esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Implementation
### Hyperparameter Tuning  
A hyperparameter can be defined as a parameter whose value is set before the learning process. For example, in an ANN, the number of hidden layers, the number of hidden neurons, the type of activation function and the learning rate are hyperparameters. In a random forest, the number of trees, the depth of the tree, or the number of samples required to split an internal node or a leaf node are examples of hyperparameters.  
Hyperparameter tuning can be defined as an optimization problem with the intent of determining those parameters that lead into an optimal value and has been tested with success for machine learning algorithms such as RF and ANN [30]. This pursuit can be computationally expensive and time consuming, especially if a brute force type of search is performed, where all possible data points are verified. Hyperparameter tuning problems, however, can be solved through an algorithm designed for its optimization.  
For example, Bayesian optimization is a strategy for determining local maxima from computationally expensive functions, considering prior tested data. The maximum value, from a given search space, is determined by a combination of exploiting spaces with high values (exploitation) and exploring other areas with uncertainty (exploration), which are common in optimization algorithms. The prior distribution of Bayesian optimization is ensured with the Gaussian process (GP), which is considered flexible and easy to handle, therefore helping with a good fit of data in the algorithm [30]. The GP is a function where the variable is a Gaussian distribution:  
\[f(x)\sim GP\big{(}m(x),k(x,x^{\prime})\big{)} \tag{7}\]  
Here \(m(x)\) is the distribution's mean function, and \(k(x,x^{\prime})\) the covariance function of two tested points \(x\) and \(x^{\prime}\). Function \(k\) is usually an exponential square function. If there is a strong correlation, there is less uncertainty, however, if the points are further away, there is less correlation and more uncertainty. If the number of data points are large enough, it is possible to have a general sense of how to optimize function \(f\)[30]. Given posterior information, the GP works in an iterative way and the acquisition function determines the next search.
# Adhesion Performance between Solid Waste and Bitumen Based on Surface Energy Theory
## 3 Methodology
### Adhesion Performance between Bitumen and Aggregates  
The original liquid asphalt and solid surfaces of the aggregate will disappear to form a new solid-liquid interface when the liquid bitumen and the aggregate contact each other and the adhesion reaction occurs. This process will result in a reduction in the Gibbs free energy [25]. The Gibbs free energy is the energy consumed during the entire adhesion process, which is the D-value between the final and initial free energy of the system, as expressed by \(\Delta G\). A large adhesion work, \(W\), strengthens the solid and liquid bond. Gibbs free energy and adhesion work are equal in value and opposite in sign (Equation (9). The Gibbs free energy of the system is expressed by Equations (10)-(12), and the adhesion work is expressed by Equation (13).  
\[W_{as}=-\Delta G_{as} \tag{9}\]  
\[\Delta G_{as}=\gamma_{as}-\gamma_{a}-\gamma_{s} \tag{10}\]  
\[\Delta G_{as}=\Big{(}\gamma_{a}^{LW}+\gamma_{s}^{LW}-2\sqrt{\gamma_{a}^{LW} \gamma_{s}^{LW}}+2\sqrt{\gamma_{a}^{+}\gamma_{a}^{-}}-2\sqrt{\gamma_{a}^{+} \gamma_{s}^{-}}-2\sqrt{\gamma_{a}^{-}\gamma_{s}^{+}}+2\sqrt{\gamma_{s}^{+} \gamma_{s}^{-}}\Big{)} \tag{11}\]  
\[-\Big{(}\gamma_{a}^{LW}+2\sqrt{\gamma_{s}^{+}\gamma_{a}^{-}}\Big{)}-\Big{(} \gamma_{s}^{LW}+2\sqrt{\gamma_{s}^{+}\gamma_{s}^{-}}\Big{)}\]  
\[\Delta G_{as}=-2\sqrt{\gamma_{a}^{LW}\gamma_{s}^{LW}}-2\sqrt{\gamma_{a}^{+} \gamma_{s}^{-}}-2\sqrt{\gamma_{a}^{-}\gamma_{s}^{+}} \tag{12}\]  
\[W_{as}=2\sqrt{\gamma_{a}^{LW}\gamma_{s}^{LW}}+2\sqrt{\gamma_{a}^{+}\gamma_{ s}^{-}}+2\sqrt{\gamma_{a}^{-}\gamma_{s}^{+}} \tag{13}\]  
where: \(W_{as}\): Adhesion work between bitumen and aggregate, mJ\(\cdot\)m\({}^{-2}\); \(\Delta G_{as}\): Gibbs free energy change value during the adhesion process of bitumen and aggregate, mJ\(\cdot\)m\({}^{-2}\).
# An Improved FFIP Method Based on Mathematical Logic and SysML
## 6 Case Study
### Result  
* Single failure simulation Single failure simulation is to simulate the system situation when one failure module is set to \"1\" and other failure modules are set to \"0\" each time. Limited by the length of the paper, only 9 typical single failures are selected for description in this section, as shown in Table 5. The single failures shown in the table are as follows.  
* Failure 1: Command does not respond,
* Failure 2: Monitor's logic is opposite,
* Failure 3: Green pump is blocked,
* Failure 4: Selector valve's logic is opposite,
* Failure 5: CMD/AS meter valve opened too late,
* Failure 6: Blue pump isolation valve leakage,
* Failure 7: Accumulator valve opened early,
* Failure 8: AS meter valve does not respond,
* Failure 9: Accumulator pump leakage.  
It should be noted that the example here only lists one failure mode for each component. There are still other failure modes for the components. Other failure deviations that may be involved in components are shown in Table 1.  
* Double failure simulation Double failure simulation is to simulate the system situation when two failure modules are set to \"1\" and other failure modules are set to \"0\" each time. Also due to space limits, this paper selects 8 typical double failures to explain, as shown in Table 6.
## 4 Engineering Application and Analysis
### Analysis of Calculated Results for Rock-Lining Synergy
#### 4.3.3 Effect of Lining Outer Radius on Surrounding Rock  
In tunnel engineering, lining design is crucial for ensuring structural integrity and facilitating smooth construction processes. The choice of lining the outer radius significantly influences the interaction with the surrounding rock, thereby impacting construction safety. To thoroughly investigate the impact of lining the outer radius on tunnel stability, this study analyzes how varying outer radii affect the stress at the tunnel wall and the plastic zone thickness under different reserved deformations, as illustrated in Figure 15.  
Figure 15 clearly illustrates the variation in hoop stress, equivalent stress, and plastic zone thickness at the tunnel wall with different lining outer radii in response to the reserved deformation \(\Delta u\):  
1. When \(\Delta u\) is zero, the hoop and equivalent stresses around the tunnel wall converge at approximately 14.3 MPa and 11.4 MPa, respectively. Following the initial stress release of the surrounding rock, the stress distribution within the rock begins to readjust. Upon reaching a critical threshold of \(\Delta u\), the hoop stress for various linings of the outer radii stabilizes at a minimum value of about 0.5 MPa.  
Figure 14: Tunnel wall stress and plastic zone thickness variations across concrete elastic moduli.  
2. The equivalent stress initially exhibits a linear decrease and subsequently transitions to a stable plateau as the reserved deformation increases. Ultimately, the equivalent stress under different lining radii converges to approximately 2.24 MPa, indicating that the impact of further increases in \(\Delta u\) on equivalent stress is minimal once the surrounding rock stress has been sufficiently released.
3. The plastic zone thickness of the surrounding rock exhibits a nonlinear relationship with reserved deformation, characterized by an initial parabolic growth curve with an average initial slope of 192.2. As the reserved deformation increases, the rate of plastic zone thickness growth decelerates until it peaks. It is evident that variations in the lining's outer radius significantly affect the plastic zone thickness of the surrounding rock. An increase of 1 m in the lining outer radius results in an average increase of 154.6 mm in the maximum plastic zone thickness of the surrounding rock.
# Can Biochar Improve the Sustainability of Animal Production?
## 2 Summary of Biochar-Production Techniques
### Properties of Biochar
#### 2.3.2 Surface Chemistry  
During thermal decomposition of the biomass and subsequent activation of biochar, several reactions occur between the biomass fragments resulting in the formation of chemical functionalities on the biochar surface [62]. Depending on the process and activation conditions, the surface is enriched with either oxygen-rich acidic groups or oxygen-deficient basic groups. The acidic groups, namely carboxylic, phenols, and lactones make the biochar surface hydrophilic and actively participate in the chemisorption of ammonia and ammonium from gaseous aqueous systems [63; 64].  
In addition, the acidic groups also participate in the cation exchange processes when applied to soils as conditioners [65]. Conversely, biochar surfaces enriched with nitrogen groups, such as pyridines and pyrroles, and certain basal planes within the biochar matrix impart basicity to the biochar surface and contribute to the adsorption of organic compounds and heavy metals in aqueous and gaseous systems [57].
# An RF-MEMS-Based Digitally Tunable SIW Filter in X-Band for Communication Satellite Applications
## 4 Fabricating, Mounting and Measurements  
The photograph of the realized filter is shown in Figure 7. It was built on standard alumina substrate. Measurements were performed on a Keysight E8364 A 45 MHz-50 GHz--VNA and a Cascade Microtech summit 9000 probe station. It is worth noting the simplicity of the RF-MEMS biasing network. Indeed, a bias line was etched on the side of the filter, allowing the distribution of actuation voltage for all RF-MEMS gates through long DC wire bondings.  
Drain and source pads were connected to the tuning post and SIW top surface, respectively. Two wire bondings were used for each connection in order to minimize the inductive effect. Moreover, a 1 M\(\Omega\) resistor was mounted directly on the other side of the filter in order to limit the current. The first (OFF) and the second (ON) states were obtained for actuation voltages of 0 and 93 V, respectively. The simulated/measured responses are shown in Figure 8.  
Thanks to the four additional wire bondings (one for each RF-MEMS), the resultant tuning range between the two states was found to match the desired specifications. The center frequencies of the two states were 10.825 and 11.075 GHz, respectively. Thus, according to the specifications, we designed a tuning range of 2.3% (250 MHz). However, it seems that the model of the RF-MEMS needs to be further improved in term of losses. The measured insertion losses of the two states were 4.4 and 2.6 dB, respectively. The measured 3 dB relative bandwidths of the two states were 2.76% and 2.89%, respectively.  
Figure 7: Photograph of the RF-MEMS-based tunable bandpass filter and zoom on the mounted RF-MEMS switch with the additional wire bonding.
# Abrasion-Resistant and Temperature Control of
###### Abstract  
With regard to the high anti-scouring and abrasion-resistant performance requirements and great temperature control difficulties of lining concrete for large-sized spillway tunnels, in this study, a performance test was conducted on anti-scouring and abrasion-resistant concrete. The finite element method was used to analyze the temperature change rules of sidewall C\({}_{90}\)50 (design strength of concrete is 50 MPa at 90 days) lining concrete for the spillway tunnel. Further, a new cooling measure was proposed for adopting \"early-throughput, high-flow and short-duration\". As indicated by the results of this study, fly ash could reduce water consumption and micro-cracks via its \"morphological effect\". Silica fume could improve the early strength of cement concrete and make up for the strength loss caused by fly ash. Polyvinyl alcohol (PVA) fiber could enhance concrete durability. The doping of these three additives reinforced the strength and abrasion resistance of concrete. The results showed that the temperature of the lining concrete presented a change trend of \"rapid increase first, followed by a slow decrease\". The peak temperature was reached roughly 2 days after casting. In addition, properly increasing throughput flow or decreasing throughput temperature in the early stage of casting could significantly reduce the highest temperature and maximum temperature difference of concrete. Based on the results from the numerical simulation of temperature control effect, it was proposed to adopt \"early-throughput, high-flow, and short-duration\" for temperature control and cracking prevention. Specifically, within 2 days after casting, cooling water at roughly 12 \({}^{\circ}\)C was guided in at a flow of approximately 3.5 m\({}^{3}\)/h. Within 3-7 days after casting, river water at around 17 \({}^{\circ}\)C was guided in at a flow rate of approximately 1.8 m\({}^{3}\)/h. After 7 days, the cooling effect can be well achieved by only using the surface flowing water for curing. According to the field monitoring data, the changes in measured temperature were basically consistent with those from numerical simulations, and detection on the temperature of the sidewall lining concrete showed that a qualification rate of \(>\)91% was satisfactorily obtained by using the proposed approach.  
spillway tunnel; concrete; anti-scouring and abrasion-resistant; temperature control; crack prevention measure +
Footnote †: journal: Applied Soft Soft 2020, 10, 7614; doi:10.3390/app10217614
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 3 Simulation Environment and the Convoy VANET System
### Simulated Wireless Procotol  
The lower layers of the network protocol consist of ns3's included QoS WAVE MAC, 802.11p, and Internet stacks. These three stacks use default parameters aside from 802.11p, and we used OFDM 12 Mbps and 10 MHz instead of 6 Mbps. This change should have no effect on the end result; it is only included for completeness. Every vehicle in the simulation has a unique IP address on the same subnet.
# Quality of Service Criteria in Integrated Passenger Transport Systems: An Overview
## 1 Introduction  
According to projections, 68% of the world's population will live in urban areas, placing a significant burden on environmental management and existing infrastructure [1]. Creating sustainable and efficient urban mobility solutions is crucial for addressing the challenges of modern cities and ensuring a high quality of life for their residents. Nowadays, transport development is directly connected to the economic and social development of a given city or region. Transport has a strong influence on spatial structure at the local, regional and global levels [2].  
UM encompasses the movement of people and goods within urban areas. It incorporates various transport modes, including walking, cycling and public and private transport, as well as shared mobility services like ridesharing and bike-sharing. It has become an increasingly important issue in recent years, as cities have faced growing traffic congestion, air pollution and other transport-related challenges [3]. Public transport services are systems designed for public use, playing a crucial role in UM. The advantages of public transport systems are numerous; they help ease traffic congestion, reduce air pollution, support sustainable mobility and enhance access to employment, education and healthcare services.  
PPT can be defined as transport available for public use, a transport system (of buses, trains, etc.) that runs on fixed routes at set times [4]. PPT is commonly used by people commuting or travelling within a city or region. It can be used by anyone possessing a valid ticket or pass. One of the concepts of PPT organisation and management is the IPTS. It refers to a coordinated network of public transport services that enable passengers to travel between origin and destination points using various modes of transport. It is characterised by the integration of different modes of transport into a unified fare system, a unified integrated and harmonised timetable, a unified ticketing system and an information system [5]. It is stated that the increasing number of passengers in urban areas has encouraged new travel options (in addition to traditional ones), with multimodal travel being a promising option [6].  
Presently, many case studies and research papers deal with the evaluation of PPT user satisfaction, considering EN 13816:2002 standard. Studies are entirely focused on analysing one mode of PPT. A notable research gap exists in the assessment of QoS within IPTS. This deficiency includes aspects such as the definition of QoS criteria levels and the development of approaches assessing service quality for various transport modes.
## 2 Methodology
### Mesh Generation  
Figure 3 shows the meshing of the cell model according to appropriate input and output dimensions with the individual dimensions of each electrolyte and the boundary layers of the SOFC model. The model comprised three main layers with different dimensions and had a small thickness compared with the width of the layers. It had flat boundary edges with 2D linear elements as triangles with straight sides. In the square fuel cell mode, a separate uniform mesh distribution was applied to each boundary layer according to the setting steps on the model for the cathode, anode, and electrolyte layers, with mesh dimensions of \(34\times 120\), \(75\times 108\), and \(75\times 2400\), respectively. These were suitable with the mesh dimensions for the three main layers of the model along with the operating pressure of the electrodes, in which the flow rate of inlet hydrogen was \(200\) mL\(\cdot\)min\({}^{-1}\) (with 3% H\({}_{2}\)O) on the anode and that of oxygen was \(400\) mL\(\cdot\)min\({}^{-1}\) on the cathode. The boundary conditions for the inlet hydrogen electrode side are defined as pressure with no viscous stress. Zero flux is specified in the electrodes and electrolyte layers. The pressures are fixed as atmospheric pressure (1 atm). The boundary conditions at the exits are limited to convective flux. The temperature boundary conditions on the anode and cathode sides are set to the operating temperatures of \(650\)\({}^{\circ}\)C, \(700\)\({}^{\circ}\)C, and \(750\)\({}^{\circ}\)C. After setting boundary conditions and physical establishment for the model, the model is meshed according to the appropriate input and output dimensions with the model shape in accordance with the individual dimensions of each electrolyte and boundary layer. The procedure for developing the simulation model is shown in Figure 4.  
Figure 3: SOFC unit model meshing.  
Figure 4: Diagram illustrating the procedure for the development of the SOFC simulation model.
## 3 Methodology
### Methods  
Assume that the given original network sequence is comprised of a series of time sequence observations \(\mathcal{O}=\{(T_{i},X_{i},Y_{i})\},i=1,2,\cdots,N\) contains network traffic events (\(N\) is the total number of events). \(T\) represents time, \(X\) indicates the variables in the original network traffic sequences with nominal, real numbers, and binary variables, and \(Y\in\{\text{normal, attack}\}\) indicates network events. To identify the underlying patterns of network events over time, the time sequence observation series (\(\mathcal{O}\)) needs to be transformed into time series data with a regular interval. Our approach consists of three steps: (1) generating one-hot encoded variables, (2) constructing time series with a pre-defined time scale \(t_{s}\), and (3) generating forecasting models. A performance evaluation was conducted with different time scales (\(t_{s}=60\) and \(120\) s) to determine the effectiveness of the proposed approach.
## 3 Results  
The activity level of each muscle was significantly affected by body position, limb rise combination, and body side (ES: \(p<10^{-3}\), \(0.71\leq\eta_{p}^{2}\leq 0.81\), \(\omega>0.99\); MF: \(p<10^{-3}\), \(0.73\leq\eta_{p}^{2}\leq 077\), \(\omega>0.99\); EO: \(p<10^{-3}\), \(0.57\leq\eta_{p}^{2}\leq 0.84\), \(\omega>0.99\); IO: \(p\leq 0.002\), \(0.25\leq\eta_{p}^{2}\leq 0.65\), \(\omega\geq 0.93\)). The activity of the superficial muscles (ES and EO) was also significantly affected by all factor interactions (ES: \(p<10^{-3}\), \(0.33\leq\eta_{p}^{2}\leq 0.55\), \(\omega>0.99\); EO: \(p<10^{-3}\), \(0.40\leq\eta_{p}^{2}\leq 0.54\), \(\omega>0.99\)). The difference in activation between body sides showed significant dependence on body position, limb rise condition, and muscle (\(p<10^{-3}\), \(0.53\leq\eta_{p}^{2}\leq 0.70\), \(\omega>0.99\)), and all their interactions (\(p<10^{-3}\), \(0.24\leq\eta_{p}^{2}\leq 0.46\), \(\omega\geq 0.97\)). The key findings from the post-hoc analysis are summarized in the following sections and illustrated in Figures 1-4. Table 1 presents the muscle activation levels recorded in each of the 12 exercise conditions, along with values available in prior studies. The color-coded map in Figure 5 displays a simplified overview of the muscular activation distribution. Figure 1: Leg rise: EMG activity in prone (**a**), quadruped (**b**), and standing-prone position (**c**); EMG activity difference between raised-leg side and contralateral side in prone (**d**), quadruped (**e**), and standing-prone position (**f**). ES = erector spine, MF = lumbar multifidus, EO = external oblique, IO = internal oblique. * Significant difference between body’s sides. Figure 2: Arm rise: EMG activity in prone (**a**), quadruped (**b**), and standing-prone position (**c**); EMG activity difference between raised-arm side and contralateral side in prone (**d**), quadruped (**e**), and standing-prone position (**f**). ES = erector spine, MF = lumbar multifidus, EO = external oblique, IO = internal oblique. * Significant difference between body’s sides. Figure 4: Ipsilateral arm-leg rise: EMG activity in prone (**a**), quadruped (**b**), and standing-prone position (**c**). EMG activity difference between raised limb side and contralateral side in prone (**d**), quadruped (**e**), and standing-prone position (**f**). ES = erector spine, MF = lumbar multififidus, EO = external oblique, and IO = internal oblique. * Significant difference between body sides. Figure 3: Contralateral arm-leg rise: EMG activity in prone (**a**), quadruped (**b**), and standing-prone position (**c**); EMG activity difference between raised-leg side and raised-arm side in prone (**d**), quadruped (**e**), and standing-prone position (**f**). ES = erector spine, MF = lumbar multififidus, EO = external oblique, and IO = internal oblique. * Significant activation between body’s sides.
## 3 Results and Discussion
### Data Processing using Machine Learning Methods
#### 3.4.6 Comparison  
Old parameters distributions within the clusters formed by new features were plotted to compare the resolution of two characteristics sets. The obtained graphs clearly showed that the lateral areas of the distributions overlap substantially, which significantly worsened the quality of classification based on these parameters (Figure 11).  
For the features obtained using autoencoder, the optimal range of values in regard to the Raman spectrum amplification is shown on Figure 12. The figures show the distribution of three random characteristics chosen from the whole experimental data.  
Figure 11: Distribution of substrate main parameters: (**a**) R3z and (**b**) Rku. Number of sites at ordinate and title value at abscissa.  
Figure 10: Surface representations in the middle layer of the autoencoder. Each image corresponds to original image on particular neuron.  
Two central distributions (clusters 1 and 3) correspond to the type V and IV, which lead to spectrum amplification. Compared to the previous plot, more values are not intersected. Both feature sets are used for the development of data-driven models to make connections between extracted values and particular spectrum amplitudes. Finally, we utilized this approach for quick and precise automatic analysis of a vast number of fabricated SERS substrates, which made it possible to find optimal deposition recipe parameters for SERS-active substrates nanoengineering.