esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Power Transmission Networks
: _An IVFSNP system (IVFSNPS) [129] of degree \(m\) is a construct of the form \(\Pi=(\mathcal{O},\sigma_{1},\sigma_{2},\ldots,\sigma_{m},syn,IN,OUT)\) where:_  
_(2) \(\sigma_{1},\sigma_{2},\ldots,\sigma_{m}\) represents the neurons and \(\sigma_{i}=(\theta_{i},c_{i},r_{i})\)\(i\in\{1,2,\ldots,m\}\), where:_  
_(a) \(\theta_{i}\) is an interval-valued fuzzy number and it represents the value of spikes initially contained in neuron \(\sigma_{i}\);_  
_(b) \(c_{i}\) is an interval-valued fuzzy number and it represents the confidence factor for a fuzzy production rule. Sometimes it is omitted while representing a fuzzy proposition;_  
_(c) \(r_{i}\) is a firing rule of neuron \(\sigma_{i}\), with the form \(a^{\theta}\to a^{\theta}\) or \(a^{\theta}\to a^{\theta}\), where \(\theta\) and \(\beta\) are two interval-valued fuzzy numbers._  
The fault diagnosis method of power transmission networks identify the faulty components using the information of tripping PRs and CBs received from the SCADA systems. The method in [129] considered fault diagnosis for both lines and buses. In [129], the fault diagnosis method is realized by IVFSNPS. Like in the earlier fault diagnosis method of power transmission [84; 89; 91], in this method IVSNPs contains three types of neurons (proposition neuron and two types of rule neurons). However, the rule neurons are of different types, i.e., \(\bigcirc\) -type rule neurons and \(\bigcirc\) -type rule neurons. IVSNPs model helps us to understand the relationship between the faulty sections, PRs and CBs. Next, the fuzzy reasoning algorithm is performed in order to obtain the degree of confidence of the faulty sections. It is done by reasoning out truth values of proposition neurons. In this algorithm, synaptic connections between the proposition neurons and rule neurons are represented using a matrix and it is further used to reason out the truth values of the proposition neurons without any truth value associated with them. It is important to note that in the fault diagnosis method based on IVFSNPs, the truth values associated with the proposition neurons are integer valued fuzzy numbers. Any large scale power transmission network has many sections. In case of identification of faults in complex and large system, for each system a corresponding IVFSNPs subsystem is constructed. Furthermore, the fault diagnosis model for large scale power transmission network consists of many IVFSNPs subsystems. These systems work in parallel and it makes the fault diagnosis process much faster. This fault diagnosis method also can determine incorrect signals. So this method is suitable for identification of faults in complex power transmission networks. It also has good fault tolerance capacity.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Days of Storage**} & \multicolumn{6}{c}{**Cell Loads (log CFU/g)**} \\ \cline{2-9}  & \multicolumn{6}{c}{**Apples in PP-Bags**} & \multicolumn{6}{c}{**Apples in PET**} \\ \cline{2-9}  & & **C** & **P5** & **P10** & **C** & **P5** & **P10** \\ \hline \multirow{6}{*}{0} & TMC & 4.94 \({}^{\text{a}}\)\(\pm\) 0.29 & 5.15 \({}^{\text{a}}\)\(\pm\) 0.25 & 5.32 \({}^{\text{a}}\)\(\pm\) 0.28 & 3.40 \({}^{\text{a}}\)\(\pm\) 0.44 & 3.34 \({}^{\text{a}}\)\(\pm\) 0.62 & 3.83 \({}^{\text{a}}\)\(\pm\) 0.53 \\  & _Lactobacillus spp._ & 3.23 \({}^{\text{a}}\)\(\pm\)0.23 & 3.10 \({}^{\text{a}}\)\(\pm\) 0.34 & 3.12 \({}^{\text{a}}\)\(\pm\) 0.30 & 2.56 \({}^{\text{a}}\)\(\pm\) 0.22 & 2.81 \({}^{\text{a}}\)\(\pm\) 0.43 & 2.70 \({}^{\text{a}}\)\(\pm\) 0.22 \\  & Yeasts & 2.50 \({}^{\text{a}}\)\(\pm\) 0.30 & 2.71 \({}^{\text{a}}\)\(\pm\) 0.24 & 2.60 \({}^{\text{a}}\)\(\pm\) 0.23 & 1.48 \({}^{\text{a}}\)\(\pm\) 0.27 & 1.56 \({}^{\text{a}}\)\(\pm\) 0.33 & 2.00 \({}^{\text{a}}\)\(\pm\) 0.32 \\  & Molds & 2.28 \({}^{\text{a}}\)\(\pm\) 0.44 & 2.27 \({}^{\text{a}}\)\(\pm\) 0.05 & 2.24 \({}^{\text{a}}\)\(\pm\) 0.34 & 1.78 \({}^{\text{a}}\)\(\pm\) 0.25 & 1.26 \({}^{\text{a}}\)\(\pm\) 0.17 & 1.55 \({}^{\text{a}}\)\(\pm\) 0.43 \\  & TMC & 6.85 \({}^{\text{a}}\)\(\pm\) 0.34 & 6.92 \({}^{\text{a}}\)\(\pm\) 0.22 & 6.83 \({}^{\text{a}}\)\(\pm\) 0.25 & 5.45 \({}^{\text{a}}\)\(\pm\) 0.18 & 4.75 \({}^{\text{a}}\)\(\pm\) 0.37 & 4.56 \({}^{\text{a}}\)\(\pm\) 0.42 \\  & _Lactobacillus spp._ & 3.05 \({}^{\text{a}}\)\(\pm\) 0.4 & 3. \(\pm\) 0.34 & 3.49 \({}^{\text{a}}\)\(\pm\) 0.30 & 4.03 \({}^{\text{a}}\)\(\pm\) 0.47 & 4.02 \({}^{\text{a}}\)\(\pm\) 0.54 & 4.43 \({}^{\text{a}}\)\(\pm\) 0.20 \\  & Yeasts & 3.330 \({}^{\text{a}}\)\(\pm\) 0.45 & 3.67 \({}^{\text{a}}\)\(\pm\) 0.10 & 3.54 \({}^{\text{a}}\)\(\pm\) 0.30 & 3.70 \({}^{\text{a}}\)\(\pm\) 0.20 & 3.51 \({}^{\text{a}}\)\(\pm\) 0.16 & 3.59 \({}^{\text{a}}\)\(\pm\) 0.24 \\  & Molds & 2.18 \({}^{\text{a}}\)\(\pm\) 0.28 & 2.02 \({}^{\text{a}}\)\(\pm\) 0.34 & 1.93 \({}^{\text{a}}\)\(\pm\) 0.21 & * & * & * & * \\  & TMC & 7.41 \({}^{\text{a}}\)\(\pm\) 0.25 & 7.21 \({}^{\text{a}}\)\(\pm\) 0.16 & 7.03 \({}^{\text{a}}\)\(\pm\) 0.23 & 6.26 \({}^{\text{a}}\)\(\pm\) 0.01 & 6.29 \({}^{\text{a}}\)\(\pm\) 0.22 & 6.93 \({}^{\text{a}}\)\(\pm\) 0.14 \\  & _Lactobacillus spp._ & 4.18 \({}^{\text{a}}\)\(\pm\) 0.10 & 3.18 \({}^{\text{a}}\)\(\pm\) 0.18 & 4.21 \({}^{\text{a}}\)\(\pm\) 0.27 & 4.92 \({}^{\text{a}}\)\(\pm\) 0.59 & 4.80 \({}^{\text{a}}\)\(\pm\) 0.47 & 4.60 \({}^{\text{a}}\)\(\pm\) 0.28 \\  & Yeasts & 4.10 \({}^{\text{a}}\)\(\pm\) 0.30 & 4.20 \({}^{\text{a}}\)\(\pm\) 0.20 & 4.12 \({}^{\text{a}}\)\(\pm\) 0.33 & 3.90 \({}^{\text{a}}\)\(\pm\) 0.01 & 4.30 \({}^{\text{a}}\)\(\pm\) 0.10 & 4.20 \({}^{\text{a}}\)\(\pm\) 0.40 \\  & Molds & 2.30 \({
## 6 Conclusions and Future Research
The results presented in this paper provide some insights that can guide managerial decisions regarding the solution algorithm to be implemented in a specific situation. If the problem size is small (five jobs and five workstations), obtaining an optimal Pareto front can possibly be done in a reasonable computational time using the exact algorithm presented in [3]. For larger cases, the developed hybrid metaheuristic approach presented in this paper is a competitive candidate that can be used to generate efficient non-dominated solutions that are very close to the unknown optimal Pareto front for medium and large cases with low density. An alternative NSGA-II approach, as presented in [4], can provide efficient solutions for large cases with high density levels. Since the studied Bi-DMOSP is NP-hard, its future algorithmic development will remain open for more efficient approaches. Some algorithmic techniques developed in this paper, especially the local search mechanism, can be further refined and extended by incorporating more efficient neighborhood search functions. More specifically, the suggested neighborhood search functions used for improving the MWFT are at a different speed than those used for improving the makespan, since they require reconstructing the whole network representation of the problem to evaluate the objective value. Quicker methods for estimating the effect of the neighborhood moves on the MWFT can significantly reduce the computational time. This could improve the results of the developed MOSS for large-sized dense cases. Another direction for future algorithmic development is the consideration of an automated parameter-tuning approach, such as irace [51], which attempts to optimize the algorithm parameters as an integrated part of its execution. They can be compared with the static parameter-tuning approach followed in this paper. Other future research extensions can include the customization of the developed MOSS approach for more general cases of the studied problem, as well as similar multiprocessor shop-scheduling problems. For instance, the developed MOSS approach can be applied to multi-processor-flow shop-scheduling and flexible-job shop-scheduling problems. The structure of the studied Bi-DMOSP can be extended to include the cases of sequence-dependent setup times, planned maintenance activities, and the scheduling of material handling equipment. This research received no external funding. The dataset is available upon request from the author. The author declares no conflicts of interest.
## 2 Kinematic Constraint Equations
### Constraint Equations of Two-Wheelel Trailer (Robot) with Conventional Wheels  
The condition of moving without the slip of a conventional wheel means that the body-fixed lateral velocity \(V_{C\eta}\) is zero, Figure 3. Using expressions (4) and (7) yields:  
\[\mathbf{V}_{\rm S}+\mathbf{\Omega}\times\mathbf{r}_{\rm S}=-\mathbf{\omega}\times\mathbf{r}_{\rm K }\,. \tag{14}\]  
The constraint Equation (14) can be represent as follows:  
\[\begin{split} V_{S\xi}-l_{S}\hat{\chi}&=R_{\rm S} \hat{\theta}_{1}\,,\\ V_{S\xi}+l_{S}\hat{\chi}&=R_{\rm S}\hat{\theta}_{2} \,,\\ V_{S\eta}&=0\end{split} \tag{15}\]  
where \(\theta_{1}\) and \(\theta_{2}\) are the angles of rotation of the wheels.  
Taking into account (1), we obtain  
\[\begin{split}\dot{x}_{\rm S}&\cos\chi+\dot{y}_{\rm S} \sin\chi-l_{S}\,\hat{\chi}=R_{\rm S}\,\hat{\theta}_{1}\,,\\ \dot{x}_{\rm S}&\cos\chi+\dot{y}_{\rm S}\sin\chi+l_{S }\,\hat{\chi}=R_{\rm S}\,\hat{\theta}_{2}\,.\\ -\dot{x}_{\rm S}\sin\chi+\dot{y}_{\rm S}\cos\chi&=0.\end{split} \tag{16}\]  
Solving Equation (16) for \(\dot{x}_{\rm S}\), \(\dot{y}_{\rm S}\), and \(\dot{\chi}\) yields:  
\[\begin{split}\dot{x}_{\rm S}&=\ \frac{R_{\rm S}}{2}(\dot{ \theta}_{1}+\dot{\theta}_{2})\cos\chi\,,\\ \dot{y}_{\rm S}&=\ \frac{R_{\rm S}}{2}(\dot{ \theta}_{1}+\dot{\theta}_{2})\sin\chi\,,\\ \dot{\chi}&=\frac{R_{\rm S}}{2l_{\rm S}}(\dot{ \theta}_{2}-\dot{\theta}_{1})\,.\end{split} \tag{17}\]  
For the robot we have:  
\[\begin{split}\dot{x}_{\rm C}&=\ \frac{R_{\rm C}}{2}(\dot{ \varphi}_{1}+\dot{\varphi}_{2})\cos\psi\,,\\ \dot{y}_{\rm C}&=\ \frac{R_{\rm C}}{2}(\dot{ \varphi}_{1}+\dot{\varphi}_{2})\sin\psi\,,\\ \dot{\psi}&=\frac{R_{\rm C}}{2l_{\rm C}}(\dot{ \varphi}_{2}-\dot{\varphi}_{1})\,.\end{split} \tag{18}\]  
This system has three constraint equations: the first two constraints are non-holonomic (non-integrable) and the last constraint is holonomic.
## 4 Discussion
The higher HbA1c, the lower bone formation around implants (Figure 7b,c) and this finding might explain the result from the studies regarding to the lower resonance frequency analysis (RFA) of dental implants in clinical situation [17; 21]. Clinically, it is impractical to retrieve implant for histomorphometric analysis even though it is a more reliable method to observe the osseointegration phenomenon. Thus, a less invasive method to reflect the healing process of dental implant is to measure implant stability by RFA. Oates and colleges [17; 21] found that the implant stability of T2 DM with higher HbA1c was significantly lower than those with lower HbA1c and required longer time to return to baseline stability indicating that the healing capacity of hypergly condition in T2 DM was adversely affected. These findings supported the result of our study of the association between higher HbA1c and lower BIC and new bone formation which in turn demonstrated lower implant stability clinically. Previous studies showed that success of dental implants in diabetic patient ranged from 92% [44] to 97.2% [23], which was comparable to their healthy counterparts. Long-term clinical comparison was reported in a prospective study up to 12 years which showed no significant difference between T2 DM and controls for simple dental implants placement as well as more complicated cases such as sinus lift and bone augmentation [23]. However, other studies demonstrated the adverse effect of DM on dental implants in terms of success rate [25; 26; 27], osseointegration by resonance frequency test [17; 21], and peri-implant tissue such as increased bleeding on probing (BOP) [19] and peri-implantitis [28]. For these inconsistent reports of the clinical outcome of DM on dental implants, a few factors should be considered. Firstly, the study of success of dental implant in diabetic patients were conducted in different manners such as retrospective, prospective and cross-sectional studies as shown in recent literature reviews; therefore, a meta-analysis was not possible [29; 45]. Secondly, not all the studies stated the type of their diabetic patients whether they are type 1 or type 2. In addition, the status of their glycemic control was not clearly defined; for instance, most authors considered HbA1c below 8% well-controlled while some others accepted less than 8%, used other parameter such as fasting plasma glucose, or did not even report the glycemic status of the participants. To our best knowledge, this study is the first clinical study that compared histologically osseointegration between healthy and T2 DM patients. From the results of our study, dental implants in T2 DM patients had lower osseointegration although all implants show 100% success clinically within our study period; therefore, long-term follow-up is required.
## 6 Profound Impact Potential on Decision-Making Processes
### Leveraging AI for Climate Research  
IBM's collaboration with NASA demonstrates the growing role of artificial intelligence in advancing climate research and Earth system science. With a partnership legacy dating back to 1949, supporting programs such as Apollo and various Earth science missions, IBM has played an instrumental role in space exploration and data analysis. In 2022, IBM and NASA formalized their collaboration under a Space Act Agreement to address global climate challenges. A major outcome of this partnership is the development of the HLS Geospatial Foundation Model (HLS Geospatial FM), trained on NASA's extensive geospatial datasets, including Harmonized Landsat and Sentinel-2 imagery. Leveraging IBM's Vela supercomputer infrastructure, this model significantly enhances the efficiency of geospatial data analysis, enabling critical applications such as flood prediction, land-use monitoring, and disaster response [41].  
Key technical advancements include:  
* A 15% improvement in accuracy compared to traditional models.
* A fourfold increase in geospatial analysis speed, achieved with reduced reliance on labeled training data.
* Broad applicability to tasks such as agricultural impact analysis, deforestation monitoring, and wildfire tracking.  
In alignment with open science principles, IBM has made the HLS Geospatial FM available on Hugging Face, fostering global collaboration and accelerating innovation in Earth observation.
## 2 Materials and Methods
### Water Cloud Model  
Agricultural regions are a combination of vegetation and bare soil and the information obtained from optical or radar remote sensing images is influenced by both components. The water cloud model is based on the semi empirical model established by the theory of microwave remote sensing radiation transmission. This model considers that the signal received by the radar sensor is composed of soil and vegetation and their mutual scattering [17]. It was initially proposed by [PERSON] and [PERSON] [16] and there are multiple ways of implementing the model [33]. [PERSON]'s version was used in this study, can be described with the Formulas (1)-(3) [34].  
\[\sigma^{0}_{pq}=\sigma^{0}_{vcg}+\sigma^{0}_{soil}, \tag{1}\]  
where \(\sigma^{0}_{vcg}\) and \(\sigma^{0}_{soil}\) represent the part of overall backscatter coefficient, \(\sigma^{0}_{pq}\), from vegetation and soil, respectively. The values \(p\) and \(q\) represent different polarizations of radar.  
\[\sigma^{0}_{vcg}=A*Veg*cos\theta(1-\exp(-2*B*Veg*sec\theta)), \tag{2}\]  
\[\sigma^{0}_{soil}=\sigma^{0}_{pq\_soil}*\exp(-2*B*Veg*sec\theta), \tag{3}\]  
where _Veg_ represents crop related parameters (in this study, we used _NDVI_ calculated from optical data), \(\theta\) represents the detection angle of radar, and \(A\) and \(B\) represent the vegetation parameters. The value \(\sigma^{0}_{pq\_soil}\) represents the initial backscatter from the soil and we assumed that it has linear relationship with soil moisture in this study.  
\[\sigma^{0}_{pq\_soil}=C*SM+D, \tag{4}\]  
where \(C\) and \(D\) are soil parameters. Combined with the above formula, we can get the formula of soil moisture.  
\[SM=\frac{\left(\frac{\sigma^{0}_{pq}-A*Veg*cos\theta(1-\exp(-2*B*Veg*sec\theta ))}{\exp(-2*B*Veg*sec\theta)}\right)-D}{C}. \tag{5}\]  
In this study, we used field measured soil moisture as a model input parameter, combined with backscattering and vegetation information, to train the model. As can be seen in Equation (5), we used field measured soil moisture data to represent _SM_ and _NDVI_ retrieved from Sentinel-2 to represent _Veg_ and \(o_{pq}^{0}\) and \(\theta\) came from Sentinel-1 backscattering coefficient and the Sentinel-1 incidence angle, respectively. We have 110 field experimental sites, half of which were used for training the water cloud model and half for validating the soil moisture retrieval accuracy. Thus, we will have 55 equations with the form of Equation (5) and A, B, C, and D are vegetation and soil parameters to be determined. These are multivariate non-linear overdetermined systems of equations and we solved the equations using MATLAB software.
## 2 Results and Discussion  
The multichannel DAD HL uses provides (_i_) an ultra-wide dynamic range, ideal for the simultaneous detection of major compounds and trace level impurities; (_ii_) data acquisition up to 200 Hz using up to 10 absorption channels; (_iii_) one spectral field delivers the best support of ultrafast separations; and (_iv_) multichannel data acquisition that permits the simultaneous determination of compounds with different optimal wavelengths of detection [40]. In this context, one run of DAD analysis guarantees, besides the analysis efficiency, a complex evaluation of low amounts of sample and a low cost and rapid analysis.
# Influence of Aging Conditions on the Dynamic Stiffness of EPDM and EVA Rail Pads
## 4 Results
### Type A Pads  
Dynamic stiffness curves obtained from tests on the various conditions of pad A, conducted under the aforementioned reference parameters, are depicted in Figure 8. The results indicate that mechanical degradation increases the stiffness of the pads as the number of cycles increases, while exposure to UV radiation causes a decrease in stiffness. In the case of hydrocarbons, a mixed effect is observed: the condition with higher exposure increases stiffness, whereas that with lower exposure reduces it.  
The boxplot diagram presented in Figure 9 illustrates the variation in dynamic stiffness obtained for the different combinations of test parameters compared to the value obtained in the reference pad, allowing visualization of the distribution of stiffness variation for each case and condition. It is observed that the influence of mechanical degradation progressively intensifies, with mean increases in dynamic stiffness of 2%, 3.5%, and 17% for M1, M2, and M3, respectively, while exposure to UV light reduces dynamic stiffness by 5.5% and 5% for UV1 and UV2, respectively, thereby increasing the dispersion relative to the effects of mechanical degradation. On the other hand, exposure to hydrocarbons results in an average variation in dynamic stiffness of \(-11.5\%\) for HC1 and \(-3\%\) for HC2; in the latter case, a high dispersion in the data is observed, with some measurements showing increases in dynamic stiffness of over 20%, while others exhibit decreases of more than 20%.  
To explain the greater dispersion observed in the chemical degradation results, especially in cases of higher exposure, a scatter diagram is presented in Figure 10 that correlates the rail pad stiffness with the tow load and the maximum load applied during dynamic tests. The stiffness variation is represented using a color scale. This diagram reveals that in the case of UV radiation exposure, an increase in stiffness is observed with the application of low loads, whereas as the load increases, the stiffness decreases. Moreover, this behavior intensifies as exposure to UV light increases.  
A scatterplot similar to Figure 10, used to analyze the exposure of the same rail pads to hydrocarbons, has already been presented and studied by the same authors in a previous publication [20].  
Figure 8: Dynamic stiffness curves obtained from tests under different conditions for pad A.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 5 Post-Processing and Parameters Extractions
### Time Dispersion Parameters  
All-time dispersion parameters were measured relative to the time of arrival of the first multipath component (the LoS component in the LoS scenario). The PDP was normalized, and all the signals above a \(X\) dB threshold, where \(X\) is set to 50 dB, were all considered MPCs. Examples of the normalized PDP at the maximum Tx-Rx separation distance of 5 m in the three industrial environments are shown in Figure 6a-c.  
The time dispersion of ultra-wideband signals can be measured from the ratio of the mean excess delay (MED)-to-the RMS delay spread (RMSDS). Here, this ratio represents the spreading factor and is defined as [58]:  
\[\mathrm{SF}=\frac{\tau_{MED}}{\tau_{RMSD}} \tag{8}\]  
\(\tau_{MED}\) and \(\tau_{RMSD}\) are the MED and RMSD and can be calculated using (9) and (10), respectively.  
\[\tau_{MED}=\frac{\sum_{l}P(\tau_{l})\tau_{l}}{\sum_{l}P(\tau_{l})}, \tag{9}\]  
\[\tau_{RMSD}=\sqrt{\overline{\tau^{2}}-(\tau_{MED})^{2}}, \tag{10}\]  
where:  
\[\overline{\tau^{2}}=\frac{\sum_{l}P(\tau_{l})\tau_{l}^{2}}{\sum_{l}P(\tau_{l} )}, \tag{11}\]  
\(\overline{\tau^{2}}\) is the second central moment of the PDP, and \(P(\tau_{l})\) is the received power at the \(l\)th multipath. The RMSDS provides a measure of the variability of the mean delay, and it is inversely proportional to the coherence bandwidth, which indicates the frequency selectivity of a channel and corresponds to the maximum channel bandwidth that can be used without the production of large ISI at the Rx. The estimation of this factor is important because the duration of each symbol or cyclic prefix for orthogonal frequency division multiplexing (OFDM) modulation must be considerably longer than the RMS delay spread to prevent ISI at the Rx.