esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Plasma Combustion Technology on Micro Gas Turbines Using Biodiesel Fuels
###### Abstract  
The adoption of biorerenewable alternative fuel resources from biofuels (ethanol or biodiesel) has produced promising solutions to reduce some toxic greenhouse gas (GHG) emissions from gas turbine engines (GTEs). Despite the reduced hydrocarbon associated with adopting alternative bio-renewable fuel resources, GTE operations still emit toxic gases due to inefficient engine performance. In this study, we assess the impact of the integration of plasma combustion technology on a micro-GTE using biodiesel fuel from animal fat with the aim of addressing performance, fuel consumption, and GHG emission reduction limitations. Laboratory design, fabrication, assembly, testing, and results evaluation were conducted at Kuwait's Public Authority for Applied Education and Training. The result indicates the lowest toxic emissions of sulfur, nitrogen oxide (NO), NO\({}_{2}\), and CO were from the biodiesel blended fuels. The improved thermal efficiency of GTE biodiesel due to the volume of hydrogen plasma injected improves the engine's overall combustion efficiency. Hence, this increases the compressor inlet and outlet firing temperature by 13.3 \({}^{\circ}\)C and 6.1 \({}^{\circ}\)C, respectively. The Plasma technology produced a thrust increment of 0.2 kgf for the highest loading condition, which significantly impacted horsepower and GTE engine efficiency and reduced the cost of fuel consumption.  
plasma technology; biodiesel; gas turbine; greenhouse gas; rich fuel; fuel atomization +
Footnote †: journal: Applied Sciences
# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Single-Layer Satellite Networks
#### 4.1.1 Topology Planarization
In the following work on satellite routing, we can explore the re-routing mechanism in more depth for different cases.
# Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System
## 4 Control Design
### Disturbance Observer  
The DOB is an effective method of robust control to solve external disturbance and model uncertainty, and is widely used in various fields. It is not only excellent in control performance but also convenient to apply because of its simple structure. In particular, by simply adding the DOB to the inner loop of the existing controller, the performance of the outer loop controller can be guaranteed without separately considering the performance degradation due to disturbance and model uncertainty. The DOB estimates the disturbance, and the estimated disturbance signal is used as a disturbance cancellation input. The DOB is composed of an inverse nominal plant \(P_{n}{(s)}^{-1}\) and LPF \(Q(s)\) that cuts off the disturbance in the low-frequency region. \(Q(s)\) is expressed as follows:  
\[Q(s)=\frac{\omega_{q}}{s+\omega_{q}} \tag{22}\]  
where \(\omega_{q}\) is the cutoff frequency. To improve the performance in suppressing disturbance, the cutoff frequency should be increased. However, this is restricted to guarantee robust stability, as more unnecessary frequencies can be passed. Thus, it is critical to design an appropriate cutoff frequency.  
The effectiveness of the DOB is clearly explained by transfer functions written as follows:  
\[\frac{F_{T}}{u_{a}}=\frac{P(s)P_{n}(s)}{Q(s)[P(s)-P_{n}(s)]+P_{n}(s)} \tag{23}\]  
\[\frac{F_{T}}{F_{d}}=\frac{P(s)P_{n}(s)[1-Q(s)]}{Q(s)[P(s)-P_{n}(s)]+P_{n}(s)} \tag{24}\]  
\[\frac{F_{T}}{\xi}=\frac{P(s)Q(s)}{Q(s)[P(s)-P_{n}(s)]+P_{n}(s)} \tag{25}\]  
where \(u_{a}\) is the control input and \(\xi\) is the measurement noise.  
\(Q(s)\) plays an important role in compensating disturbance and model uncertainty in the DOB. If the input frequency is smaller than the bandwidth of the \(Q(s)\) (i.e., \(Q=1\)), the first expression is \(P_{n}\) and the second expression is zero. This means that model uncertainty is compensated for and low frequency disturbances are rejected. On the other hand, if the input frequency is higher than the band-width of the \(Q(s)\) (i.e., \(Q=0\)), the third equation is zero. Therefore, the high-frequency noise is removed.
## 8 Results and Discussion
### Dynamic Scenario  
This trajectory simulates a land vehicle driving in downtown Toronto, incorporating a realistic driving manner such as deceleration during sharp turns. The trajectory started with the land vehicle being stationary for 1 min. Then, it accelerated to reach a final speed of 50 km/h. The land vehicle continued its motion for about 4 min with several accelerations, decelerations, and sharp turns. Eventually, it decelerated to 0 km/h to arrive at the same start point of the trajectory. The speed of the land vehicle was varied from 0 to 60 km/h throughout the trajectory. After the land vehicle started moving, by about 3 min, the signal power level of PRN 1 was attenuated by 15 dB for thirty seconds.  
Figure 13 shows the navigation solution of the scalar and vector-based receivers compared to the reference visualized on Google Earth. The figure illustrates that the vector-based receiver maintained an outstanding navigation solution, despite the attenuated satellite. In contrast, the scalar-based receiver reported multiple aberrant position solutions during the period at which the satellite was attenuated. Such fluctuations in the navigation solution can lead to significant consequences in driverless vehicles.  
Figure 11: Doppler shift for scenario #3. PRN 16 (**left**), and PRN 30 (**right**).  
Figure 12: Velocity solution for scenario #3. Horizontal speed (**left**), and up-velocity component (**right**).
## 4 Discussion and Conclusions
On the other hand, Bayesian networks, because of their ability to integrate both qualitative and quantitative data and their explicit consideration of uncertainty, are a powerful tool for spatial modeling of ES [96]. In particular, they can be used to map human demand for ES, defining in combination with the spatialization of supply, the global and complete approach to the socio-ecological concept of ES [28]. However, in some cases, the synthetic assessment of the potential of supply and/or demand of ES may be difficult because of the different physical meanings of the indicators, and it is then necessary to focus on one or a few easily identifiable and interpretable ES. In this case, some biophysical indicators that can be estimated by remote sensing are quite appropriate. In any case, as emphasized by [PERSON] et al. (2014) [27], the approach to the assessment and mapping of ES is multidisciplinary in nature and involves collaboration between researchers from different disciplines such as ecology, remote sensing, GIS, geography, and social sciences. In this configuration, we have initiated a mapping of the ES provision of a mountain forest in Algeria, a country for which these kinds of works are rare and for which any attempt to provide useful data for forestry management of the territory is valuable. We hope that this work will encourage further assessment of forest ES in Algeria, allowing us to implement some of the methodological perspectives advocated. Conceptualization, A.L. and F.M. ; methodology, A.L. and F.M. ; software, F.M. and A.L. ; formal analysis, F.M. ; investigation, A.L. ; writing--original draft preparation, A.L. and F.M. ; writing--review and editing, F.M. and A.L. ; project administration, Y.D. and R.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors thank the foresters of the wilaya of Bordj Bou Arreridj, in particular those of Bordj Ghedir and Ras El Oued for their help in the field. The authors declare no conflict of interest.
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 2 Materials and Methods
### Dataset  
The dataset used to test the models developed using \(R\) packages pertains to lung-related diseases. This publicly available dataset can be downloaded from the repository [27]. It is also found in [52]. It consists of chest X-ray images depicting various conditions: normal chest X-rays with clean lungs and no abnormal opacification areas, chest X-rays with bacterial pneumonia typically showing focal lobular consolidation visible in the center of the image, and chest X-rays with viral pneumonia often exhibiting a more diffuse \"interstitial\" pattern in both lungs [27; 53]. The dataset is organized into three folders (train, test, val), each containing subfolders for each image category (Pneumonia/Normal). There are a total of 5856 JPEG X-ray images categorized into two groups: Pneumonia and Normal.
## 4 Results and Discussion
### Statistical Analysis of Kolmogorov-Smirnov Test
#### 4.2.2 Maximum and Minimum Temperature (T\({}_{\text{max}}\) and T\({}_{\text{min}}\))
Also in this case, based on the statistics estimated by the K-S test, the DM method gives better results in comparison to the LS and VS methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**No**} & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**Variance**} & \multicolumn{2}{c}{**Distribution**} \\  & \multicolumn{2}{c}{**Correction**} & \multicolumn{2}{c}{**Scaling**} & \multicolumn{2}{c}{**Scaling**} & \multicolumn{2}{c}{**Mapping**} \\ \cline{2-9}  & **D** & \(p\) & **D** & \(p\) & **D** & \(p\) & **D** & \(p\) \\ \hline January & 0.6774 & 0.0 \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**No**} & \multicolumn{2}{c}{**Linear**} & \multirow{2}{*}{**Variance Scaling**} & \multicolumn{2}{c}{**Distribution**} \\  & \multicolumn{2}{c}{**Correction**} & \multicolumn{2}{c}{**Scaling**} & \multicolumn{2}{c}{**Variance Scaling**} & \multicolumn{2}{c}{**Mapping**} \\ \cline{2-9}  & **D** & \(p\) & **D** & \(p\) & **D** & \(p\) & **D** & \(p\) \\ \hline January & 0.5161 & 0 & 0.1290 & 0.9440 & 0.1935 & 0.5590 & 0.1290 & 0.9440 \\ February & 0.4286 & 0.008 & 0.1429 & 0.9170 & 0.1429 & 0.9170 & 0.1071 & 0.9950 \\ March & 0.2581 & 0.216 & 0.0645 & 1.0000 & 0.1290 & 0.9440 & 0.0645 & 1.0000 \\ April & 0.0667 & 1 & 0.1000 & 0.9970 & 0.1000 & 0.9970 & 0.1000 & 0.9970 \\ May & 0.0968 & 0.998 & 0.0968 & 0.9980 & 0.0968 & 0.9980 & 0.0645 & 1.0000 \\ June & 0.3667 & 0.026 & 0.1000 & 0.9970 & 0.1333 & 0.9360 & 0.0667 & 1.0000 \\ July & 1.0000 & 0 & 0.0645 & 1.0000 & 0.0968 & 0.9980 & 0.0645 & 1.0000 \\ August & 1.0000 & 0 & 0.1290 & 0.9440 & 0.1290 & 0.9440 & 0.1613 & 0.7780 \\ September & 1.0000 & 0 & 0.1333 & 0.9360 & 0.1333 & 0.9360 & 0.1000 & 0.9970 \\ October & 0.8710 & 0 & 0.1290 & 0.9440 & 0.1613 & 0.7780 & 0.1290 & 0.9440 \\ November & 0.8000 & 0 & 0.1333 & 0.9360 & 0.1333 & 0.9360 & 0.1333 & 0.9360 \\ December & 0.7097 & 0 & 0.0968 & 0.9980 & 0.2581 & 0.2160 & 0.0968 & 0.9980 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The K–S test statistic D and level of significance for the cumulative distribution functions of the model-simulated data without correction and data corrected using the LS, VS, and DM methods with respect to the observed daily minimum temperature data. Figure 8: K–S test statistic D for bias correction methods with respect to observed daily minimum temperature data. Figure 7: K–S test statistic D for bias correction methods with respect to observed daily maximum temperature data.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 2 Conventional Interpretation Techniques for Bender Element Tests
### Time Domain Methods  
Time domain methods attempt to read the arrival time of the shear wave by analysing input and output signals directly. Figure 1 shows an example of a typical pulse input signal and an idealised received (output) signal.  
Based on the idealised output signal presented in Figure 1, four classes of interpretation techniques are proposed, namely the first arrival method, the peak-to-peak method, the second arrival method, and the cross-correlation method. All are based on the assumption of a plane shear wave-front travelling through the sample in the absence of any reflected or refracted waves [8].
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 4 Results
### Visualising Local and Global Heatmaps of Features Using LRP  
The main objective of XAI in this work is to see how or why a deep network gave a specific decision (TP, TN, FP, or FN). Figure 4a shows a heatmap generated by LRP for local interpretation of a single record that resulted in TP. The heatmap clearly shows that because of features such as the customer being a senior citizen (SeniorCitizen), monthly contract (contract_M_to_M), Fiber optic internet (IS_Fiber_Optic), high monthly charges (MonthlyCharges), and no phone service (PhoneService) it is predicted that this customer is going to churn. Based on our research in the domain/general business domain, it is known and/or learned that a customer is more likely to churn if the contract is monthly, having no phone service, and with high monthly charges. To retain the customer, it is suggested that the company reduces some charges by reducing some features but offers a yearly or biyearly contract so as to retain the customer for a longer term. Hence, although it is not being tested/validated by us, it is still known and understandable by the companies (e.g., VirginMedia Ireland Mobile and Internet [60] as they offer lower charges but longer contracts as well as introducing fibre connections rather than old technology. Figure 4b shows the local analysis of a record which will not churn. The heatmap shows the features for a customer who will not churn, and is not a senior citizen, has online security, a contract of one year without fiber optic, with several other features but fewer monthly charges. Figure 4c shows a clear pattern for all the correctly classified TP samples in the testing set, highlighting the important features that play a key role in the model decision towards being marked as TP. The same applies in the case for TNs as shown in Figure 4d. This ability to understand model decisions at a class level has a tangible business use case. In our TCCPD business domain example, understanding TP and TN can help a company/data scientist in increasing the revenue by retaining the customers--supporting the well-known maxim that in business, it is easier to keep a customer than to find a new one. On the other hand, understanding and recognising the key features for the local and global analysis of FP and FN samples may help data scientists to avoid or reduce the discrepancy in the data results in miss-classifications by the model. Our demonstrator [61] also highlights heatmaps for FP/FNs and the features that play key roles in generating incorrect predictions. This is helpful for understanding useful information about the data and enhancing the trained model information e.g., to avoid redundant or ambiguous features that impact the performance of the deep learning models. Figure 5 shows the heatmaps for local (individual) and global (all) TP and TN samples in a CCFDD testing set.
# New Indices for Evaluating Vibration Characteristics of Flexible-Joint Robots
###### Abstract
The inertial matching ellipsoid is a performance index combining dynamic manipulability and manipulating force ellipsoids which represents the dynamic torque-force transmission efficiency between the joint torque and the static force applied to a part mounted on the robot end-effector [31]. These performance indices are derived based on the assumption that the robot is rigid and are usually proposed for dimensional synthesis. Stiffness is an important structural consideration for robotic applications and represents the effect of applied static forces and torques on the end-effector deformation. Similar to the dexterity index, [PERSON] et al. [32] used the condition number of the stiffness matrix to evaluate the stiffness of 3-DOF (degrees of freedom) spherical parallel robots. [PERSON] et al. [33] chose the maximum and minimum eigenvalues of stiffness matrix to calculate the upper and lower bounds of the robot stiffness which occurs in the corresponding eigenvector directions. However, none of the aforementioned indices can be used to evaluate the structural vibration characteristics when the robot end-effector affords dynamic loading. Aiming at addressing this issue, the main contribution of this work is to derive two indices which can be used for efficient evaluation of the vibration characteristics of flexible-joint robots under a dynamic force and torque, respectively. For typical industrial articulated robots, the joints have been considered as most significant flexible components, due to the deformations of transmission components such as gears, shafts, belts and harmonic drives [1; 26]. Therefore, we limit our scope to the robots with rigid links and flexible joints in this work. It is also assumed that the influence of the robot deformation on the external force and torque can be neglected. Two indices are defined for evaluating the vibration characteristics of robots under a dynamic force and torque, respectively. To this end, a structural dynamic model is first established for flexible-joint robots that undergo forced vibrations, and obtain the frequency response of the tool displacement due to the process excitation. Then, the displacement-force and displacement-torque frequency response ratios are derived from the frequency-domain analysis, which represent the mapping from the amplitudes of external force and torque to the amplitude of tool displacement due to forced vibrations, respectively. The upper bounds of the two ratios are used as indices to describe the worst situation of robot vibrations under a harmonic excitation with an amplitude of unit force and unit torque, respectively. The development leads to an efficient method to evaluate the tool misalignment caused by periodic process excitation.
# Analysis of Life Cycle Environmental Impact of Recycled Aggregate
## 3 Analysis of Life Cycle Impact Assessment (LCIA)
### Process of LCIA
#### 3.2.1 Raw Material Stage  
This study assessed the environmental impact according to the production process of waste concrete, a raw material for recycled aggregate [36]. Recycling breaks and separates waste concrete into small pieces and recycled aggregate are produced for use. Waste concrete is generated during the demolition and deconstruction of structures and is classified as construction waste if it is not used as another product. During the deconstruction of a building, a large amount of energy is used and environmental impact substances are discharged; however, since waste concrete is a by-product, its previous industrial process is not included in the environmental impact assessment category. Thus, the process of waste concrete discharge during the demolition and deconstruction of a structure was excluded from the categories of environmental impact assessment.  
\[\text{Raw material}_{\text{E,I}}=\sum\left[\left(\text{M(i)}\text{ / Wc}\right)\times\text{t}\times\text{D}\right]\text{ (i = Waste concrete)} \tag{2}\]  
Here, raw material E.I is the environmental impact of raw materials, (kg-unit/kg), M(i) is waste concrete (ton), Wc is (i) the volume (ton) of waste concrete collecting equipment, T is work hours (h), and D is characterization value (kg-unit/L) of diesel by environmental impact category.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Division** & **Reference** & **Country** \\ \hline Raw material & Waste concrete & National LCI & Korea \\ \hline \multirow{3}{*}{Energy} & Electric & National LCI & Korea \\  & Diesel & National LCI & Korea \\  & Kerosene & National LCI & Korea \\ \hline Transportation & Truck & National LCI & Korea \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Life Cycle Index (LCI) Database.
## 2 Materials and Methods  
### Composites Production  
For this work, commercial \(\alpha\)- SiAlON and \(\beta\)-SiAlON (d\({}_{50}\) = 360 \(\pm\) 0.05 nm and d\({}_{50}\) = 530 \(\pm\) 0.05 nm, respectively, made by Plasmotherm, Moscow, Russia) and TiN (d\({}_{50}\) = 0.6 \(\mu\)m, purity 99.9%, Plasmotherm, Moscow, Russia) powders were used. Powders were wet-mixed for 24 h in a plastic container using a multidirectional mixer Turbula (Tomasova Lea s.r.o., Pardubice, Czech Republic) with isopropyl as liquid media and Si\({}_{3}\)N\({}_{4}\) grinding balls. The ratio in the starting powders between alfa- and beta-SiAlON was 90 wt.% and 10 wt.%, respectively. Obtained slurries were freeze-dried using LabConco FreeZone2.5 (Kansas, MO, USA) freeze dry system. This method of drying contributes to homogeneous distribution of powder mixture while avoiding sieving stage. More detailed information about the operating mode has been presented by the authors of [10]. The obtained powder was placed in a die-punch setup made from isostatic graphite (grade C4, DonCarb Graphite, Rostov, Russia). Disks of 20 mm diameter and 3 mm height were produced using H-HP D-25 SD spark plasma sintering (SPS, FCT Systeme GmbH, Rauenstein, Germany) furnace with variation of temperatures (1600, 1650 and 1700 \({}^{\circ}\)C) with a heating rate of 200 \({}^{\circ}\)C/min under vacuum at 80 MPa axial pressure for a dwelling time of 30 min at the final temperature. Figure 1 shows the temperature-time diagram recorded in SPS apparatus during the process.  
The temperature was controlled during sintering by a pyrometer situated at the top of the machine and focused at the center of the blank (3 mm over the top surface). The mixture of \(\alpha\)- and \(\beta\)-SiAlON powders was used as a ceramic matrix to produce 3 compositions containing 0 wt.%, 10 wt.%, and 20 wt.% TiN. The sintered nanocomposites were labelled \(\alpha\)-\(\beta\)0T, \(\alpha\)-\(\beta\)10T, and \(\alpha\)-\(\beta\)20T, where the numbers show the TiN weight content. Besides the SPS samples, commercial TC3030 (TaeguTeC Ltd., Daegu, Korea) cutting inserts with cylindrical shape (\(\alpha\) 19 mm) were used as a reference sample.
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 2 Clock Recovery in Coherent Optical Receivers  
With rich modulation formats becoming the norm in coherent transceivers, data converters are more sensitive to degradations in signal-to-noise ratio (SNR); especially when single-carrier modems are used at high-baud rates. One way to maintain performance above required levels, and to allow signal processing to compensate for imperfections in the optical channel, is to reduce errors in clock synchronization for the ADC. The typical architecture of such apparatus is shown in Figure 2, in which the bulk CD is compensated for prior to PD. The error generated by the latter drives a filter that controls the frequency/phase of the ADC sampler. The main objective of the clock recovery is to estimate the timing offset and to compensate for it. The architectures discussed in the literature are typically a combination of digital and analog blocks, and designs can be feedforward-based, feedback, or a combination of both, and data-aided versus non-data-aided [16].  
In the context of optical gridless networks, interference from adjacent channels is an extra source of linear impairment. It becomes a more difficult challenge when clock recovery depends on excess bandwidth, i.e., PD is non-data-aided. This is indeed the case in Nyquist and Super-Nyquist spacing. The latter is defined as when the spacing between channel and its neighbor (\(\Delta f\)) is smaller than the channel bandwidth (\(B\)). In the case of root-raised cosine pulse shaping, the excess bandwidth is governed by the roll-off factor \(\beta\) and \(T\) is the duration of the symbol and it is defined in time-domain as:  
\[h(t)=\ \frac{2\beta}{\pi\sqrt{T}}\frac{\cos^{\left[\frac{(1+\beta)nt}{T}\right]}+ \ \frac{\sin^{\left[\frac{(1-\beta)nt}{T}\right]}}{4\beta t/T}}{1-\ \left(4\beta t/T\right)^{2}} \tag{1}\]