esa-sceva/satcom-chunk-collection huggingface dataset:
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 6 Experiment
### Model Variations
#### 6.5.1 The Effect of Hyperparameter \(\alpha\) on the Model  
In our experiment, we chose the representative dataset, SWAT, to analyze the impact of \(\alpha\) in the model. \(\alpha\in\{0,0.2,0.4,0.6,0.8,1\}\). In Equation (7), \(\alpha\) is the proportion of reconstruction loss \(G_{loss}\) and discrimination loss \(D_{loss}\) in the anomaly score. For this experiment, we kept the other hyperparameters constant, while setting the number of transformer layers to four and the sliding window length to 60. In SWAT, when \(\alpha\) is set as 0.2, all the metrics showed the best performance. With the increase of \(\alpha\), _Precision_, _Recall_, and \(F1\) decreased dramatically, and \(AUC\) and \(PRAUC\) also decreased.  
As show in Figure 6, with \(\alpha=0\), the anomaly score only depended on \(D_{loss}\). Almost all the results with \(\alpha=0\) were higher than the average performance of all the experiments and ranked only second to the results with \(\alpha=0.2\), and only the \(AUC\) value was slightly higher with \(\alpha=0.2\). It indicates that the anomaly score depends more on the discrimination loss, and a small quantity of the reconstruction loss can improve the overall performance. With \(\alpha=1\), it indicates that the anomaly score depends only on \(G_{loss}\). The results with \(\alpha=1\) were much lower than the results with other \(\alpha\) settings. It shows that reconstruction loss has a weak impact on the anomaly score of TGAN-AD.  
Figure 6: These line graphs show the effect of setting different hyperparameter \(\alpha\) on each metric, \(\alpha\in\{\,0,0.2,0.4,0.6,0.8,1\}\).
## 4 Progress of Data Processing Modules for Solar PV Monitoring System
### Raspberry Pi-Based Module
Further modifications such as flexibility in the operating system as well as the processing application could lead to future expansion. Raspberry Pi is one of the most effective data processing modules in the field of monitoring systems. However, it does have some technical drawbacks such as, it does not have a Real-Time Clock (RTC) with a backup battery, and it has a high power consumption and weight [65]. Moreover, it lacks a Basic Input Output System (BIOS), and thus it always boots from an SD card. Besides, it does not have a built-in Analog to Digital (AD) converter and hence, an external component must be used for AD conversion [66].
# Concentrating Solar Collectors for a Trigeneration System--A Comparative Study
## 3 Results and Discussion
### Monthly and Yearly Performance of the Studied Cases
In December, the system energy efficiency was 31.86%, 4.47% and 60.20% with the PTC, LFR and DISH, respectively. The system performance with the LFR was extremely low during the winter, something that makes its application face important limitations in Greece during the winter period. At the end of the results section, Table 4 summarizes the yearly results of the system performance. The yearly energy performance was found to be 64.40%, 35.78% and 62.41% with the PTC, LFR and DISH, respectively. The yearly exergy performance was found to be 16.63%, 9.24% and  
Figure 11: Monthly cooling production for the three solar collectors. Figure 12: Monthly system energy efficiency for the three solar collectors. 16.12% with the PTC, LFR and DISH, respectively. The investment simple payback period was found to be 6.25, 10.92 and 6.95 years with the PTC, LFR and DISH, respectively. The results indicate that the most effective technologies for the trigeneration system are the PTC and the solar dish. The LFR was found to be the least efficient system and the system which led to the maximum payback period, thus it is better not to be selected for driving trigeneration configurations. The PTC was the best system, and the dish led to slightly lower performance results. So, both these technologies could be selected for feeding trigeneration systems with heat. In the future, there is a need for performing similar comparative works for other locations with different weather data and especially different latitudes. An interesting question for future work is associated with the best solar technology at higher altitudes than Athens, which is 38\({}^{\circ}\), in order to check if the PTC remains the best choice or not. The higher latitude may introduce higher optical losses in the PTC which is a linear concentrating technology, and this is the reason for investigating this issue further. Moreover, it must be stated that the solar-driven trigeneration systems are viable in locations with relatively high solar potentials in order to produce high amounts of useful output and to have a high yearly income. Lastly, regarding the LFR, there is a need to develop an advanced design with lower optical losses in order to make it a more competitive technology energetically. More specifically, there are ideas with rotating platforms, extended receivers and inclined receivers which can lead to higher optical and consequently thermal performance. In any case, the respective increase in the cost has to be taken into consideration in every case.
# Analytic Combinatorics for Computing Seeding Probabilities
## 4 Results
### Substitutions and Deletions
Now, substituting the obtained value in Equation (16) gives \(C\approx 1.096177\), so the probability is approximately \(1.096177/1.006705^{101}\approx 0.558141\). For comparison, a 99% confidence interval obtained by performing 10 billion random simulations is \(0.55813-0.55816\)._  
Once again, the analytic combinatorics estimates are accurate. Figure 6 illustrates the precision of the estimates for different values of the deletion rate \(\delta\) and of the read size \(k\). The probabilities can also be computed by recurrence using Theorem 3, after replacing \(F_{\gamma}(z)\) by \(qz\big{(}1-((1-\delta)qz)^{\gamma-1}\big{)}/(1-(1-\delta)qz)\) in expression (15). Denoting \([z^{k}]S_{\gamma}(z)\) as \(s_{k}\), one obtains for every integer \(\gamma>1\)  
\[s_{k}=\left\{ \begin{array}{ll}1,&\text{if $0\leq k<\gamma$,}\\ 1-(1-\delta)^{\gamma-1}q^{\gamma},&\text{if $k=\gamma$,}\\ s_{k-1}-\delta q^{\gamma}(1-\delta)^{\gamma-1}\cdot s_{k-\gamma}-pq^{\gamma}(1 -\delta)^{\gamma}\cdot s_{k-\gamma-1},&\text{if $k>\gamma$.}\end{array} \right. \tag{17}\]  
Figure 6: _Example estimates for substitutions and deletions._ The analytic combinatorics estimates of Proposition 3 are benchmarked against random simulations. Shown on both panels are the probabilities that a read of given size contains a seed, either estimated by 10,000,000 random simulations (dots), or by Proposition 3 (lines). The curves are drawn for \(\gamma=17\), \(p=0.05\) and \(\delta=0.14\), \(\delta=0.15\) or \(\delta=0.16\) (from top to bottom).
## 2 Materials and Methods
### Validation with Experimental Data  
In order to corroborate that the CFD simulations have adequate accuracy, the results obtained by the simulations must be validated. For this purpose, the simulation results should be compared with the experimental results. As the current work is done and simulated by the same CFD software EURANUS (NUMECA), the same turbulence model (S-A) and the same boundary conditions as the work done by [PERSON] et al. [33], the validation reported in their research work is used for validating the numerical simulation in this study. They compared the overall performance of the CFD results and experimental results. The comparison of the isentropic efficiency and static as well as total pressure predicted by the CFD simulation and the experiment data are presented in Figure 9. The highest deviation between the CFD simulation result and the experimental results for the isentropic efficiency is 0.81%, which indicates that the CFD has adequate accuracy for simulating the flow. Furthermore, the comparison of the static and total pressure of the experiment data and CFD simulation shows that the results with CFD are consistent with the experimental data.  
The meridional velocity and the relative velocity contours of CFD simulation were also compared with the experimental data in Figure 10.  
Figure 8: The y+ distribution on solid walls.  
Figure 9: Overall performance comparison: (**a**) Efficiency; (**b**) Pressures, adapted with permission from [33], Copyright SAGE Publishing, 2016.  
The overall prediction of the meridional velocity and relative velocity are excellent. However, there is a small difference between the CFD and experimental data for the relative velocity contour. The regions with a low relative velocity which presents the wakes are mostly central in CFD when it is near the suction side for the experimental data. A too large tip clearance can be responsible for this small difference.  
Therefore, it is confirmed that all the significant characteristics of the compressor, such as stage performance as well as the meridional velocity and relative velocity, obtained by the CFD simulation are in excellent agreement with the experimental data. Therefore, the CFD simulation utilized in this study and its results can provide strong evidence for this research.
## 4 Numerical Simulation Considering the Stress Ratio \(R\) Based on the XFEM
### Effect of the Stress Ratio \(R\)  
Fatigue crack propagation da/dN-\(\Delta\)G curves at \(R\) ratios of 0.1, 0.3, 0.5 and 0.7 are shown in Figure 11. The simulation results were calculated based on the XFEM using the da/dN-\(\Delta\)G-R equation, as shown in Figure 8, and were close to the experimental results. Figure 11 shows that under the same energy release rate range \(\Delta\)G, the crack growth rate da/dN decreases as the stress ratio \(R\) increases. However, under the same number of cycles \(N\), the crack length \(a\) increases along with \(R\), as shown in Figure 5a. This means that in the experiment, the fatigue crack growth rate increases as \(R\) increases, which seems to contradict the results in Figure 11. The reason for this is that \(\Delta\)G is not a constant value under the same load amplitude \(\Delta\)P and crack length \(a\), and instead increases along with \(R\), as shown in Equation (6). \(\Delta\)K is a constant value under the same \(\Delta\)P and crack length \(a\) according to Equation (8). For example, when \(a\) = 20 mm, \(\Delta\)G is 8334 N/m, 12,664 N/m, 20,458 N/m, and 38,642 N/m and da/dN is 2.45 \(\times\) 10\({}^{-7}\), 2.54 \(\times\) 10\({}^{-7}\), 2.67 \(\times\) 10\({}^{-7}\), and 2.87 \(\times\) 10\({}^{-7}\) for \(R\) = 0.1, 0.3, 0.5, and 0.7, respectively, as shown in the Figure 11. Therefore, under the same crack length \(a\) and load amplitude \(\Delta\)P, the fatigue crack growth rate increases as \(R\) increases. In the experiment, under the same number of cycles \(N\), the crack length \(a\) increases as \(R\) increases.  
Figure 12 compares the experimental and numerical simulation results under different stress ratios. In Figure 12, the initial crack length \(a_{0}\) is 10 mm, which is given in Section 3.1. Numerical simulation results considering the stress ratio \(R\) were calculated based on the XFEM using the da/dN-\(\Delta\)G-R equation, as shown in Figure 8. The simulation results without considering \(R\) were calculated using the Paris equation. Figure 11 shows that the simulation results considering \(R\) from this paper are closer to the experimental data. However, the simulation crack length \(a\) considering \(R\) is slightly smaller than the test results obtained under the same number of cycles \(N\). This may be because in the tests, the fixture and specimen are not completely fixed constraints, which is slightly different from the simulation. The simulation results without considering \(R\) calculated by the Paris equation, which are close to the mean values of different stress ratios, are inaccurate; when \(R\) is equal to 0.1 or 0.3, the crack length without considering \(R\) is larger than the test value, while it is  
Figure 11: da/dN\(-\Delta\)G curves at \(R\) ratios of 0.1, 0.3, 0.5 and 0.7.  
smaller than the test value when \(R\) is equal to 0.5 or 0.7. Therefore, these findings suggest that the method based on the da/dN-\(\Delta\)G-R equation proposed in this paper should be used to calculate the FCP life of steel Q345 qD.
# Parametric Analysis of the Shear Lag Effect in Tube Structural Systems of Tall Buildings
## 1 Introduction  
Today, most of the mid to high tall buildings are built with tube structural systems. This type of system is known for its circumferential frame structure composed of closely placed columns and high beams, forming a tubelike box cross section, whose main purpose is to resist horizontal loads such as winds and earthquakes. On the inside of the building, additional columns may be found for bearing only vertical loads. Additionally, a combination of a tube structure with a central core is quite often, thus further enhancing the stiffness of the structure. The greatest advantages of the tube structural system are the ability to build very tall buildings, the enhanced stiffness of the building, the possibility of construction in various materials such as reinforced concrete and steel, possibility to use prefabricated elements for the purpose of repeating the floor dimensions and plans, and finally, a much-desired openness for the interior design of the building. Aside from very small openings that are a direct result of the column spacing on the exterior of the building, the most significant shortcoming of the tube system is the shear lag effect [1]. This effect is also very common in other structural systems of tall buildings [2; 3; 4] which will be discussed in detail in chapter 2 of this paper.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 4 Methodology
### Scenarios and Discussion
#### 4.3.3 Scenario 3: 200 Nodes  
With 200 nodes, the network's complexity increased, challenging the routing protocols.
## 3 Results and Discussion
### Characteristics of PAHs in Each Year  
Figure 9 shows the results arranged by the ring number of PAHs to investigate whether there is a behavior in the general types of PAHs for each research year. The suburban and forest sites yielded similar results, which supports the idea that they were influenced by similar sources. In the last 4 years, 4- and 5-ring PAHs have been abundant, while few 3-, 6-, and higher-ring PAHs have been reported. This behavior is similar to that observed in a long-term survey report in Japan [24; 26]. There was no clear relationship between the number of rings and the recent decreasing behavior in PAH concentrations in Nagasaki. Although the results are not shown, there was no clear relationship between the number of rings and decreasing concentrations of PAHs in the seasonal analysis.  
Source characteristics were estimated from the diagnostic ratios of the PAHs because of the possible relationship between the source type and PAH composition. Various diagnostic ratios have been proposed for this estimation [27; 28; 29]. In this study, Flt/(Flt + Pyr) was selected as an indicator that can distinguish three types of sources: petroleum, oil combustion, and biomass/coal combustion, using four-ring PAHs with relatively high abundance (Figure 9). For example, Flt/(Flt + Pyr) values of less than 0.4 are considered to originate from petroleum; 0.4-0.5 from liquid fossil fuel combustion; and >0.5 from biomass/coal combustion [29; 30; 31]. Figure 10 shows behaviors in the diagnostic ratio using all data from forest site that were less susceptible to local pollution. The results showed that the source category was \"grass, wood, or coal combustion,\" although there was some variation. These results are consistent with those of previous studies [19; 24; 32]. These results were determined by collectively calculating the data for one year under various  
Figure 9: Behaviors in composition of PAHs by ring number: (**a**) suburban site; (**b**) forest site.  
conditions, such as wind direction and temperature; the winter values were extracted and analyzed in the next paragraph.  
Figure 11 is a graph plotting each year by extracting and calculating only winter values, when PAH concentrations were higher. Figure 11 shows that the source category was sometimes \"petroleum\" in 2017 and 2018, but overall, as in Figure 10, the source category would be \"grass, wood, or coal combustion\" even when looking at winter alone. This could relate to the less advanced environmental measures in 2017 and 2018, whereby there were contributions from a variety of sources, including \"petroleum\", but from 2019 onward, contributions from such sources decreased. It also shows that biomass combustion and coal combustion remain considerable despite the advancements in recent years. Overall, the concentration of PAHs has decreased in Nagasaki in recent years, but the \(\text{Flt}/(\text{Flt}+\text{Pyr})\) index indicates that there was no significant change in the attributes of the sources. This result is consistent with that of previous studies [19; 24; 32].
# Analysis of Rewetting Characteristics and Process Parameters in Tobacco Strip Redrying Stage
## 4 Results and Discussion
### The Effective Moisture Diffusivity (\(D_{\mathit{eff}}\)) and Activation Energy (\(E_{a}\))  
The moisture content is an important indicator of rewetting [4], and it changes mainly through diffusion in the heaped tobacco strips. The moisture diffusivity is related to the changes in structure, temperature, and moisture content. The moisture diffusivity properties can be explained by [PERSON]'s second law of diffusion [31]:  
\[\frac{\partial c}{\partial t}=D_{\mathit{eff}}\frac{\partial}{\partial x}( \frac{\partial c}{\partial x}) \tag{22}\]  
where \(D_{\mathit{eff}}\) indicates the effective moisture diffusivity in tobacco strips (m\({}^{2}\)/s), and \(x\) indicates the tobacco strips' distance from the border to the center (m).  
The trend of effective moisture diffusivity with the moisture content under different temperature conditions is shown in Figure 12. Figure 12a,b shows the effective moisture diffusivity on the surface and inside the heaped tobacco strips, respectively. This coefficient increases rapidly with the increase in moisture content, and then, due to the reduction in the temperature difference and concentration difference, it tends to be flat. The higher the temperature, the greater the variation in the effective moisture diffusivity, which indicates that both relative humidity and temperature have a greater influence on the effective moisture diffusivity. Additionally, the \(D_{\mathit{eff}}\) in this study is higher than Li [32]. There are two reasons: (1) The \(D_{\mathit{eff}}\) is affected by temperature. The higher the temperature, the higher the moisture molecular activity making the effective moisture diffusivity higher, which is consistent with the studies of [PERSON] [33] and [PERSON] [28]. (2) The effective moisture diffusivity is also related to the velocity of flow. When the gas-phase velocity is high, it increases the mass transfer coefficient and accelerates the effective moisture diffusivity to the interior.  
In other fields, the influence of temperature on effective moisture diffusivity has been studied [34; 35]. The logarithm was taken based on Equation (20), and written as Equation (23):  
\[\ln(D_{\mathit{eff}})=\ln(D_{0})-\frac{E_{a}}{R(T+273.15)} \tag{23}\]  
\(\ln D_{\mathit{eff}}\) and \(1/T\) were used for plotting, and a line was obtained by fitting, as shown in Figure 13. The activation energies of the tobacco strips' surface and middle were 37.86 kJ/mol and 37.15 kJ/mol, respectively. The activation energy of the surface was slightly higher than that of the interior, which indicated that the effective moisture diffusivity increased faster on the surface than in the interior at the same temperature.  
Figure 12: The variation in effective moisture diffusivity with moisture content at different temperatures. (**a**) The heaped tobacco strips’ surface; (**b**) the heaped tobacco strips’ interior.
### Objective and Scope of the Study  
Research on human-related issues is popular, and many articles have been published in various journals, such as Safety Science, Reliability Engineering and System Safety, and Ocean Engineering.  
While studying the literature, we observed that many scholars used the three above-mentioned terms interchangeably within a single article to express similar meanings. However, the authors of each article are not always the same, and it is not accurate and scientific to presume that these articles express the same meaning as different authors cannot always have the same opinion concerning a vague topic. Therefore, the following questions arise: what are the differences and connections between each term? Is it necessary to identify their usage, and how can this be done? A review of different databases (e.g., Web of Science, ScienceDirect, and IEEE Xplore) showed that almost no systemic studies on this topic exist.  
Herein, we aim to compare the use of the terms \"human element\", \"human factor\", and \"human error\" to identify their differences and boundaries and help end the consistent mix-up of these terms from an academic perspective. This is accomplished through a systemic survey of the three terms in peer-reviewed articles and official information on maritime transportation.
## 4 Evaluation Scope
### Test Scenarios
#### 4.3.1 Real-World Scenarios with Critical Impact  
The category of real-world scenarios addresses test scenarios, which address real-world cases in which AI decisions significantly affect individuals or carry a high risk of substantial impact on the lives of individuals, groups, or society. These are domains where the stakes of AI decisions are high, necessitating rigorous and reliable explanation systems. By using an open and iterative scoping review methodology, we ensured the inclusion of diverse evaluation practices and methodologies, capturing the complexity and real-world relevance of explainable AI research across various critical domains like healthcare, justice, finance, etc.  
Many of our selected domain-driven studies, such as [29; 30; 31; 32; 43; 47; 49; 55; 56; 58; 67; 71; 73; 77], fall into this category. These studies typically place a high degree of emphasis on the ecological validity of their research. Various studies investigate the actual explanation needs of affected people and/or the usage of explanatory systems in practice [25; 29; 30; 32; 77]. There is also a strong emphasis on creating evaluation scenarios that reflect the real-world setting of the domain as closely as possible [31; 49; 56; 58; 67; 73].  
Within this category [26; 28; 31; 33; 40; 41; 46; 50; 68; 70; 86; 87], there are also various concept-oriented studies. These studies are less focused on specific application domains but rather on generic explanatory concepts and their impact on users. The use of real-world scenarios in these studies, however, helps to demonstrate the research's relevance and evaluate the concepts by using scenarios that are meaningful for the participants. The same applies to methodological studies [44; 50; 57], where the focus is on how explainability can be evaluated and what appropriate measurements and procedures are. In these cases, real-world scenarios are also used to illustrate general considerations or to validate the developed measurement methods through specific application cases.  
Real-world scenarios frequently focus on the healthcare [44; 49; 57; 85] and judicial sector [36; 52; 65; 73; 86; 87], where mistakes in decisions can have a significant impact on individuals' lives. To a lesser extent, this also applies to the financial sector [15; 25; 50; 65; 68; 70; 88], where the denial of a loan or a poor investment in houses, stocks, or other financial products can have significant repercussions. Other real-world scenarios address the denial of access to educational institutions, such as universities [43], and essential digital services, which can have a significant impact on an individual's life. Other real-world scenarios address news recommendation algorithms, which can impact the spread of fake news and the formation of filter bubbles [55].