esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Model Limitations and Future Directions
#### 5.1.2 Misclassified Objects  
The identification of objects within the system is not without error. There is a paucity of misclassifications of objects. These are a result of the complexity and originality of individual cases, which differ significantly from objects in the environment. They are influenced by unusual roof forms, elements located on roofs, and the high density of buildings. In order to resolve this issue, it would be beneficial to augment the number of objects in the prepared data for the YOLO model and to establish an additional class to accommodate the misidentified objects.  
Advanced segmentation methods are planned to improve the effect of overlapping objects on densities. The use of semantic segmentation through integration with models such as FCN (Fully Convolutional Networks) or instance segmentation (Mask R-CNN method) can improve the results.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 4 Discussion
Developing a framework to automatically map land use features would benefit mapping programs in Australia and globally. The automated and efficient classification of land use features from high-resolution imagery will be extremely valuable in responding to current and future biosecurity incidents as well as other events requiring a rapid response such as natural disasters. This will have applications in agricultural productivity and sustainability, land use planning, natural resource condition monitoring and investment, biodiversity conservation, and improving water availability and quality [14].
# Multilevel Bipolar Electroforming-Free Resistive Switching Memory Based on Silicon Oxynitride
## 3 Results and Discussion
### Electrical Characteristics  
The I-V characteristics of the fabricated Au/Ni/SiO\({}_{x}\)N\({}_{y}\)/p\({}^{+}\)-Si device were obtained by cycling the applied DC bias voltage according to the sequence of 0 V \(\rightarrow\)+7 V \(\rightarrow\)0 V \(\rightarrow\)-7 V\(\rightarrow\)0 V with a voltage step of 50 mV. The compliance current (I\({}_{\text{COMP}}\)) was set to 0.25 mA for positive voltage bias. The multilevel switching operation of the 10-nm-thick SiO\({}_{x}\)N\({}_{y}\) based device (blue) is shown in Figure 2.  
Figure 1: Fourier transform infrared (FTIR) absorbance spectra of silicon oxynitride film.  
The stability of the IRS was confirmed by cycling 0 V \(\rightarrow\)+6.5 V \(\rightarrow\)0 V \(\rightarrow\)\(-\)7 V\(\rightarrow\)0 V, which is shown in red. The I-V characteristics exhibit a pronounced electric hysteresis, which is a signature of resistive switching. In particular, it was observed that bipolar multilevel resistive switching can be obtained by controlling the magnitude of the applied voltage.  
In the positive bias voltage region, the device was switched from the initial HRS to IRS at around +6 V, which is called V\({}_{\text{IRS}}\). As the higher positive bias voltage was applied to the device in the IRS, it leads to a sudden increase of current to the compliance current limit at around +7 V which is the LRS. In the negative bias voltage region, the current decreases abruptly when the applied voltage reaches around \(-\)1 V, leading to the first RESET process from the LRS to the IRS. As the negative bias voltage reaches \(-\)6 V, the second RESET process occurs from the IRS to the HRS.  
The first SET and last RESET voltages of the full cycle of the I-V curve was almost the same as those of the intermediate cycle of the I-V curve. Additionally, the resistance of the initial state and HRS after the second RESET process are also the same (\(\sim\)10\({}^{7}\)\(\Omega\)), which demonstrates the electroforming-free operational characteristics [24]. Detailed analysis and reasoning on the resistive switching, asymmetric behavior and electroforming-free behavior are discussed in the next subsection.
# Floating Step-Down Converter with a Novel Lossless Snubber
## 3 Power Component Design
### Design of Resonant Tank Components \(C_{r}\) and \(L_{r}\)  
As compared with the original circuit without the snubber, the design aims to extend the time \(\Delta t_{43}\) so that the resonant capacitor is slowly discharged, thereby making the slope of the rise in the voltage \(v_{ds}\) on the main power switch \(Q_{1}\) slower. However, \(\Delta t_{43}\) should not be longer than the cutoff time of \(Q_{1}\). As the output capacitor of \(Q_{1}\), \(C_{oss}\), is charged, when there is no snubber, the peak inductor current \(l_{Lo,peak}\) is rapidly charged to \(C_{oss}\), while the proposed circuit is charged to the equivalent \(C_{eq}\) as shown in (48), so we hope that \(C_{eq}\) can be much larger than \(C_{oss}\), that is,  
\[C_{eq}=C_{oss}+C_{r}>10C_{oss} \tag{34}\]  
By considering the worst case of (35), substituting the light-load \(I_{Lo,peak}\) into (35) can obtain the inequality of the value of \(C_{eq}\), as shown in (36):  
\[\Delta t_{43}=\frac{V_{in}C_{eq}}{I_{Lo,peak}}<~{}(1-D)T_{s} \tag{35}\]  
\[\begin{array}{ll}C_{eq}&<\frac{(1-D)(I_{Lo,peak}+\frac{\Delta t_{Eq}}{2})}{ V_{in}f_{s}}\\ &=\frac{0.55(0.4+0.2475)}{300\times 100~{}\mathrm{k}}=11.87~{}\mathrm{nF} \end{array} \tag{36}\]  
Eventually, the resonant capacitor \(C_{r}\) is selected to be two 1 nF Ycap capacitors connected together.  
In order to allow the current in the resonant inductor to be demagnetized before the cutoff of the switch, it is necessary to meet the requirements as shown in (37):  
\[\frac{1}{\omega_{a}}\left[\cos^{-1}(\frac{-V_{o}}{V_{in}-V_{o}})+\frac{\sqrt{ V_{in}^{2}-2V_{in}V_{o}}}{V_{o}}\right]<DT_{s} \tag{37}\]  
By substituting the system specifications in Table 1 and the designed value of \(C_{r}\) into (37), we can obtain  
\[\begin{array}{ll}\sqrt{L_{r}C_{r}}[2.529+0.703]<~{}4.5\times 10^{-6}\\ \Rightarrow L_{r}<969.29~{}\mathrm{\mu H}\end{array} \tag{38}\]
## 4 Discussion
### The Influence of Slim Straight Electrode Insertion Depth on NRT and PSA  
#### 4.2.1 Electrode Insertion Depth and NRT  
NRT thresholds depend on the proximity of the electrode array and the spiral ganglion cells. In the apex, the diameter of the cochlea is smaller, and the electrode array converges to the spiral ganglion cells, hence NRT thresholds are lower in the apex than in the base and that is why an electrode with a greater insertion angle can be assumed to correlate with a smaller NRT ratio [10; 16; 17]. In our research, we found that the slim straight electrode insertion depth is inversely proportional to the average of all NRT thresholds. This means that the deeper the electrode insertion depth the lower the NRT, although the spiral ganglion cells are in the cochlea only up to a depth of 17 mm [18]. Certainly, the length of the cochlea varies individually. Another study confirmed the importance of electrode positioning in relation to the modiolus in the stimulation of the auditory nerve fibers [19]. [PERSON] et al. [13] demonstrated that NRT thresholds depend on the depth of the insertion of the electrode into the cochlea, the size of the cochlea, the number of residually functional neurons, and the duration of hearing loss. Contrarily, [PERSON] et al. [10] did not prove the correlation between a greater insertion angle and a smaller NRT ratio.
# A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms
## 2 Literature Review
### Classification Algorithm  
Various machine learning models have been explored in fault diagnosis to detect the occurrence of faults. There are two broad categories: supervised and unsupervised algorithms. The former is the dominant and more widely used method. The important difference between supervised machine learning and unsupervised machine learning is the existence (or lack thereof) of a training set that has a corresponding target output with multiple given inputs [22].  
Supervised learning has been widely used in the field of fault diagnosis. [PERSON] et al. proposed a new hybrid method of random forest classifiers and applied it to the fault diagnosis of rolling bearings. Experiments show that their method has high diagnostic accuracy, but this method can only diagnose a single fault [6]. [PERSON] et al. improved the C4.5 decision tree and performed fault classification by extracting fault features in the brake system. In the application scenarios of big data, the classification accuracy of the improved algorithm has been greatly improved [23]. [PERSON] et al. studied the superconducting fault current limiter and proposed a support vector machine fault diagnosis method, which was applied to a nonlinear regression between AC current and AC voltage [24]. However, this study uses an image-oriented feature extraction method, which is very time-consuming.  
Other scholars applied unsupervised learning to the field of fault diagnosis. [PERSON] et al. proposed a fault diagnosis method for the analysis of dissolved gases in power transformers based on association rules and compared it with K-nearest neighbor (KNN), SVM, and other algorithms [25]. [PERSON] et al. used frequent pattern growth (FP-Growth) to propose a method for locating and diagnosing branch line faults in a distribution network with multiple data sources [26]. [PERSON] et al. proposed a method of using pattern growth to mine fault tolerant frequent patterns. They stored the original data set in a highly concentrated environment, which avoided multiple scans of the data set, and used algorithms such as Apriori for comparison [27]. [PERSON] et al. used the FP-Growth algorithm to increase the speed of rule mining for new crown virus diagnosis, but a certain amount of memory overhead was generated during rule generation [28].  
Compared with the classification algorithm used in the above study, the combination of fault rules and algorithms is used in this study, which is more accurate than the study using only rules, and it improves the interpretability of supervised algorithms.
## 4 Simulation and Results
### Number of Active Readers:  
\[\begin{array}{c}AR=\sum_{i=1}^{P}\sum_{j=1}^{TS}\frac{\mathit{TDMA\_Schem(i,j)}}{P}\\ \left\{\begin{array}{l}AR\ \mathit{Maximize}\\ TS\ \mathit{Minimise}\end{array}\right.\end{array} \tag{1}\]  
The following formula illustrates the dynamic interaction between simulation periods, time slots, and the reuse of time slots by multiple readers. Here, \(\mathit{TDMA\_Schem(i,j)}\) denotes the number of readers simultaneously accessing a particular time slot during the \(i\)th simulation period, with \(P\) representing the total number of simulation periods and \(TS\) representing the available time slots.  
Furthermore, our objective functions, stipulating the maximization of active readers while potentially minimizing the number of time slots \(TS\), highlight the trade-off between system performance and resource allocation. This optimization framework enables the design and implementation of TDMA schemes tailored to specific operational requirements, balancing throughput and resource utilization in RFID communication systems.
###### Abstract
It was determined that Jet A-1 aviation fuel can be used in diesel engines when mixed with diesel fuel, resulting in a decrease in ignition delay time and NOx emissions and an increase in brake torque and C.O. emissions. [PERSON] et al. [24] conducted an experiment to compare the performance of gas turbines running on biofuels and methane using a mathematical model of a gas turbine via GateCycle software. Although the decrease in performance was confirmed when using biofuels, the overall efficiency has increased. The test of [PERSON] et al. [25] was focused on the impact of alternative aviation fuels on the performance of a small-scale turbojet engine. Three types of fuels were used for the experiment--Jet-A kerosene, a synthetic Gas to Liquid (GTL), and a blend of Jet-A with 30% of Jatropha Methyl Ester (JME). The overall performance characteristics were fairly similar. With the JME, there is a need for improvements in the freezing point before its use in aviation. As expected, GTL and JME showed a decrease in emissions. When it comes to FAME fuel, which is also the subject of this paper, it is important to mention its certification status. The implementation of alternative aviation fuels for use in blends with conventional aviation kerosene requires extensive testing. To guarantee appropriate engine performance, fuel compatibility with aircraft systems, the safety of storage, and optimal fuel performance during all aircraft operations, extensive assessments are necessary. The ASTM controls the fuel standards for the aviation industry. FAME fuel additives so far have not been approved as safe for use in blends with any type of aviation fuel, as it is often regarded as a pollutant in jet fuel and is only allowed at the proportion of 5 mg/kg [26]. This is due to the fact that FAME changes fuel's physical characteristics. There are even rising concerns across the aviation industry about the possibility of the accidental contamination of jet fuel in facilities that carry multiple fuels, as the demand for FAME additives to diesel is rising [27]. On the other hand, FAME can have some advantages, as they, in contrast to other alternative fuels, have oxygen in their molecular structure [28] and therefore lower the fuel's carbon content, which in turn lowers its carbon footprint and also lowers the fuel's soot emissions and hence can lower particulate matter emissions by roughly 40%. These positive characteristics have already been demonstratedin experimental and computational studies and therefore give a base for other studies to be conducted and look further for its potential use as an additive to aviation fuels [29]. The aim of the experimental measurements carried out is to determine the influence of various mixtures of FAME biofuel and Jet A-1 aviation kerosene on the operating characteristics of an experimental small jet engine in the specialized laboratory of small jet engines at the Department of Aviation Engineering at the Faculty of Aeronautics of the Technical University of Kosice.
## 3 Methodology
### Reliability Assessment Method for Series Systems Under No-Failure Scenarios
#### 3.1.2 Some Components Are Identical  
In the case of a series system containing several identical components, these components can be combined into a single equivalent component, and its equivalent test time can be calculated according to Theorem 2.  
**Theorem 2**.: _Assuming that there are \(m^{*}\) (\(1<m^{*}<m\)) identical components and the GTT of these components is \(T_{0}=\sum_{i=1}^{m_{\mathrm{r}}}T_{i}\) (no failure), it is equivalent to treating the \(m^{*}\) identical components as a whole and conducting a test with an equivalent GTT of \(T_{0}^{*}\) (no failure). \(T_{0}^{*}\) is calculated by the following equation:_  
\[T_{0}^{*}=T_{0}/m^{*} \tag{21}\]  
**Proof of Theorem 2.** Let the Weibull shape parameter of identical components be \(\alpha_{1}\); then, the corresponding reliability is defined as \(R_{1}(t)\), and \((\alpha_{1},T_{0})\) represents the zero-failure data of these components. According to Equation (6), the LCL of \(R_{1}(t)\) at the confidence level \(\gamma\) is  
\[R_{\mathrm{LL},\gamma}(t)=\exp\Bigl{\{}-\chi_{\gamma}^{2}(2)t^{\alpha_{1}}/ \left(2T_{0}\right)\Bigr{\}} \tag{22}\]  
For a new component consisting of \(m^{*}\) identical components in series, the LCL of its reliability \(R^{*}(t)=R_{1}^{m^{*}}(t)\) at the confidence level \(\gamma\) is  
\[R_{\mathrm{L},\gamma}^{*}(t)=R_{\mathrm{LL},\gamma}^{m^{*}}(t)=\exp\Bigl{\{}- \chi_{\gamma}^{2}(2)m^{*}t^{\alpha_{1}}/(2T_{0})\Bigr{\}} \tag{23}\]  
If these components are treated as a whole and subjected to a life test resulting in zero failure with a GTT of \(T_{0}^{*}\), as mentioned in Section 2.1, the lifetime of a series system consisting of identical components obeys a 2-parameter Weibull distribution with a constant  
Figure 1: Equivalence of LCL curves for reliability and reliable life.  
shape parameter \(\alpha_{1}\). Therefore, the LCL of its reliability \(R^{*}(t)\) at the confidence level \(\gamma\) can still be obtained from Equation (6):  
\[R^{*}_{\text{L},\gamma}(t)=\exp\Bigl{\{}-\chi_{\gamma}^{2}(2)t^{\alpha_{1}}/(2T_ {0}^{*})\,\Bigr{\}} \tag{24}\]  
According to the same principle of reliability confidence limits, the values of \(R^{*}_{\text{L},\gamma}(t)\) obtained from Equations (23) and (24) should be equal. By comparing the results of these two equations, Theorem 2 is proved. \(\square\)  
Subsequently, the new equivalent component with a GTT of \(T_{0}^{*}\) is connected in series with the remaining \(m-m^{*}\) components, forming a series system with \(m-m^{*}+1\) different components. Figure 2 illustrates the simplification of the system reliability model when identical components are present. The simplified configuration satisfies the conditions required for the proposed method in Section 3.1.1. Finally, the LCLs for the reliability and reliable life of the series system can be calculated using Equations (8) and (20), respectively.
# Practical Grammar Compression Based on Maximal Repeats +
## 3 Analysis of RePair
### MR-Order
: _If there are several distinct pairs with the same maximum frequency, the size of the grammar generated by RePair depends on their replacement order._  
However, the following theorem states that the MR-order rather than the replacement order of pairs determines the size of the grammar generated by RePair. **Theorem 2**. : _The sizes of grammars generated by RePair are the same if they are generated in the same MR-order._  
Proof. : Let \(T\) be a variable sequence appearing in the grammar generation process of RePair and \(f\) be the maximum frequency of pairs in \(T\). Suppose that \(T^{\prime}\) is a variable sequence generated after RePair replaces every pair occurring \(f\) times. According to Theorem 1, all generated \(T^{\prime}\) are isomorphic to one another, then the length of all of them is the same, regardless of the replacement order of pairs. Let \(r_{1}\) be the most frequent maximal repeats of \(T\) with \(r_{1}\) preceding all other maximal repeats in this MR-order. As a result, \(r_{1}\) is converted into a variable, and according to Lemma 2, all pairs included in \(r_{1}\) are distinct. Then, the size of the subgrammar which exactly derives \(r_{1}\) is \(2(|r_{1}|-1)+1=2|r_{1}|-1\). This holds for the next prioritized maximal repeat (we denote it as \(r_{2}\)) with the following slight difference: the pattern actually replaced would be a substring of \(r_{2}\) excluding its beginning or end if there are occurrences of overlap with \(r_{1}\). However, these strings are common in the same MR-order. Then, the sizes of generated subgrammars are the same, regardless of the order of selecting pairs. Similarly, this holds for all most frequent maximal repeats and every maximum frequency of pairs through the entire process of RePair.
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 4 Bearing Capacity under Eccentric Load
### Parameter Analysis of Bearing Capacity
#### 4.2.2 Influence of Steel Wedge Length  
The relationship between steel wedge length and the bearing capacity of the DC joints is revealed in Figure 12a. Bearing capacity is significantly affected by steel wedge length; the bearing capacity increases with steel wedge length. When the length exceeds 400 mm, it will no longer affect bearing capacity. Figure 12b shows the effect of steel wedge length on initial compression stiffness and yield load. When steel wedge length is less than 350 mm, the initial compression stiffness and yield load increase with steel wedge length. For every increase of the length by 50 mm, the initial compression stiffness and yield load increase by 115.8 kN/mm and 412.5 kN, respectively. Steel wedge length weakly affects bearing capacity as the length exceeds 350 mm. When the steel wedge length exceeds 400 mm, the length will no longer be the influential factor on the initial compression stiffness and  
Figure 11: Impact of eccentricity on bearing capacity.  
yield load. The increase of the length of the steel wedges is ineffective with respect to the compression area of the steel wedges.
## 3 Results and Discussion
### Ventilation Rate Determinants
#### 3.3.2. Ventilation Rates vs. VOC Emissions  
The ventilation requirements to dilute indoor VOC emissions from materials exposed to ozone at various concentration levels were predicted using Equation (9) and are shown in Figure 8.  
Two major findings can be drawn from Figure 8. First, the ventilation requirements were heavily dependent on the air volume required to dilute the formaldehyde concentrations. In other words, formaldehyde was the ventilation rate determinant for all the cases using wood-based panels, as well as the flannel, since all the above materials emitted and/or produced a considerable amount of formaldehyde at almost all three ozone concentration levels. Only when formaldehyde was absent or low were the ventilation requirements determined by other pollutants, such as xylene. Notably, many pollutants that may end up as determinants were not measured during the VOC analysis. Previous studies, such as the study in [60], showed that formaldehyde can be a serious problem in indoor environments in China, especially in newly-built/decorated environments. This paper provides further evidence that formaldehyde can be frequently detected at concentrations that require high ventilation rates on materials sold in Chinese markets.  
Second, by combining Figures 7 and 8, the results indicate that when ozone concentration in the outdoor air is not severely high, it is possible to use ventilation to dilute indoor VOC emissions while maintaining an acceptable indoor ozone concentration. However, when the outdoor ozone concentration is too high, directly using ventilation as a means to dilute indoor VOC emissions can be  
Figure 8: The ventilation requirements to dilute indoor VOC emissions with or without exposure to ozone. (**a**) Using wood-based panels as the indoor material. (**b**) Using synthetic fibers as the indoor material.  
counter-productive because indoor ozone pollution would still be considered harmful to the occupant's health. Lower ventilation requirements for VOC dilution (such as those of leather and lining) seem to better reduce ventilation to cope with outdoor ozone pollution.
## 3 Monitoring and Detection Technology of SF\({}_{6}\) and Its Decomposition Components
### Ion Migration Spectrometry (IMS) Method  
Ion mobility spectrometry is a kind of monitoring method for separating and measuring objects according to the difference in ion mobility under environmental pressure. It has the advantages of fast response time, high sensitivity, low cost, and simple operation. IMS has been used to monitor the purity and quality of SF\({}_{6}\) in gas-insulated switchgear. The drift tube is filled with sample gas, and the mass of the gas is sensed by the IMS peak displacement caused by various ions formed by impurities.  
[PERSON] et al. [54] studied a self-built IMS device that uses drift gas as clean air to monitor SF\({}_{6}\) in ambient air in real time. The leakage simulation of SF\({}_{6}\) was carried out to control the start-up and leakage of SF\({}_{6}\) manually. The final measurement has a temporal  
Figure 5: (**a**) Experimental setup diagram. (**b**) Raman spectra of 0.5 kPa \({}^{12}\)CO\({}_{2}\) and \({}^{13}\)CO\({}_{2}\). (**c**) Raman spectra of 0.5 kPa CO. Adapted with permission from [10].  
resolution of less than 1 s and a detection limit (LOD) of 0.16-0.68 ppmv. IMS has great application potential in monitoring SF\({}_{6}\) concentration in the environment.