esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we propose the HetMECNet, a new heterogeneous edge computing network architecture that is composed of MEC Servers with different transmission power, computing capabilities, number of CPU cores, and different waiting queue lengths. We utilize the average delay of task offloading, immediate service probability, and blocking probability to characterize network performance. We then evaluate the impacts of network parameters and bias factors on network performance. Our results provide insights for HetMECNet design. Specifically, (1) as the MEC server computing capability increases, offloading more user tasks to MEC servers with high computing capability can achieve better system performance. For example, in high-traffic areas such as smart factories, increasing the computing power of MEC servers can improve performance by reducing task processing latency. (2) When user task volume is large, system performance decreases, and it is best to offload tasks to MEC servers with lower computing capabilities. For example, during city peak hours, user tasks surge, and offloading tasks to a server with a lower capacity can prevent system overload. (3) Single-core edge servers can be replaced by multi-core edge servers with a lower service rate for better performance. For example, in latency-critical scenarios such as healthcare IoT, replacing single-core servers with dual-core (or triple-core) nodes can reduce latency. (4) Increasing the waiting queue length allows for offloading more user tasks to MEC servers, which can improve system performance. For example, extending the wait queue length in dense network scenarios can increase the immediate service probability during peak loads, allowing more tasks to be processed without blocking.
## 3 Control Design and Stability Analysis
### Stability Analysis
: _It is worth noting that the log-type and tan-type barrier Lyapunov function techniques in [31; 32] only guarantee that the constrained tracking errors \(e_{i}\), \(i=1,2...\) never cross the constrained boundaries \(k_{i}\), namely, the tracking errors are satisfied \(|e_{i}|<k_{i}\). The proposed method in this paper not only guarantees the tracking errors are satisfied \(|e_{i}|<k_{i}\) but also are made arbitrarily small by suitably selecting design parameters._  
**Remark 12**. : _In [33], the log-type barrier Lyapunov function technique was used to constrain the position and speed tracking errors of the hovercraft and only guaranteed that the tracking errors remain within the time-invariant constraint boundaries. However, in this paper, the proposed integral barrier Lyapunov function combining the appointed-time constraint function not only deals with the time-varying constraints but also guarantees that the tracking errors can converge to any small region around zero._
# Elemental Mercury Removal over CeO\({}_{2}\)/TiO\({}_{2}\) Catalyst Prepared by Sol-Gel Method
## 3 Results and Discussion
### The Effects of flue Gas Components on Catalysts Performance
#### 3.2.4 The Effect of the Ratio of NH\({}_{3}\)/NO on Catalytic Performance  
The ratio of NH\({}_{3}\) to NO is one of the most vital parameters influencing the efficiency of NO\({}_{x}\) removal in a SCR system. According to Section 3.2.3, the concentration of NH\({}_{3}\) and NO could also influence the elemental mercury removal. Hence, it is necessary to study the effect of the NH\({}_{3}\)/NO ratio on the Hg\({}^{0}\) removal efficiency of the catalysts. The results are shown in Figure 4d. The range of NH\({}_{3}\)/NO ratio during the test was from 0.8 to 1.2 with a constant NO concentration of 200 \(\upmu\)L\(\cdot\)L\({}^{-1}\). It can be observed that higher NH\({}_{3}\)/NO ratio would cause a decline in Hg\({}^{0}\) removal efficiency. The result is reasonable, since NH\({}_{3}\) could restrain the oxidization of elemental mercury, as mentioned in Section 3.2.3. Generally speaking, the initial NH\({}_{3}\) concentration is slightly higher than the initial NO concentration in order to ensure an ideal De-NO\({}_{x}\) performance. As can be seen from Figure 4d, the negative effect of high NH\({}_{3}\)/NO ratio on Hg\({}^{0}\) removal was relatively minor within the test range. Except for Ce1.0 Ti, the Hg\({}^{0}\) removal efficiencies of other catalysts decreased only by less than 10% as the NH\({}_{3}\)/NO ratio changed from 1.0 to 1.2.
## 4 Discussion
### Theoretical Rationale and Design Motivation  
Starting from the imaging characteristics of remote sensing images, whether they are from satellite platforms or low-altitude unmanned aerial vehicles, remote sensing images generally have non-ideal factors such as illumination changes, haze interference, and vegetation occlusion. In response to the above problems, this paper introduces the LKF module, which effectively bridges the mechanism gap between traditional filtering models and deep learning and effectively alleviates the bottleneck of traditional filters' strong dependence on prior parameters and poor adaptability in nonlinear scenarios. This module sets the state transition matrix and the covariance matrix as trainable parameters, enabling the adaptive correction of disturbed features during the end-to-end training process. This allows the model to continuously correct prediction errors in remote sensing scenarios with spatial noise and observational uncertainties, enhancing the consistency and stability of its representation. At the same time, the matrix reasoning process on which the module depends is essentially a tensor operation with a high degree of parallelism, which has the advantages of good computational efficiency and memory usage. The experimental results further show that this module can effectively enhance the stability of feature representation during the feature extraction stage. Especially in typical interference areas such as vegetation occlusion and shadow coverage, it significantly improves the classification accuracy of the model.
## 4 Discussion  
We implemented and evaluated MORSE, a perturbation method to find multiple optima for ILPs. After [33], few subsequent papers have investigated randomization in this context (see Section 1.2). A key advantage of the present work and other randomized methods is that they are amenable to parallelization. Another distinguishing feature is that we proved that our perturbation method preserves optima when all the coefficients are integers, which [PERSON] and [PERSON] did not prove for their implementation of a randomized algorithm ([6]).  
With MORSE, we generally find more diverse optima (as quantified by higher average pairwise Hamming distance between solution vectors) within the first 100 distinct optima returned for MIPLIB instances with >100 optima. Additionally, when executing \(n\) runs in parallel, we find distinct optima at a faster rate with MORSE than with the method in Gurobi. Based on the number of optima and the number of runs allotted for a given instance, we either find the same number of optima in fewer runs or find a greater number of optima using MORSE. An important practical application of our method is to decide quickly if an instance has more than one optimal solution. For example, in Figure 9, subpanels for  
Figure 15: Full MIPLIB results, average Shannon entropy vs. optimization method for all MIPLIB instances tested. The plots are arranged in descending order of maximum average Shannon entropy.  
supportcase14 and supportcase16, we see two instances in which Gurobi's solution pool method suggests that the optimal solution is unique, but MORSE finds two distinct optimal solutions. Similarly, the CPLEX populate function returned just one optimum each for the MIPLIB instances set1a1 and set1c1, despite setting the parameter values to return both optima. Moreover, the SCIP count function requires multiple steps to decide if there is more than one optimal solution: first solving to optimality, and then constructing and solving a modified ILP.  
We also integrated our method with MadHitter, an algorithm that identifies minimal gene target hitting sets for experimental gene therapy cancer treatments by formulating the problem as an ILP. Similar to the result we obtained when testing on MIPLIB instances, we either found all optimal gene target sets with MORSE, or a greater number of optimal gene target sets, after executing 1000 runs in parallel compared to Gurobi. We quantified the Shannon entropy of each vector of 1000 runs for each dataset and weights method tested, and found that, for any given dataset, the Shannon entropy for MORSE runs exceeded that of uniform weights runs, indicating a higher diversity among the optima found by MORSE.
## 3 Results and Discussion
### Mathematical Model  
Groundwater is uniform within an infinite small range. Each ion concentration of groundwater is low, and the temperature of groundwater is generally relatively constant [44]. The aeration zone blocks the exchange of aquifer and atmospheric gas, and the carbonate in an oil-polluted aquifer is often saturated [34]. Therefore, the following assumptions are made to facilitate the establishment of the mathematical model: (1) the components are well mixed, and the pressure, salinity, and ionic strength are uniform, (2) CO\({}_{2}\) is the ideal gas, (3) the temperature is constant, (4) the ionic activity of an ion equals the actual concentration at which the reactions take place in a homogeneous environment, and (5) the solution in the system, which is a closed system, is a calcium carbonate saturated solution. Based on the mechanism model, the mathematical model was derived from the mass conservation and charge conservation equations in Equations (12) and (13). In case a carbonate component is not introduced into the system, the Ca\({}^{2+}\) and dissolved carbonate components come from the degradation of toluene and calcite. As a result, the mass conservation equation can be expressed as follows:  
\[7[\text{C}_{7}\text{H}_{8}]+[\text{Ca}^{2+}]=[\text{H}_{2}\text{CO}_{3}]+[\text {HCO}_{3}\text{${}^{-}$}]+[\text{CO}_{3}\text{${}^{2-}$}]+[\text{CO}_{2}] \tag{14}\]  
where \([\text{C}_{7}\text{H}_{8}]\) is the concentration of toluene involved in the reaction, mol/L. [] is the ionic activity of the ions, mol/L. The electric charge conservation equation is as follows:  
\[[\text{H}^{+}]+[\text{Na}^{+}]+[\text{K}^{+}]+2[\text{Ca}^{2+}]+2[\text{Mg}^{ 2+}]=[\text{Cl}^{-}]+2[\text{SO}_{4}\text{${}^{2-}$}]+[\text{HCO}_{3}\text{${} ^{-}$}]+2[\text{CO}_{3}\text{${}^{2-}$}]+[\text{NO}_{2}\text{${}^{-}$}]+[ \text{OH}^{-}] \tag{15}\]  
where [] is the ionic activity of the ions, mol/L. The equilibrium constant in the system can be expressed as:  
\[\text{K}_{\text{H}}\ =\ \frac{[\text{H}_{2}\text{CO}_{3}]}{\text{P}_{\text{co2}}} \tag{16}\]  
\[\text{K}_{\text{H2 CO3}}\ =\ \frac{\left[\text{H}^{+}\right]\times\left[\text{ HCO}_{3}^{-}\right]}{[\text{H}_{2}\text{CO}_{3}]} \tag{17}\]  
\[\text{K}_{\text{HCO}_{3-}}\ =\ \frac{\left[\text{H}^{+}\right]\times\left[ \text{CO}_{3}^{2-}\right]}{[\text{HCO}_{3}^{-}\right]} \tag{18}\]  
\[\text{K}_{\text{w}}\ =\ \left[\text{H}^{+}\right]\times\left[\text{OH}^{-}\right] \tag{19}\]  
\[\text{K}_{\text{cal}}\ =\ \left[\text{Ca}^{2+}\right]\times\left[\text{CO}_{3}^{2-}\right] \tag{20}\]  
According to Equations (16)-(20), the relationship equation between the H\({}^{+}\) concentration and the toluene degradation is established (Equation (21)). \[\left\{
# Numerical Analysis of Reinforcing Effect for Scissors-Type Bridge with Strut Members
## 4 Numerical Results
### Three Scissors Unit Model
#### 4.2.1 In the Case of Nonreinforcing Pattern (Case 0)  
Figure 12 shows the numerical results for Case 0 without any reinforcement. Figure 12a shows the bending moment distribution (BMD), and Figure 12b shows the axial force distribution (AFD). The units of each result are the same as in Figure 7. From Figure 12a, the maximum bending moment is at the central scissors unit. As can be seen from the BMD, no bending moment is generated in the outermost member whose end is not fixed, similar to the results in the two scissors unit model. Figure 12b shows that the maximum tensile force is between the member point \(\overline{B_{2}C_{1}}\), and the maximum compressive force is the point \(\overline{B_{2}C_{1}}\). It can also be seen that the tensile forces are acting on the lower half of the scissors members except for the members connecting to the supports, and the compressive forces are acting on the upper half of the scissors members. The obtained maximum stresses are \(|\sigma_{M_{0}}|=8.5\) MPa and \(|\sigma_{N_{0}}|=0.4\) MPa. Compared with the maximum value in the two scissors unit model, the maximum bending stress increases by 1.4 times and the maximum axial stress decreases by 0.6 times due to the increment of the scissors unit. These results indicate that the maximum bending moment increases with the number of scissors units, but the maximum axial force does not necessarily increase with its number.
## 3 Results
### Accuracy Assessment Analytics
#### 3.3.1 Radiative Transfer Models Used for CYP  
Evaluation of accuracy for CYP was performed cumulatively for the RTMs used alone or integrated with CGMs. The reason for this cumulative analysis was that RTMs alone (4 screened articles) have a limited number of articles. Moreover, integrated RTMs also have limited screened articles where the integration of the same CGM and RTM was studied. There were two articles where APSIM (CGM) and 6S (RTM) [51; 52] were used, with a difference of study area only. The remaining RTM and CGM integrations in relevant screened articles feature different CGMs. Therefore, the R\({}^{2}\) analysis did not provide any insight into the performance of CGMs with different crops or retrieved crop traits. The RTM boxplot (Figure 8) showed that PROSAIL is the only model with a reasonable number of studies for analysis. PROSAIL had a maximum R\({}^{2}\) value of 0.96, with median and mean values of 0.62 and 0.59. The PROSAIL model boxplot showed an R\({}^{2}\) value of 0.11 as an outlier (dot in the boxplot) for the wheat yield estimated via integration with the Aquacrop-OS Model [47]. This outlier was only present in yield estimation; otherwise, FVC retrieval and biomass estimation had a good relationship, with R\({}^{2}\) values of 0.76 and 0.51, respectively. The 6S model had a maximum R\({}^{2}\) value of 1, with a mean and median of 0.90 and 0.93, respectively. However, in these research studies [51; 52], only radiation flux density (RFD) was measured instead of crop traits, and variation in R\({}^{2}\) values was due to changes in the area of the studies in different provinces of China. There is a need to evaluate APSIM model integration using crop traits with RTMs, particularly of PROSAIL as APSIM and PROSAIL are popular CGMs and RTMs, respectively. Some papers calculated R\({}^{2}\) values separately for each crop trait, each crop or each area in the study. Therefore, the count of data in the analysis is greater than the total number of papers selected.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Smart Substation Automation  
The technological development of large-scale integrated circuits, which led to the current availability of advanced, fast, and powerful microprocessors, has resulted in the development of automated digital substations which, by utilizing IEDs, are able to perform functions, such as local and remote monitoring, equipment supervision, etc. Furthermore, the use of digital technology improved the interoperability and communication between devices of different suppliers and technological generations [48, 49]. The specific technology, which is also referred to as the process bus, contributed to the interoperability between heterogeneous systems and the configuration of functions that are based on open architecture to make the system viable. Regarding the communication between devices, their absolute synchronization is required to perform the exchange of large data sets with small latency and precision, in addition to the use of open communication standards such as Ethernet, TCP/IP, and XML to achieve interoperability.  
The above goals can be accomplished by standardizing the synchronization process of IEC 61850-9-2 functions as well as wideband technologies that support quality of service (QoS), such as 5G telecommunication networks [50; 51]. Concluding, IEC 61850 is the only international standard for substation automation that is based on open architecture and incorporates specifications for sampling and timing synchronization based on IEEE 1588 in local and in wide area networks [14; 49].
## 4 Current Approaches to Measure Resilience in Smart ICI
### Quantitative (Holistic) Approach
#### 4.2.5 Multi-Dimensional Resilience Metric  
In [47], the authors propose the model presented in Equation (9), which can be defined by measuring the rate of total potential damage over the time period \(T\):  
\[R(X,T)=\frac{T^{*}-XT/2}{T^{*}}=1-\frac{XT}{2T^{*}} \tag{9}\]  
The components of this model used to determine the overall resilience in smart ICIs are described as follows: \(X=(0;1)\) represents the percentage of functionality lost after a disturbance, \(T=(0;T)\) denotes the time required for complete recovery, and \(T\) signifies an extended period during which lost functionality is assessed. In the same study [47], the authors found that the equivalent level of resilience observed from the resilience triangle could be achieved by employing different combinations of \(X\) and \(T\). Additionally, they present a visualization of the trade-offs between reduced functionality and recovery time for corresponding levels of resilience.  
_Limitations:_ While resilience based on the quantification approach is considered one of the most promising solutions for smart ICIs, the lack of data remains the most challenging aspect of conducting a resilience analysis using this measure. Companies are often unwilling to release data such as mean time to failure (MTTF) or mean time to repair (MTTR), and even when they do, the released data may not cover all actual events.
## 3 Image Fusion
A color moment (including the mean, variance and skewness features) and texture (mean, standard deviation, smoothness, third-order moments, consistency and entropy features) fusion example was carried out in [46] to use the results as the input for a recognition training algorithm. The main advantage of this fusion is having fewer dimensional feature vectors thanks to the compression. In addition, the study proved that fusion has increased recognition accuracy and reduces the false detection rate. Finally, decision-level fusion is a cognitive strategy where source images are converted to generate an independent target attribute estimation which is already a representative symbol for information extraction [44]. A partial density-based fusion was proposed in [47], using the computation of the single-sensor spatial density estimation. The calculated partial densities, instead of feature extraction or pixel-wise merging, are merged with a basic summation fusion rule ignoring the maximum value to provide false alarm reduction. The main advantage is that this technique explicitly treats the localisation uncertainty caused by registration problems. Consequently, the selection of the fusion level that will be applied depends on various parameters, such as the image sources, processed data type and information that the image has. This should be performed in a scrutinizing manner according to the overall expectations from the sources and fusion. Now, we will present current fusion applications that are already used for different materials and various fields within a generic classification according to the methods they are based on in the next section.
## 3 An Instance-Based Learning (IBL) Approach
### Overview of Methodology  
As illustrated in Figure 2, the proposed IBL approach consists of three major components, namely, spectrogram generation, feature extraction, and RUL estimation:  
* Spectrogram generation applies the short-time-Fourier-transform (STFT) technique on the raw signals during each time cycle to generate frequency domain signals, so that spectrograms can be obtained for both learning and test instances.
* Feature extraction is conducted using the principal component analysis (PCA) technique. The principal components (PCs) are calculated from the spectrogram of learning instances. Prognostic features are the coefficients of a spectrogram when projected onto the PCs.
* RUL estimation provides the RUL estimate for the test instance by identifying its similar prognostic feature sequences, which are generated synthetically from the learning instance using the data augmentation method.
## 3 Results and Discussion  
### COSMO Validation  
Due to the novelty of the systems studied, little to no DES-difluoromethane system data was readily available to the authors. Thus, the approach for validating the COSMO system was to compare solubility data fordifluoromethane in ionic liquid literature data, of which DES are considered analogues. This approach has been reportedly used in such novel systems where experimental data are unavailable, as is shown through the work of [PERSON] et al. [34]. The benchmarking data set consists of 42 molar solubility datapoints in various ionic liquids at various pressures at 298.15 k as reported by [PERSON] et al. [35]. The ionic liquid components were generated using the same methods reported in Section 2.1, along with the solubility computations. These generated values were regressed with the experimental values with a results R\({}^{2}\) value of 0.86. The same data set produced an absolute average relative deviation of 25.7% average absolute relative deviation (AARD). Figure 1 contains the resulting datapoints from a \(y\)-axis of experimental values, and an \(x\)-axis of COSMO predicted values from this work. While the single point error (AARD) is relatively high, the qualitative information predicted about these systems is well preserved as is evident from the high R-squared value. Thus, for the purpose of this study into the relative effectiveness of the solvents and energetic mechanisms driving them, COSMO is deemed an appropriate method of examination.
## 3 Challenges
### Fault Tolerance  
CR-WSNs should have self-forming, self-configuration and self-healing properties. In other words, whenever some nodes or links fail, an alternative path that avoids the faulty node or link must be derived. In CR-WSNs, faults can occur for a variety of reasons, such as hardware or software malfunctioning, or natural calamities, e.g., fire, floods, earthquakes, volcanic eruptions, or tsunamis _etc._ A CR-WSN should always be prepared to deal with such situations. There are several types of faults, such as node fault, network fault, and sink fault, _etc._ [PERSON] _et al._[54] surveyed the well fault tolerance in WSNs. [PERSON] _et al._[55] modeled the fault tolerance or reliability _R\({}_{k}\)(t)_ of a wireless sensor node using the Poisson distribution within the time interval (0,t) as follows:  
\[R_{k}\textit{(t)}=\textit{exp}(-\lambda_{k}\textit{t}) \tag{4}\]  
where \(\lambda_{k}\) is the failure rate of wireless sensor node \(k\) and \(t\) is the time period.  
The fault tolerance is one of the challenging issues in CR-WSNs. The protocols designed for CR-WSNs should have a level of fault tolerance capability so that the overall function of the WSNs should not be interrupted.