esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Resiliency Enhancement Using Load Shedding  
In the islanded mode, we studied the resiliency enhancement using load shedding technique. This technique is mainly based on covering the critical demand in case of the absence of the main grid. Algorithm 1 illustrates the applied scheme. This algorithm starts by continuously sensing the current flowing from the main grid towards the MG. Next, the difference between the generated power and the consumption power, \(\Delta P\) is computed as given in Equation (10). In general, the power balance state is achieved when the generated power equals the consumed power, as shown in Equations (11) and (12) [23].  
\[\Delta P\ =\ P_{generated}\ -\ P_{consumed} \tag{10}\]  
where \(P_{generated}\) and \(P_{consumed}\) can be given as:  
\[P_{generated}\ =\ P_{Grid}\ +\ P_{PV}\ +\ P_{DG}+P_{SS\_discharge} \tag{11}\]  
\[P_{consumed}\ =\ P_{demand}\ +\ P_{SS\_charge} \tag{12}\]  
where is the power from the main grid, \(P_{PV}\) is the \(PV\) output power, \(P_{DG}\) is the diesel generator power, \(P_{SS\_discharge}\) is the storage systems power during the discharging, \(P_{demand}\) is the demand power, \(P_{SS\_charge}\) the storage systems power during the charging operation.  
\(\Delta P\) has positive, or negative, or zero values. The positive value of the \(\Delta P\) parameter means that the generated power is more than the demand. In this case, and since there is no storage system in the built-model, other mechanisms can be applied to benefit from this case economically and environmentally. For instance, surplus power can be sold to other organizations, MGs, institutions, etc. On the other hand, if the sign of the \(\Delta P\) parameter is negative. This means that the demand side is more than the generated power. Other strategies have to be implemented to maintain a stable and reliable operation of the system.  
Through this paper, a load shedding algorithm has been implemented to decrease the demand side utill the power balance state is achieved. The load shedding algorithm is defined as the process of gradual load disconnection in terms of their serving priorities. In addition to that, the zero value of \(\Delta P\) indicates the balance state between the generation and the consumption sides. Figure 4 represents the load shedding flowchart. The load shedding is conducted by disconnecting the non-critical loads in ascending order.  
Figure 4: Load shedding flowchart.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 3 Real-Time Health Assessment Algorithm
### Interplanetary Communication Link Health Assessment  
Assuming that information transmission delay and the packet loss are not considered and the channel has sufficient bandwidth, a destructiveness-based approach is generally adopted to confirm the link importance in the topology. The judging index is the natural connectivity [18], which reflects the redundancy of the intersatellite alternative paths and indicates the degree of intersatellite connectivity even after a link failure. Combining the above analysis, the following definition is given.  
**Definition 6**.: _Intersatellite correlation matrix \(A(G)=\left\{a_{ij}\right\}_{N\times N}\): The elements of the matrix indicate the presence or the absence of communication links between satellites. If there is a link between \(v_{i}\) and \(v_{j}\), \(a_{ij}=1\); otherwise, \(a_{ij}=0\)._  
**Definition 7**.: _The natural connectivity is \(\bar{\lambda}(G)\): The average value of all characteristic roots of \(A(G)\) concerning the natural logarithm, the natural exponent [18]._  
\[\bar{\lambda}(G)=\ln\left(\frac{1}{N}\sum_{i=1}^{N}e^{\lambda_{i}(G)}\right) \tag{12}\]  
_where \(\lambda_{i}\) is the characteristic root \(i\) of \(A(G)\). The larger the natural connectivity \(\bar{\lambda}\) is, the more resistant the connection is to destruction. When the link \(e_{i}\) fails, the recalculated natural connectivity is denoted by \(\bar{\lambda}(G-e_{i})\)._  
**Definition 8**.: _Link importance \(I(e_{i})\): can indicate the link importance, and the expression is as follows._  
\[I(e_{i})=1-\frac{\bar{\lambda}(G-e_{i})}{\bar{\lambda}(G)} \tag{13}\]  
The normalization process leads to the following link importance weight values.  
\[\omega(e_{i})=\frac{I(e_{i})}{\sum\limits_{i=1}^{M}I(e_{i})} \tag{14}\]In this paper, when performing link analysis, the default satellites are visible to each other, and only the reliability of the communication link transmission needs to be considered. According to the reliability knowledge, the intersatellite link reliability can be expressed as follows.  
\[R_{ei}(t)=e^{-\lambda_{ei}t} \tag{15}\]  
where \(\lambda_{ei}\) denotes the failure rate of link \(e_{i}(i=1,2,\cdots,M)\), which can be obtained from the Monte Carlo simulation based on historical data. Combining the interstellar link reliability with the link weight values and further collating, the interstellar communication link health can be derived as follows.  
\[H_{link}=\sum\limits_{i=1}^{M}R_{ei}\times\omega(e_{i}) \tag{16}\]
## 4 Experimentation and Results
### Statistical Analysis
#### 4.5.4 Baseline Comparison  
We used the same statistical method to evaluate the performance of YOLOv5 + DeepSORT with more straightforward models (such as SSD and Faster R-CNN) for additional validation. Regarding detection accuracy, the YOLOv5 + DeepSORT combo performed noticeably better than these baseline models (\(p<0.01\)). These statistical analyses ensure that the reported performance gains are accurate and not due to random data fluctuations.
applied sciences  
The Effect of Conductive Alginate Capsules Encapsulating Rejuvenator (HealRoad Capsules) on the Healing Properties of 10 mm Stone Mastic Asphalt Mix  
[1] [PERSON] 1,2,*(c), [PERSON] 3 and [PERSON] 3  
[1] Structural Reliability Section, Department of Infrastructure & Maritime, TNO, Stieltjesweg 1, 2628 CK Delft, The Netherlands [2] Department of Materials, Mechanics, Management & Design (3 Md), Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinsey 1, 2628 CN Delft, The Netherlands [3] Lagan Breedon House, Rosemount Business Park, Ballycolin Road, D11 K2 TP Dublin, Ireland; [EMAIL_ADDRESS] (C.F.): [EMAIL_ADDRESS] (D.O.):  
[2] Correspondence: [EMAIL_ADDRESS]; Tel. +31-15-27-81985  
[3]**Featured Application: Conductive alginate capsules encapsulating a bitumen rejuvenator (HealRoad capsules) is a unique self-healing asphalt technology which reinforces the asphalt pavement by improving its tensile and compressive strength and enhances its autonomous healing properties. The concept combines two existing self-healing asphalt technologies (induction heating and rejuvenator encapsulation) to create a self-healing system which provides quick and effective asphalt pavement repair. The induction heating is used to repair asphalt damage (cracks and ravelling) while the rejuvenator is released to rejuvenate aged asphalt binder (bitumen).**  
Conductive alginate capsules encapsulating a bitumen rejuvenator (HealRoad capsules) has demonstrated good healing abilities in pure bitumen and mortar mixes. HealRoad capsules can efficiently heal damage via induction heating. They also release the encapsulated rejuvenator, thereby rejuvenating aged bitumen. These findings indicate that HealRoad capsules and induction heating systems combined could represent a possible asphalt pavement maintenance method. This paper investigated the effect of HealRoad capsules on the mechanical performance of the 10 mm stone music asphalt mix and measured the damage repair (healing) efficiency of the capsules in an asphalt mix. The results indicate that in small amounts, >1%, HealRoad capsules do not degrade the mix performance (indirect tensile strength and rutting resistance) and in some cases, the HealRoad capsules actually improve mix performance, e.g., in terms of the indirect tensile strength ratio (water sensitivity). However, the HealRoad capsules are unable to stimulate induction healing due to the small volume of capsules within the mix. Further investigation demonstrated that increasing the capsules in the mix to >5% can stimulate induction heating effectively. However, it also indicated that a high content of HealRoad capsules reduces the asphalt mix strength. The study has shown that HealRoad capsules are an effective healing system for high bitumen content mixtures such as mortar mixtures but is an inefficient healing system for a full asphalt mix, such as the 10 mm stone music asphalt mix.  
[1]**Keywords:** asphalt pavements; self-healing; hybrid self-healing system; induction heating; indirect tensile strength; water sensitivity; wheel tracking rutting resistance
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 2 Impacts of Extreme Events on Power Systems  
Natural disasters such as hurricanes, floods, earthquakes, snowstorms, and wild-fires, as well as man-made threats such as physical attacks, human operator missteps, and cyberattacks, can cause significant damage to infrastructure, including permanent damage [14; 15; 16; 17]. These events can lead to long-term power outages and the destruction of major equipment such as transmission and distribution lines, substations, and power plants, severely impacting the power system [18]. For instance, hurricanes, snowstorms, and wildfires can affect overhead transmission and distribution lines and towers. Earthquakes can impact both overhead and underground structures [19]. Floods may damage substations and power plants, leading to operational interruptions. Studies on the consequences of past power outages indicate that it is currently difficult to completely prevent power outages caused by natural disasters or man-made threats [10]. Therefore, resilience assessment is crucial to mitigate and respond to the impact of extreme events on power systems. Some resilience studies used fragility curves to assess the vulnerability of the system [20]. In [19], the annual occurrence probability of hurricanes was represented by a probability distribution function using a statistical approach. In [21], fragility functions were introduced to characterize the vulnerability of substations to high winds, representing the probability of damage based on terrain types and wind speeds. In [22], the failure probability of substations caused by floods was related to the flood depth, and the overall failure probability of the substation was obtained from the failure probability of each substation in a flood scenario. In [23], the failure function was characterized based on a logarithmic normal fragility curve, representing the probability of exceeding a damaged state under given earthquake parameters. In [24; 25], the failure probability could be expressed as a function of weather intensity. When evaluating ice disasters, the weather intensity can be represented by ice thickness to obtain the failure probability. In [26], the rate of wildfire spread depends on wind speeds and the vegetation in the area. Thus, the rate can be calculated using a wind-dependent function. In [27], the variation of the conductor temperature was calculated using the non-steady heat balance equation when calculating the temperature of overhead lines. Table 1 presents the quantifications of the impact caused by natural disasters.
## 3 Methods
### Extracting Feature Data
#### 3.2.2 Feature Selection  
Due to the numerous and high-dimensional engineering parameters of a spacecraft, directly processing all data cannot quickly obtain spacecraft state information. Therefore, it is necessary to screen the parameter set and identify the parameters that best reflect the spacecraft's state. Considering that there is related information among spacecraft state parameters, we can determine the correlation between parameters by mining the associated knowledge among them and selecting features based on the degree of correlation. In this paper, the Kendall rank correlation coefficient is used as a tool for parameter correlation analysis.  
Figure 6: Relationship extraction model.  
Suppose there are two random variables \(X\) and \(Y\) with \(n\) data points, forming \(n\) element pairs (\(X_{i}\), \(Y_{i}\)). Concordant pairs represent that \(X\) and \(Y\) have the same trend, while discordant pairs indicate opposite trends. [PERSON]'s tau statistic calculates the number of concordant and discordant pairs in pairwise comparisons. The correlation coefficient ranges from \(-1\) to \(1\), with negative numbers indicating negative correlation, positive numbers indicating positive correlation, and larger absolute values indicating a closer correlation. When the correlation coefficient is approaching \(0\), the correlation is less significant. The calculation formula is as follows:  
\[\tau=\frac{2}{n(n-1)}{\sum_{i<j}}sgn(X_{i}-X_{j})sgn(Y_{i}-Y_{j}), \tag{1}\]  
where \(sgn\) is the sign function.  
Before performing feature selection, we discretize the continuous attribute parameters into K finite intervals using equidistant binning, resulting in K states for each parameter. The Kendall correlation coefficient is employed to measure the correlation among various parameters. Parameters with lower correlation with other variables are removed first. We set a threshold, denoted as \(\tau\), and if the absolute sum of the correlation coefficients between a parameter and other variables is lower than \(\tau\), the parameter is removed. Please note that this \(\tau\) threshold is not solely used to keep the highest correlated features. Instead, it serves as a cut-off value to filter out features that have little correlation with the target variable. This approach helps to reduce the dimensionality of the data and prevent overfitting.
## 2 Materials and Methods
### Sensory Analysis  
#### 2.3.1 Expert Evaluation  
The overall quality of the apple jams was assessed using the sensory profiling method to characterize the flavor and aroma profile of the obtained product. The evaluation conditions were determined in accordance with Civille and Carr [35]. The selection of distinguishing features for sensory analysis was performed using the procedure PN-EN ISO 13299:2016-05 [36]. The evaluation was conducted by a qualified team, who were checked for individual sensory sensitivity and had experience evaluating fruit and vegetable products [37; 38].  
Samples of apple jam (30 g) were placed in plastic containers and then covered with a lid. After labeling them with a three-digit code, the samples were delivered for evaluation at a temperature of 25 \(\pm\) 1 \({}^{\circ}\)C, together with an evaluation card. Drinking water was provided between trials. Quantitative feature evaluation was performed on an unstructured 10 cm linear scale from 0 to 10 conventional units (c.u.) with appropriate word anchors:  
* odor--apple, sweet, nectar, wine, metallic, sharp, and \"other\" (none-strong);
* taste--sweet, bitter, and sour (none-strong);
* flavor--apple, nectar, spicy (woody), metallic, bland, astringent, and \"other\" (none-strong);
* color (light-dark);
* sensory balance--the degree of harmonization between the sensory attributes (low-high).  
The quality attributes were defined in words to unify the evaluation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & **Sugar** & \multicolumn{2}{c}{**Steviol Glycos**} & **Pectin** & **Citic Acids** \\ \cline{2-5}  & **(g)** & **(g)** & **(\%) * & **(g 100 g\({}^{-1}\))** & **(g 100 g\({}^{-1}\))** \\ \hline VL0 & 10 & 0.00 & 0 & 0.3 & 0.52 \\ \hline VL10 & 9 & 0.01 & 10 & 0.3 & 0.51 \\ \hline VL20 & 8 & 0.02 & 20 & 0.3 & 0.50 \\ \hline VL30 & 7 & 0.03 & 30 & 0.3 & 0.49 \\ \hline VL40 & 6 & 0.04 & 40 & 0.3 & 0.48 \\ \hline VL50 & 5 & 0.05 & 50 & 0.3 & 0.46 \\ \hline VL60 & 4 & 0.06 & 60 & 0.3 & 0.45 \\ \hline VL70 & 3 & 0.07 & 70 & 0.3 & 0.44 \\ \hline VL80 & 2 & 0.08 & 80 & 0.3 & 0.43 \\ \hline VL90 & 1 & 0.09 & 90 & 0.3 & 0.42 \\ \hline VL100 & 0 & 0.10 & 100 & 0.3 & 0.41 \\ \hline \hline \end{tabular}  
* Percentage of sugar substitution with steviol glycosides; and VL, very-low-sugar variants.  
\end{table}
Table 1: The composition of very-low-sugar apple jams for 100 g of apple pulp.  
For further detailed analyses and storage studies, four levels of sugar substitution by steviol glycosides were selected--0, 30, 50, and 80%--alongside chosen quality parameters that differentiated the products, as found in the first stage of the research. These were color (light-dark), odor (apple, metallic), taste (sweet, bitter), and flavor (apple), and, due to changes during storage, consistency (loose-compact) and the overall quality (bad-very good) were added.
## 4 Energy Conserving Solutions in Wireless Networking-Based IoT
### Energy Conserving Solutions for WPAN-Based IoT
#### 4.3.2 Energy Conserving Solutions for Z-Wave  
Z-Wave is one of the most widely used technologies in smart home IoT environments owing to its network reliability and stability. Authors in [82, 87] considered different layers of Z-wave for more reliable and delay tolerant transmission, and efficient routing for efficient home management. Particularly, the MAC layer is enhanced with energy saving support, where devices use acknowledgment based collision avoidance techniques to reduce the chances for collision and avoid power waste.  
[PERSON] _et al._[89] presented a solution to the problem of network faults caused by device failures. They proposed the use of a meta-routing framework which enables automatic route reconstruction at run-time to overcome faults by making devices keep track of their neighbors. If a node is facing any failure, it can be recovered by using the routing table of its nearby neighbors. For a low energy policy, the implementation of this framework can show energy efficiency for M2M communications.  
We note that the discussed solutions did not consider the usage of sleeping intervals which could be a major source of energy conservation in devices. They did not consider possible improvements to Z-Wave routing which assumes a central controller with global topology information and thus may be a limitation in a network lifetime.
## 4 Results and Discussion
### Post-adsorption Studies
#### 4.5.2 Adsorption Mechanism  
The FTIR spectra before and after adsorption (Figure 8) allowed us to predict the actual mechanisms involved in the adsorption. Various functional groups such as -OH, -C=C, and -CO were detected on the surface of the material. The groups were protonated or deprotonated depending on the pH of the solution and allowed interactions between the material and the pollutants. The interaction took place through various adsorption mechanisms, which occurred in a synergistic manner, as shown in Scheme 1. The adsorption mechanisms were facilitated by the exchange and transfer of electrons, leading to electrostatic interactions, hydrogen bonding, Yoshida hydrogen bonding, and complex formation. In addition, an ion exchange reaction took place between the Cr(VI) in the solution and the calcium in the CaO. This is consistent with the data obtained for the kinetic models (Table 3).
# Modeling of Fatigue-Strength Development
### The Maturity Method
#### 1.2.3 Strength-Maturity Relationships  
The relationship between concrete strength development and maturity has been described using logarithmic [48], linear hyperbolic [49], parabolic hyperbolic [50], and exponential maturity functions [51]. Recent studies on foamed and emulsion bitumen asphalts have found out that linear hyperbolic and parabolic hyperbolic maturity functions offer the best prediction of stiffness development in cold-mix asphalts [28; 39]. The linear hyperbolic and parabolic hyperbolic maturity functions are given by Equations (6) and (7), respectively.  
\[\text{S}=\text{S}_{\text{u}}\frac{\text{k(M-M_{\text{o}})}}{1+\text{k(M-M_{ \text{o}})}} \tag{6}\]  
\[\text{S}=\text{S}_{\text{u}}\frac{\sqrt{\text{k(M-M_{\text{o}})}}}{1+\sqrt{ \text{k(M-M_{\text{o}})}}} \tag{7}\]  
where,  
S = Strength at age t  
S\({}_{\text{u}}\) = Limiting strength at infinite age  
k = Rate constant, initial slope of strength-maturity curve (1/days)  
M\({}_{\text{o}}\) = Maturity index at the start of strength development, at age t\({}_{\text{o}}\) (\({}^{\circ}\)C-hours or \({}^{\circ}\)C-days)  
M = Maturity index at age t (\({}^{\circ}\)C-hours or \({}^{\circ}\)C-days)
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 3 Simulation Analysis of Static and Dynamic Characteristics of Bus Body Frame  
### Static Structural Mechanics Simulation Results  
#### 3.1.1 Horizontal Bending Condition  
In Figure 3, under the horizontal bending condition, the maximum stress in the bus body frame is 220.7 MPa, and it occurs at the connection between the left side frame and the fifth beam of the roof frame. The bus body frame material adopted is Q345, with a yield strength of 345 MPa, indicating that the overall structural integrity of the bus body can meet the strength requirements for this condition.  
According to Figure 4, the maximum displacement in this condition is 7.723 mm, with significant deformation concentrated mainly in the front half of the roof frame. The overall deformation of the body frame is controlled within an acceptable range of 30 mm, which does not exceed the expected target and meets the stiffness requirements for design.
# The Ionospheric Scintillation Effects on the BeiDou Signal Receiver
## 3 Solution of Signal Propagation Based on MPS Technique
### Formulation
Considering that the profile is a series of real numbers, the random amplitude and phase fluctuation of the received signal on ground screen can be described as:  
\[\chi(n)=\sum\limits_{k=0}^{N-1}\sqrt{\Phi_{\chi}(k\cdot\frac{2\pi}{L_{h}}) \cdot\frac{2\pi}{L_{h}}}\cdot\cos(\frac{2\pi nk}{N}+\varphi_{k}) \tag{29}\]  
\[S(n)=\sum\limits_{k=0}^{N-1}\sqrt{\Phi_{S}(k\cdot\frac{2\pi}{L_{h}})\cdot\frac {2\pi}{L_{h}}}\cdot\cos(\frac{2\pi nk}{N}+\varphi_{k}) \tag{30}\]  
Generally, ionospheric scintillations are most severe and prevalent at 200 km to 400 km from the ground. To let the distribution of a phase screen adequately represent the actual signal fluctuations, the distribution on the phase screen must contain at least one whole period of signal and the phase-screen length \(L_{h}\) must be at least five times as large as the outer scale size. For the spacing between grid points \(\Delta q\), it should be set small enough to ensure that the phase change from one grid to the next is less than \(\pi\) to satisfy the Nyquist Sampling Theorem. The limitation of \(L_{h}\) and \(\Delta q\) is given by [14]:  
\[L_{h}\geq 5L_{0} \tag{31}\]  
\[\Delta q\leq\frac{l_{i}}{3} \tag{32}\]  
\[\Delta q\leq\pi(-\frac{d^{2}B_{\varphi}(\xi)}{d\xi^{2}}|_{\xi=0})^{-1/2} \tag{33}\]  
In Equation (33), \(B_{\varphi}(\xi)\) is given in Equation (27). On the other hand, to make sure that the simulation is close enough to the actual propagation, the difference of the function \(\kappa^{2}z/k\) between two adjacent values of \(\kappa\) must be less than 2\(\pi\). The limitation of \(z\) is:  
\[\frac{\pi^{2}N^{2}z}{{k{L_{h}}}^{2}}-\frac{\pi^{2}(N-1)^{2}z}{{k{L_{h}}}^{2}}<2\pi \tag{34}\]  
Then the ionospheric scintillation index \(S_{4}\) can be calculated as follows:  
\[S_{4}=\sqrt{\frac{<l^{2}>-<I>^{2}}{<I>^{2}}} \tag{35}\]In Equation (35), \(I\) is the signal intensity, equal to the normalized signal amplitude.