing, all samples were fixed in 10% formalin solution (Sigma-Aldrich, Burlington, VT, USA) at 4 \({}^{\circ}\)C for 48 h, stepwise dehydrated using ethanol and xylene, and embedded in paraffin wax. Wax-embedded samples were sectioned along the transverse axis using a microtome (Leica Biosystems, Wetzlar, Germany) into 7 \(\upmu\)m thick slices. Slides were stained with [PERSON]'s elastic stain and imaged using an Aperio CS2 microscope installed with ImageJScope software (V12.3.). QuPath (V0.2.3) image analysis software was used to quantify elastin content using a colour deconvolution approach comprising three distinct steps [52]. First, the RGB values defining stain colour (referred to as the stain vector) were determined from a subset of images. Next, a thresholder was defined to distinguish the tissue from the background in images. Lastly, using the stain vector, a separate thresholder was applied to identify the percentage surface area fraction of the tissue area stained for elastin (Equation (4)).  
\[\%\text{Aerafraction}=\frac{area\ stained\ for\ elastin}{tissue\ area}*100 \tag{4}\]Protocols for stain vector determination and tissue and stain thresholding were standardised for all measurements. For elastin quantification of individual rings, each ring was sectioned into four slices, each of which was assessed at three ROIs, and as such, the individual values for each ring represent the average of 12 individual measurements. Each ROI encompassed the full wall thickness.
# Survey for Soil Sensing with IOT and Traditional Systems
## 4 Inference Method and Evaluation Metrics
### Evaluation Metrics  
To assess the performance of models predicting the target element, two prevalent metrics are typically used to gauge the relationship between predicted values and the actual ground truth (dependent variable): the Correlation Coefficient (\(R\)) and the Coefficient of Determination (\(R^{2}\)). The equations for \(R\) and \(R^{2}\) are provided in Equations (19) and (20), respectively [85].  
\[r=\frac{\sum_{k=1}^{m}(y^{\prime}_{k}-\bar{y^{\prime}})(y_{k}-\bar{y})}{\sqrt {\sum_{k=1}^{m}(y^{\prime}_{k}-\bar{y^{\prime}})^{2}\sum_{k=1}^{m}(y_{k}-\bar{ y})^{2}}} \tag{19}\]  
\[R^{2}=1-\frac{\sum_{k=1}^{m}(y_{k}-y^{\prime}_{k})^{2}}{\sum_{k=1}^{m}(y_{k}- \bar{y})^{2}} \tag{20}\]  
where \(y^{\prime}_{k}\) is the predicted value of \(k\)th sample and \(y_{k}\) is the ground truth of the \(k\)th sample. \(m\) is the total number of experiment samples. \(\bar{y}\) and \(\bar{y^{\prime}}\) is the mean value of the ground truth and prediction. The correlation coefficient \(r\) ranges between \(-1\) and \(1\). The coefficient of determination \(R^{2}=1\) if the predicted value \(y^{\prime}_{k}\) exactly matches the observed value \(y_{k}\). A negative value of \(R^{2}\) suggests that the chosen model performs poorer than a simplistic model that merely predicts the mean of the observed values.  
Three vital metrics used to evaluate model performance are the root mean square error of calibration (\(RMSEC\)), root mean square error of prediction (\(RMSEP\)), and root mean square error of cross-validation (\(RMSECV\)). These indicators measure the prediction errorsof the model on the different data groups applied to the model. The formulas to compute \(RMSEC\), \(RMSEP\), and \(RMSECV\) are given below [86]:  
\[RMSEC =\sqrt{\frac{\sum_{l=1}^{L_{c}}(\hat{y}_{l}-y_{cl})^{2}}{L_{c}}} \tag{21}\] \[RMSEP =\sqrt{\frac{\sum_{l=1}^{L_{p}}(\hat{y}_{l}-y_{pl})^{2}}{L_{p}}}\] (22) \[RMSECV =\sqrt{\frac{\sum_{l=1}^{L_{cp}}(\hat{y}_{l}-y_{cvl})^{2}}{L_{cv}}} \tag{23}\]  
where \(l\) is the sample number, \(L_{c}\), \(L_{p}\), and \(L_{cv}\) are the total number of samples in the calibration, validation, and prediction groups. \(y_{cl}\), \(y_{pl}\), and \(y_{cvl}\) are the predicted concentration values of the target element from the validation group and prediction group. The selection of evaluation metrics can vary between studies based on factors such as sample size and specific research objectives. For instance, \(RMSECV\) is versatile and can be applied irrespective of the total sample count, whereas \(RMSEP\) is typically favored for studies with a larger dataset. As demonstrated in [33], they employed a combination of \(RMSEC\) and \(RMSECV\) for assessment. Conversely, [32] relied on \(RMSEC\) and \(RMSEP\). [35] adapts \(RMSEP\) and \(RMSECV\) during the evaluation. A comprehensive evaluation was observed in [36], where all three metrics were utilized, providing a well-rounded assessment of their system's stability.
## 2 Materials and Methods
### QBER Model
Polarization of signal coincides with orientation of effective modulation, phase difference equals zero: \(\mu\to 4\mu\),
2. Polarization of signal coincides with orientation of effective modulation, phase difference equals \(\pi\): \(\mu\to 0\),
3. Polarization of signal does not coincide with orientation of effective modulation, phase difference equals zero: \(\mu\to\mu(1+\sigma)^{2}\),
4. Polarization of signal does not coincide with orientation of effective modulation, phase difference equals \(\pi\): \(\mu\to\mu(1-\sigma)^{2}\). It is important to comment on the modulation of thermal light that appears in the fiber. We assume that there is no thermal light at the first modulation (\(t\approx 0\)). Thus, the second modulation only reallocates the energy of the thermal photons as if it is only the first modulation. In other words, the second modulation does not affect the mean value of thermal light photons in the spectrum. Taking into account all the previous reasoning, one may derive a final expression for QBER (that is the ratio of undesirable detection events when the phase difference equals \(\pi\) to the total amount of detection events; detection events rates are proportional to optical power of the signal in [PERSON] linear approximation [PERSON] (1961), that can be applied taking into account \(\mu<1\) and additioanl losses):  
\[Q(t)=\frac{P_{H^{\prime}}(t,0)+P_{V^{\prime}}\left(t,\mu(1-\sigma)^{2}\right) }{P_{H^{\prime}}(t,0)+P_{V^{\prime}}\left(t,\mu(1-\sigma)^{2}\right)+P_{H^{ \prime}}(t,4\mu)+P_{V^{\prime}}\left(t,\mu(1+\sigma)^{2}\right)} \tag{25}\]  
We also want to explicitly show the relation between time-dependent losses and OF-length-dependent losses:  
\[e^{-\frac{(\gamma_{H}+\gamma_{V})t}{2}}=10^{\frac{\alpha t}{10}} \tag{26}\]  
where \(\alpha\) is specific loss coefficient (dB/km), \(L=\frac{ct}{\beta}\) is the distance that optical signal passes in the OF, \(c\) is the speed of light, \(\beta\) is propagation constant. We want to define relative loss difference for two polarization modes for the purpose of accurate OF dichroism description:  
\[\Delta=\frac{\gamma_{H}-\gamma_{V}}{\gamma_{H}} \tag{27}\]  
Then we get:  
\[ \begin{array}{l}\gamma_{H}=\frac{-2\alpha c}{\beta(2-\Delta)10\,\mathrm{m}( 10)}\\ \gamma_{V}=\gamma_{H}\left(1-\Delta\right)\end{array}  \tag{28}\]
## 2 Materials and Methods
### System Implementation
#### 2.3.2 Feature Extraction  
The discrete wavelet transform was used to decompose the EEG signals into multiple levels. A Daubechies 10 mother wavelet was used to perform a 4-level wavelet decomposition. Each time window was split into four details (D1-D4) and one approximation (A4). Wavelet coefficients were extracted from component D4, which encompasses the frequency range of 7.813-15.625 Hz and includes the alpha rhythm frequency band (8-12 Hz). We then used the wavelet coefficients to calculate three statistical features: the mean of the absolute value, the standard deviation, and the average power of the wavelet coefficients. Since two channels were considered for this study, a total of 6 statistical features were used. This feature extraction method was introduced by [PERSON] and [PERSON] [47] and previously used by [PERSON] et al. [48] to classify alpha activity. Features were normalized using min-max scaling.
# Optimal Coordination of PV Active Power Curtailment and EVs Charging among Aggregators
###### Abstract  
With the growing adoption of electric vehicles (EVs) and residential photovoltaic (PV) systems around the world, the distribution system operators (DSOs) are facing several technical challenges on their network planning and operation, particularly on low-voltage grids. As the aggregators are intermediary actors at that system level, they are a promising figure to coordinate these devices in an aggregated manner to help to mitigate adverse effects like overloading of network assets. However, to do so, proper coordination techniques among these entities and the DSO should be developed to avoid further investments in new network assets. In this context, a centralised coordination strategy among aggregators at the DSO level is proposed. By employing a linear programming model, the optimal export limit of PV and charging profile for each aggregator is dictated by the DSO, maintaining the operational limits of the network assets. A case study on two aggregators with moderate and critical penetration levels was carried out. Results show that, by controlling the aggregated export limit of PV power and the aggregated charging rate of EVs, high penetration levels can be integrated into current networks with minor or no need for reinforcing network infrastructure.  
aggregator; active power curtailment; electric vehicles; linear programming; low voltage network; photovoltaic systems +
Footnote †: journal: Applied Sciences
## 2 Reliability Assessment Method Based on Fusion of Simulation and Test Data
### Reliability Assessment Method for Fusion of Simulation and Test Data
#### 2.2.2 Lifetime Reliability Prediction Method Based on Data Fusion  
The zero-failure data with sample sizes of \(n_{N}\), \(n_{S}\), and \(n_{O,i}(i=1,2,\cdots,m)\) were obtained from the test, the simulation, and m groups of previous test results, respectively. By applying the calculation method for the lower confidence limit described in Section 2.1, we obtained the lower confidence limits \(t_{N,RL}\), \(t_{S,RL}\), and \(t_{O,RI,i}(i=1,2,\cdots,m)\) of the logarithmic reliable life with a confidence level of \(\gamma\) and reliability of \(R\). The shape parameters corresponding to different \(t_{O,RI}\) values can be different. The predicted life fusion equations for the commonly used distributions in engineering are as follows.  
1. Normal distribution \[t_{T,RL}=\frac{n_{N}}{n_{T}}t_{N,RL}+\frac{n_{S}}{n_{T}}t_{S,RL}+\sum_{i=1}^{m} \frac{n_{O,i}}{n_{T}}t_{O,RIi}+(t_{T,R_{0}}-\frac{n_{N}}{n_{T}}t_{N,R_{0}}- \frac{n_{S}}{n_{T}}t_{S,R_{0}}-\sum_{i=1}^{m}\frac{n_{O,i}}{n_{T}}t_{O,R_{0}i })\hat{\sigma}\] (18) Here, \(n_{T}=n_{N}+n_{S}+\sum_{i=1}^{m}\ n_{o,i}\), \(t_{T,R_{0}}=\[PERSON]^{-1}\Big{[}(1-\gamma)^{1/n_{T}}\Big{]}\), \(\[PERSON]^{-1}\Big{[}(1-\gamma)^{1/n_{N}}\Big{]}\), \(t_{S,R_{0}}=\[PERSON]^{-1}\Big{[}(1-\gamma)^{1/n_{N}}\Big{]}\), and \(t_{O,R_{0}i}=\[PERSON]^{-1}\Big{[}(1-\gamma)^{1/n_{N}}\Big{]}\). As indicated by Equation (12), \(t_{T,RL}\) is a one-sided lower confidence limit for the logarithmically reliable life \(t_{R}\) with a confidence level of \(\gamma\), i.e., \(P\ (t_{R}\geq t_{T,RL})\geq\gamma\).
2. Weibull distribution \[t_{T,RL}=(t_{N,RL}^{\hat{k}}+t_{S,RL}^{\hat{k}}+\sum_{i=1}^{m}t_{O,RIi}^{\hat{ k}})^{1/\hat{k}}\] (19) According to Equation (17), \(t_{T,RL}\) is a one-sided lower confidence limit for the logarithmically reliable life \(t_{R}\) with a confidence level of \(\gamma\), i.e., \(P\ (t_{R}\geq t_{T,RL})\geq\gamma\).
# Cybersecurity Challenges in the Maritime Sector
## 5 Security and Safety Countermeasures
Finally, collaborative defence systems that implicate multiple stakeholders are being explored [16, 80]. These defence systems can participate in the identification and mitigation of potential cyberattacks at multiple levels. Detection and countermeasures taken for an attack on a single vessel can be communicated to other autonomous vessels in the fleet. Table 4 presents possible countermeasures and mitigation actions that can help to build resilience to external and internal security threats. \begin{table}
\begin{tabular}{l l} \hline \hline
**Systems** & **Mitigation Actions** \\ \hline \hline \multirow{4}{*}{Automatic Identification System (AIS)} & - All AIS information should be verified \\  & - Encryption of the VHF signals \\  & - Integrity of broadcast information should be monitored to ensure that position and identity are correct. \\  & - Equipment that broadcasts AIS signals should be secured, and unauthorised access should not be possible \\  & - Local navigation warnings should be considered if false AIS signals are being broadcast \\ \hline \multirow{4}{*}{Electronic Chart Display System (ECDIS)} & - ECDIS developers should look to adopt security development lifecycles \\  & - Regular documentation, monitoring, and patching of the ECDIS framework \\  & - ECDIS chart update should be monitored and registered, especially manual updates via CD or USB disc \\  & - All upgrade files should be scanned with antivirus software \\  & - The internal network to which ECDIS is linked should be examined to see whether the ECDIS system can be fully isolated or firewalled \\  & - Only approved staff should have physical access to ECDIS and its underlying components \\ \hline \multirow{2}{*}{GNSS and GPS} & - Device identification and authentication \\  & - Cryptographic protection \\  & - Protection of information at rest \\ \hline \multirow{3}{*}{Radar} & - Device identification and authentication \\  & - Cryptographic protection \\  & - Information system backup \\ \hline \multirow{3}{*}{Global Maritime Distress System (GMDSS)} & - Cryptographic protection. \\  & - Device identification and authentication \\ \cline{1-1}  & - Protection of information at rest \\ \cline{1-1}  & - Physical access control \\ \cline{1-1}  & - Contingency plan \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A summary of possible mitigation actions.
## 2 Materials and Methods
### Description of the Intelligent Irrigation Control System (IICS)
#### 2.3.2 Automatic Irrigation System  
The main control element of automatic irrigation system is the programmable logic controller (PLC). PLCs control irrigation according to the set irrigation conditions. The PLC controls the water meter and pulse solenoid valve of each plot. The PLC simultaneously measures the irrigation volume and monitors the state of the solenoid valve (on/off). Automatic irrigation system can monitor data and manage irrigation strategies, agricultural activities, user information, etc. Users can query real-time data, view variable historical data curves, view irrigation records and view irrigation details in the \"real-time monitoring\" module and add, delete, modify and query agricultural activities in the \"farm management\" module. Furthermore, users can manage user information in the \"system management\" module (Figure 4).  
* Real-time monitoring: Data analyses can be performed to view the historical data curve of selected variables within a day, two days interval, a week, a month or a custom time period.
* Irrigation strategy management: Irrigation strategies can be managed, and users can add, delete and modify the starting and ending strategies according to the real conditions. For example, the user can set multiple end conditions, and when any end condition is met, the valve will automatically close.
* View total irrigation records and irrigation details: Users can view the number of irrigation times, irrigation times and irrigation amounts in one day-, two day-, one week- or one-month-long intervals or custom time periods as needed.
## 4 Results
### Visualization Insights  
Figures 2-4 visually show the results of forecasted new cancer cases from 2022 to 2026 for regions, age groups, and sex, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Sex/Predicted Years**} & **2022** & **2023** & **2024** & **2025** & **2026** \\ \hline \multirow{2}{*}{**Males**} & New Cancer Cases & 129,012 & 129,515 & 130,009 & 131,690 & 133,088 \\  & Cancer Incidence Rate & 600.6 & 598.9 & 599.7 & 600.8 & 602.3 \\ \hline \multirow{2}{*}{**Females**} & New Cancer Cases & 119,514 & 120,428 & 121,119 & 121,614 & 123,904 \\  & Cancer Incidence Rate & 540.1 & 539.2 & 537.3 & 534.4 & 530.6 \\ \hline \multirow{2}{*}{**Both Sexes**} & New Cancer Cases & 244,007 & 244,941 & 245,242 & 245,051 & 248,705 \\  & Cancer Incidence Rate & 567.6 & 566.3 & 565.3 & 563.8 & 561.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Predicted number of new cancer cases and cancer incidence rate by sex in Canada.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Age Group/Predicted Years**} & **2022** & **2023** & **2024** & **2025** & **2026** \\ \hline \multirow{2}{*}{50 to 54 years} & New Cancer Cases & 15,325 & 14,930 & 14,509 & 14,018 & 13,488 \\  & Cancer Incidence Rate & 509.6 & 510.0 & 510.6 & 510.9 & 510.9 \\ \hline \multirow{2}{*}{55 to 59 years} & New Cancer Cases & 22,931 & 22,845 & 22,740 & 22,576 & 22,228 \\  & Cancer Incidence Rate & 775.0 & 775.4 & 774.7 & 774.8 & 774.6 \\ \hline \multirow{2}{*}{60 to 64 years} & New Cancer Cases & 32,186 & 32,419 & 32,481 & 32,641 & 33,517 \\  & Cancer Incidence Rate & 1140.0 & 1144.1 & 1142.8 & 1139.6 & 1140.5 \\ \hline \multirow{2}{*}{65 to 69 years} & New Cancer Cases & 38,617 & 39,032 & 39,397 & 39,965 & 41,398 \\  & Cancer Incidence Rate & 1604.8 & 1608.2 & 1604.8 & 1603.0 & 1603.0 \\ \hline \multirow{2}{*}{70 to 74 years} & New Cancer Cases & 39,764 & 40,584 & 41,431 & 42,145 & 43,771 \\  & Cancer Incidence Rate & 2040.0 & 2037.9 & 2037.4 & 2033.9 & 2031.8 \\ \hline \multirow{2}{*}{75 to 79 years} & New Cancer Cases & 31,024 & 31,431 & 31,890 & 32,303 & 33,313 \\  & Cancer Incidence Rate & 2362.2 & 2369.2 & 2372.2 & 2372.2 & 2368.4 \\ \hline \multirow{2}{*}{80 to 84 years} & New Cancer Cases & 22,494 & 22,578 & 22,682 & 22,787 & 22,883 \\  & Cancer Incidence Rate & 2590.6 & 2591.1 & 2590.8 & 2588.9 & 2586.9 \\ \hline \multirow{2}{*}{85 to 89 years} & New Cancer Cases & 14,756 & 15,094 & 15,181 & 15,342 & 15,602 \\  & Cancer Incidence Rate & 2667.2 & 2671.3 & 2671.5 & 2672.3 & 2674.6 \\ \hline \multirow{2}{*}{90 years and above} & New Cancer Cases & 8185 & 8311 & 8432 & 8550 & 8685 \\  & Cancer Incidence Rate & 2446.3 & 2449.3 & 2444.8 & 2437.5 & 2440.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._Figure 3: Forecasted new cancer cases across various age groups in Canada (the yellow-shaded area) shows the predicted values).  
Figure 2: Forecasted new cancer cases across various regions in Canada (the yellow-shaded area) shows the predicted values).
# Cellular Automata Approach to Topology Optimization of Graded Multi-Material Structures
###### Abstract  
Despite decades of progress, structural topology optimization is still one of the most important areas of engineering optimal design. The intensive research within this area has been stimulated by the development of efficient methods and algorithms on one side and the needs and demands of contemporary engineering on the other. Over the years, the practical aspect of topology optimization has become one of the most significant issues within the design community. Simultaneously, the range of design applications has been broadening. Among many research areas where topology optimization is present, attention has been paid to the design of multi-material structures. The gradation of the material properties has a significant influence on the final layout of the structure, so this problem can be treated as an extension of the classical task of the topology optimization of structures made of a material with uniform distributions of properties. While working with multi-material structures, the important role plays an interface between parts made of materials with different properties. In this paper, the implementation of interfaces made of functionally graded materials (FGM) is proposed. A functionally graded interface means that continuous and smooth changes of properties are assigned to a particular direction from one material surface to another. This paper presents the idea of topology optimization of graded multi-material structures using a simple, fast convergent technique based on the Cellular Automata approach. The proposal is to take the advantage of the versatility of efficient professional finite element-based structural analysis software and the simplicity of the original heuristic topology generator, to build a tool for the optimization of FGM structures as well as multi-material structures including the FGM interface.