esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Experimental Setups
#### 3.1.1 Development of the Reward Function in the Study  
Before starting the training process using the scenarios created with the parameters shown in Table 3, the design of the reward function is highly important. The primary goal in reward design is to ensure that the vehicle completes its route in city traffic as quickly as possible and to engage in acceleration actions that minimize the waiting time at traffic lights while achieving this goal. When this goal is achieved, increasing safety and comfort during travel time, as well as ensuring minimum fuel consumption, is an important focus. To achieve this goal, a penalty point of \(-1\) is given at each step, and the waiting time is multiplied by \(-1\) to give a penalty point. The distance covered/fuel consumed and the distance covered/emission amount at each step are also given as rewards. These penalty points are applied to encourage the vehicle to avoid certain acceleration actions and maintain its optimal performance. The formula expressing the reward function is expressed in Equation (1).  
\[\text{Reward}=\sum_{i=0}^{n}r_{i}+d_{i}/ca_{i}+d_{i}/fe_{i}-wt\times 1+bgrw \tag{1}\]  
In Equation (1), the symbol \(r_{i}\) denotes the total reward at step I, \(d_{i}\) represents the distance traveled up to step I, \(ca_{i}\) signifies the amount of carbon dioxide emitted at step I, \(fe_{i}\) indicates the fuel consumed, wt denotes the waiting time, and bgrw stands for the grand reward at the end of the section. The formula for the grand reward is presented in Equation (2).  
\[bgrw=\left\{\begin{array}{ccc}\max\_steps-steps&\text{steps}<\max\_steps \\ 1000&\text{length}==\text{distance}\\ (\text{length}-\text{distance})\times-1&\text{others}\end{array}\right\} \tag{2}\]  
In the formula expressed in Equation (2),'steps' represents the number of steps taken from the start until the termination condition is met, '\(max\_steps\)' represents the maximum number of steps, 'length' represents the length of the path, and 'distance' represents the path taken until the termination condition is met. If the vehicle completes the entire path on time, it is awarded 1000 points. If it cannot complete on time, it receives penalty points equal to the remaining distance, and if it completes ahead of time, it receives a reward equal to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Scenario 1** & **Scenario 2** & **Scenario 3** \\ \hline Distance (m) & 6600 & 8700 & 15,000 \\ Number of Traffic Lights & 4 & 11 & 10 \\ Traffic light group name & Scenario 1 & Scenario 2 & Scenario 3 \\ Car & 53\% & 53\% & 53\% \\ Truck & 14\% & 14\% & 14\% \\ Bus & 33\% & 33\% & 33\% \\ Total number of Steps & 4500 & 4500 & 4500 \\ Number of states & 14 & 14 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the scenarios used in the study.  
remaining time. The grand prize of 1000 full points was determined because it determined the travel time at the optimum level with ideal fuel consumption. In this study, the restrictive parameter was set as Vmax = 50 km/hour, which is the speed limit for city traffic.
# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 3 Materials and Methods
### Bloom Filter
As a result, the value of \(k\) that minimizes the false positive probability \(p\) for a given \(m\) and \(n\) is (see Equation (5)):  
\[k=\frac{m}{n}\ln 2. \tag{5}\]  
The number of bits in a BF \(m\) must expand linearly with the number of elements inserted in the filter \(n\) to maintain a stable likelihood of false positives. As a result, for the desired number of items \(n\) and false positive probability \(p\), the filter length \(m\) is (see Equation (6)):  
\[m=-\frac{n\ln p}{(\ln 2)^{2}}. \tag{6}\]  
We can calculate the number of hash functions \(k\) and filter size \(m\) based on those parameters using Equations (4)-(6), as the expected number of different readings that must be inserted into a BF can be obtained from MCS service requirements and a priori known data, and we can set the desired probability of false positives \(p\) for an MCS service. Removing an element from a BF is not possible because false negatives are not permitted. To remove an element from a filter, we need to set any of \(h_{i}(r)\) bits to 0. However, if other elements in a filter are mapped on that bit, they would also be removed, introducing the possibility for false negatives. This BF characteristic is in line with the MCS behavior, as only readings that have already been sent to an ME host are encoded in the Bloom filter (and processed by the ME host), and therefore they cannot be revoked.
###### Abstract  
A theoretical and experimental study on the stimulated Brillouin scattering (SBS) threshold for optical pulse in the presence of modulation instability (MI) in long optical fiber is presented. The effects of MI on the SBS gain and threshold are analyzed based on the coupled-wave equation. An analytic expression is obtained to calculate the SBS threshold in the presence of MI. Numerical simulation is conducted to study the effects of the repetition rate and amplified spontaneous emission (ASE) noise on the SBS threshold for optical pulses. An experiment is conducted, and the results agree well with the theoretical analysis. The results clearly reveal how MI affects the Brillouin gain and SBS threshold of optical pulses in long optical fiber. The SBS threshold is affected by the ASE noise level and the repetition rate of the optical pulse. The study is helpful for the power evaluation of interferometric fiber sensing systems and optical power transmission systems.  
nonlinear fiber optics; stimulated Brillouin scattering; modulation instability +
Footnote †: journal: Applied Sciences
## 2 Manufacturing System Context
### Flow Shop
#### 2.4.1 Permutation Flow Shop  
The permutation flow shops are characterized by a fixed order of the jobs through the machines [66].  
Controlling power peak is an important issue in energy efficiency. A scheduling model that considers peak power as a constraint and makespan as the objective function was discussed in [67]. [PERSON] and [PERSON] [68] considered, as objectives of the scheduling problem, the maximum completion time and total energy consumption and addressed the optimization using an evolutionary algorithm based on decomposition. The flow shop analyzed was characterized by the setup time of each job depending on the sequence.  
[PERSON] et al. [69] extended the permutation flow shop scheduling problem, considering in their model, transportation time, sequence-dependent setup time, and energy consumption in the setup stage and in the transportation phase and in the public period with the classical time and energy features. A hybrid multi-objective backtracking search algorithm was provided to solve the scheduling model.  
A mixed integer linear programming model was developed in [70], considering as objective function the minimization of the total flow time and of total energy consumption and assuming a machine variable processing speed. To address the problem for small instances, an \(\varepsilon\)-constraint approach was proposed, whereas for the large instance problems, an \(\varepsilon\)-constraint approach and two iterated greedy algorithms, a variable block insertion heuristic algorithm, and construction heuristic procedure were provided. [PERSON] et al. [71] developed a hybrid whale optimization algorithm in order to minimize energy consumption in a permutation flow shop scheduling with a dependent sequence setup. [PERSON] and [PERSON] [72] considered the simultaneous minimization of the makespan and total energy consumption in the no-wait permutation flow shop scheduling problem. In their study, machine processing speeds were adapted, respecting the objective functions, and an adaptive multi-objective variable neighborhood search algorithm was used to achieve the Pareto front.  
In the same manufacturing context, total energy consumption and the total tardiness were considered as objective functions and three metaheuristic algorithms (an ant colony algorithm, a genetic algorithm, and a genetic algorithm with local search) are provided in [73].  
The distributed permutation flow shop is a general case of a permutational flow shop characterized by several identical factories, each of them consisting of the same machines arranged in series [74]. [PERSON] et al. [75] proposed a scheduling model for the distributed no-idle permutation flow shop context, with the aim of minimizing total energy consumption and the makespan using a collaborative optimization algorithm. The same targets were considered in [76] regarding distributed permutation flow shop. In this work, the problem is solved using a knowledge-based cooperative algorithm.
# Dynamic Modeling and Simulation of a Cyclic Towing System for Underwater Vehicles
## 5 Conclusions  
This paper derives dynamic differential equations of a cyclic towing system based on Lagrange equations of the second kind. The fluid resistance coefficient of the underwater vehicle was calculated using the computational fluid dynamics software Fluent, and the resistance was then calculated using the fluid resistance formula. A dynamic model of a cyclic towing system for an underwater vehicle was thus established. The model's validity was verified through comparison with the commercial software ADAMS. Furthermore, the effect of different input torques and tension forces on the system's operation were discussed. The following conclusions were drawn:  
1. The dynamic model of the cyclic towing system for an underwater vehicle can predict the vehicle's displacement, velocity, cable tension, and cable deformation. In different case studies with varying input torques, the speed fluctuations of the underwater vehicle are significantly influenced by the flexibility and elasticity of the cable. As the input torque increases, the deformation of the cable intensifies, leading to more pronounced speed fluctuations.
2. Regarding the tension force applied to the cable by the tensioning device, as the tension force increases, the operation of the underwater vehicle during the constant speed phase becomes more stable. The tension force prevents the cable from becoming overly slack. However, there is an optimal range where the tension force effectively suppresses speed fluctuations.
3. To calculate the deformation of the cable during system operation, this paper adopts the Kelvin-Voigt mechanical model for the cable. Unlike the discrete model used in commercial software such as ADAMS, the proposed method accurately computes the cable's deformation. From Case Study 1 and 2, it is evident that the cable tension increases with an increase in input torque and tensioning force, and the cable's deformation also increases. The maximum cable tensions observed were 9424 N and 8686 N, respectively, corresponding to maximum elongation rates of 1.2% and 1.09%.  
The simulation method presented in this paper can predict the dynamic characteristics of the system under different input torques and tension forces, providing theoretical guidance for the design, development, and improvement of cyclic towing systems.  
Methodology, X.L.; Software, X.L.; Validation, X.L.; Formal analysis, X.L.; Writing--original draft, X.L.; Writing--review & editing, S.H.; Supervision, S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Additive Manufacturing for Sensors: Piezoresistive Strain Gauge with Temperature Compensation
## 2 Materials and Methods  
### Fabrication of the 3D-Printed Strain Sensor  
The fused filament fabrication (FFF) technology has been used to manufacture in a single shot both the piezoresistive strain gauge and the dielectric support structure (Figure 1). The dual-extruder Ultimaker S5 (Ultimaker, Utrecht, The Netherlands) was exploited in conjunction with a soft material (polyurethane thermoplastic, henceforth TPU) and a conductive material (polylactic acid matrix doped with carbon nanotubes, henceforth CPLA). Thanks to the dual-extruder FFF machine, two filaments can be extruded in the same printing cycle (paper [28] provides a comprehensive review of the 3D printer set-up allowing multi-material extrusion on the market). As shown in scientific literature, the adhesion between the two materials is a crucial point. It depends on several factors such as materials affinity, chemical composition, and process parameters [29; 30] which have been taken into account during the manufacturing process.  
Two identical samples were manufactured: more details about the dimensions, the printing strategies, and the process parameters can be found in [21].  
It is worth mentioning that the process parameters have been chosen to minimize the final electrical resistance and its variability, as shown in [21].
## 2 Description of the Experiment
### Thermodynamics Approaches for Prediction of Phase Behavior  
The accuracy of equilibrium and rate-based models depend on the accurate prediction of the phase behavior properties of chemical species and their mixtures [19]. There are two conventional approaches for the prediction of phase behavior: approach (\(\varphi,\varphi\)) and approach (\(\varphi/\gamma\)) [38]. By approach (\(\varphi,\varphi\)), the fugacity coefficient \(\varphi\). is applied for predicting the non-ideal behavior of both vapor and liquid phases [19]. The fugacity coefficient is calculated at the equilibrium condition according to [PERSON] et al. [38] as below:  
\[f_{i}^{V}=f_{i}^{L} \tag{32}\]  
\[f_{i}^{V}=\varphi_{i}^{V}y_{i}P \tag{33}\]  
\[f_{i}^{L}=\varphi_{i}^{L}x_{i}P \tag{34}\]  
\[ln\varphi_{i}^{a}=-\frac{1}{RT}\int_{\infty}^{Va}\!\!\!\left[\left(\frac{ \partial p}{\partial n_{i}}\right)_{T,V,n_{i,j}}-\frac{RT}{V}\right]\!dV-lnZ_{ m}^{a} \tag{35}\]  
where \(f_{i}^{L}\) and \(f_{i}^{V}\) are Fugacity of component \(i\) in the liquid and gas phase respectively, \(\varphi_{i}^{L}\) and \(\varphi_{i}^{V}\) are Fugacity coefficient of the component \(i\) in liquid and gas phase respectively, \(P\) is pressure, \(x_{i}\) and \(y_{i}\) mole fraction of the \(f\) component \(i\) in liquid and gas phase respectively, \(\varphi_{i}^{a}\) is Fugacity coefficient of the component \(i\) where \(a\) refer to liquid or gas phase, \(V\) is total volume, \(R\) is Gas constant it has the value \(0.08314\) [L bar K\({}^{-1}\)], \(T\) is temperature, \(n_{i}\) is mole number of component \(i\), \(Z_{m}^{a}\) Compression factor.  
The approach (\(\varphi/\gamma\)) uses the fugacity coefficients (\(\varphi\)) and the activity coefficients (\(\gamma\)) to account the non-ideal behavior of vapor and liquid phase. The equations related to (\(\varphi/\gamma\)) approach are listed as below [38]:  
\[f_{i}^{V}=f_{i}^{L} \tag{36}\]  
\[f_{i}^{V}=\varphi_{i}^{V}y_{i}P \tag{37}\]  
\[f_{i}^{L}=x_{i}\gamma_{i}f_{i}^{\gamma I} \tag{38}\]where \({f_{i}^{*}}^{I}\) is liquid fugacity of pure component \(i\) at mixture temperature, \(\gamma_{i}\) is the liquid activity coefficient of component \(i\). While \(\varphi_{i}^{V}\) is calculated according to Equation (26), the activity coefficients can be calculated from non-random two liquid model (NRTL) as below [39]:  
\[ln\gamma_{i}=\frac{\sum_{j=1}^{n}x_{j}\tau_{ji}G_{ji}}{\sum_{k=1}^{n}x_{k}G_{ki} }+\sum_{j=1}^{n}\frac{x_{j}G_{ij}}{\sum_{k=1}^{n}x_{k}G_{kj}}\Bigg{(}\tau_{ij} -\frac{\sum_{m=1}^{n}x_{m}\tau_{mj}G_{mj}}{\sum_{k=1}^{n}x_{k}G_{kj}}\Bigg{)} \tag{39}\]  
\[G_{ij}=exp\Big{(}-a_{ij}\tau_{ij}\Big{)} \tag{40}\]  
\[\tau_{ij}=a_{ij}+\frac{b_{ij}}{T}+e_{ij}lnT+f_{ij}T \tag{41}\]  
\[a_{ij}=c_{ij}+d_{ij}(T-273.315K) \tag{42}\]  
Here, \(a_{ij}\) is NRTL non-randomness constant for binary interaction, \(a_{ij},\ b_{ij},\ c_{ij},\ d_{ij},\ e_{ij},\ \text{and}\ f_{ij}\) are binary parameters. The ASPEN PLUS physical property system has extensive property databanks for binary parameters for the model.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 4 Model Situation
### Results of Models of Individual Scenarios
#### 4.1.2 Scenario 1.2. High-Concentration Ammonia Leak with Fatal Consequences
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Danger Zone**} & **Concentration Level** & **Equivalent** \\  & **(ppm)** & **Concentration** \\  & **(mg.m\({}^{-3}\))** & **Range (m)** \\ \hline ERPG-2 & 2160 & 1504,498 & 89 \\ ERPG-1 & 300 & 208,958 & 254 \\ \hline \hline \end{tabular} Note: Colour coding used for hazard zones—yellow (ERPG-1, low risk), orange (ERPG-2, moderate risk), and red (ERPG-3, high risk), reflects increasing levels of risk due to ammonia concentration.  
\end{table} 
Table 3: Danger zones for the NH\({}_{3}\) leak scenario at the skating arena, Scenario 1.2. The IDLH (Immediately Dangerous to Life or Health) zone in this scenario includes areas where ammonia concentrations exceed critical values, posing an immediate threat to life and health. The mathematical relationship between exposure time (\(t\)) and concentration (C) can be described as inversely proportional in life-threatening situations:  
\[t\propto\frac{1}{C} \tag{4}\]  
This relationship illustrates that as the concentration of ammonia increases, the time available for safe exposure decreases rapidly. Emergency response teams must account for this dynamic when planning evacuations and interventions. Scheme 2 and Figure 3 show the dispersion patterns of ammonia for Scenario 1.2, with color-coded zones representing varying levels of risk. These visual models clearly demonstrate that, under the conditions of Scenario 1.2, the elevated concentrations necessitate immediate evacuation and the swift deployment of specialised emergency units. The modelling results for Scenario 1.2 highlight the importance of prompt intervention and evacuation, especially as ammonia concentrations reach hazardous levels. The data presented in Table 3 delineate the ranges of danger zones and their associated risks, illustrating the necessity for precise, timely response strategies in this scenario. Scheme 2 provides a graphical representation of the ammonia dispersion zones for Scenario 1.2, illustrating concentration levels across the affected areas. Figure 3, showing a geospatial model of hazard zones, employs color-coded areas (red, orange, and yellow) to visually represent the varying levels of risk. The red ERPG-3 zone represents the highest level of danger, while the orange ERPG-2 and yellow ERPG-1 zones indicate progressively lower risks. The use of Google Earth for visualisation, as depicted in Figure 3, helps emergency planners precisely identify critical areas, enabling them to implement protective measures promptly and efficiently. Table 4 consolidates data from both Scenario 1.1 and Scenario 1.2, providing an overview of hazard zones. The table includes essential variables, such as wind speed, air temperature, leak duration, and the mass of leaked ammonia, which were key factors in
## 3 Reliable Robust \(H_{\infty}\) Controller Design
\begin{array}{cccc}\overline{\overline{\Lambda}}_{11}&\mathbf{B}_{1i }&\overline{\overline{\Lambda}}_{13}&\eta_{ij}\mathbf{B}_{2i}&\overline{ \mathbf{k}}_{j}^{T}\xi_{0}^{T}\\ *&-\gamma^{2}\mathbf{I}&0&0&0\\ *&*&-\mathbf{I}&\eta_{ij}\mathbf{D}_{1i}&0\\ *&*&*&-\eta_{ij}\mathbf{J}^{-1}&0\\ *&*&*&*&-\eta_{ij}\mathbf{J}^{-1}\end{array} \right]<0\;i,j=1,2,3,4 \tag{45}\]  
\[\left[ \begin{array}{cccc}-\mathbf{I}+\varepsilon\mathbf{J}&\sqrt{\rho}\xi_ {0}\overline{\mathbf{k}}_{j}&0\\ *&-u_{\max}^{2}\overline{\mathbf{P}}_{j}&\sqrt{\rho}\overline{\mathbf{k}}_{j} ^{T}\xi_{0}^{T}\\ *&*&\varepsilon_{j}\mathbf{J}^{-1}\end{array} \right]<0\;j=1,2,3,4 \tag{46}\]  
\[\left[ \begin{array}{cccc}-\mathbf{I}&\sqrt{\rho}\mathbf{C}_{2}\overline{ \mathbf{P}}_{j}\\ *&-z_{\max}^{2}\overline{\mathbf{P}}_{j}\end{array} \right]<0\;i,j=1,2,3,4 \tag{47}\]  
where  
\[\overline{\overline{\Lambda}}_{11}\mathbf{=sym}(\mathbf{A}_{i}\overline{ \mathbf{P}}_{j}+\mathbf{B}_{2i}\xi_{0}\overline{\mathbf{k}}_{j}),\;\overline{ \overline{\Lambda}}_{13}=\overline{\mathbf{P}}_{j}\mathbf{C}_{1i}^{T}+ \overline{\mathbf{k}}_{j}^{T}\xi_{0}^{T}\mathbf{D}_{1i}^{T}.\]  
Thus, by solving the following convex optimization problem, the robust state-feedback controller design can be accomplished. \(\min\;\gamma\)  
s.t. inequalities (45)-(47), \(\overline{\mathbf{P}}_{j}>0\), \(\eta_{ij}>0\), \(\varepsilon_{j}>0\)  
for \(\overline{\mathbf{k}}_{j}\), \(\mathrm{i,j=1,2,3,4}\),  
The gain matrix of the controller can be expressed by  
\[\mathbf{K}=\xi\left(\sum_{j=1}^{4}a_{j}(\lambda)\overline{\mathbf{k}}_{j} \right)\left(\sum_{j=1}^{4}a_{j}(\lambda)\overline{\mathbf{P}}_{j}\right)^{-1} \tag{48}\]  
This convex optimization problem can be solved by the function mincx provided by MATLAB. Employing a similar method to what is proposed in Theorem 1, the theorem 2 can be achieved for the active suspension of the hub-driven EVs without actuator faults and parameter uncertainties. **Theorem 2**. : _Given positive constant \(\rho\), a \(\mathrm{H}_{\infty}\) controller exists, such that the closed-loop system is asymptotically stable and satisfies \(\|\mathbf{z}_{1}(t)_{2}\|<\gamma\|w(t)\|_{2}\) for all \(\mathbf{w}\) satisfying \(\|w(t)\|_{2}^{2}\leq\|w(t)\|_{max}=\rho/\gamma^{2}\), while the constrains in inequalities (22-24) are guaranteed, if there exist symmetric matrices \(\mathbf{P}\) and general matrix \(\mathbf{k}\) satisfying_  
\[\left[ \begin{array}{cc}\mathrm{sym}\left(\mathbf{A}\mathbf{P}+\mathbf{B}_{2}\mathbf{k}\right) &\mathbf{B}_{1}&\mathbf{PC}_{1}^{\mathsf{T}}\mathbf{+k}^{T}\mathbf{D}_{1}^{\mathsf{T}}\\ *&-\gamma^{2}\mathbf{I}&0\\ *&*&-\mathbf{I}\end{array} \right]<0 \tag{49}\]  
\[\left[ \begin{array}{cc}-\mathbf{I}&\sqrt{\rho}\mathbf{k}\\ *&-u_{\max}^{2}\mathbf{P}\end{array} \right]<0 \tag{50}\]  
\[\left[ \begin{array}{cc}-\mathbf{I}&\sqrt{\rho}\mathbf{C}_{2}\mathbf{P}\\ *&-z_{\max}^{2}\mathbf{P}\end{array} \right]<0 \tag{51}\]  
Thus, the feedback gain matrix \(\mathrm{K}_{2}\) of the controller can be given by \(\mathbf{K}_{2}=\mathrm{k}P^{-1}\).
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Cellular Network Data Mining  
Cellular networks provide coverage for both indoor and outdoor areas, but the type of base stations used varies by environment. Suburban outdoor areas typically rely on macro-base stations, while urban outdoor areas often utilize micro-base stations. In contrast, indoor areas generally employ picocell base stations. These differences in base station design and transmit power significantly impact the coverage of cellular networks indoors and outdoors [28].  
Figure 9b illustrates the number of cellular network signals received by two different devices in various indoor and outdoor environments. The overall fluctuation indoors  
Figure 8: Visible AP distribution of Wi-Fi: (**a**) number distribution, (**b**) signal strength distribution.  
Figure 9: Variation of light sensors and cellular network sensors: (**a**) variation of indoor and outdoor light intensity over 24 h, (**b**) variation of the number of base stations receiving signals.  
is minimal, and the number of signals received is relatively low. This is primarily due to the limited number of pico-base stations and room substations available indoors. In contrast, outdoor areas experience greater fluctuations and a higher number of received signals, as they can access macro-base stations, micro-base stations, and a smaller number of pico-base stations.  
From this analysis, we can deduce that the variance, peaks, valleys, and average of the signal strength, as well as the number of cellular base stations within a specified sliding window, can be used as key features for scene classification.