esa-sceva/satcom-chunk-collection huggingface dataset:
# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 4 Soft Sensor for Pollution Monitoring
### Mass Air Flow  
In Section 4.2, the method used to estimate the amount of CO\({}_{2}\) was described, and it was found that it was necessary to know the amount of air entering the engine at the moment of combustion to perform the desired calculations. One way to find this value is via the MAF sensor.  
The MAF sensor is able to convert the amount of air entering the motor to a voltage level. This information is useful for maintaining the stoichiometric relationship between the amount of air and fuel in the mixture. This sensor is normally located directly in the vehicle air intake, and the two main types of MAF sensor currently used are the hot wire and vane.  
The hot wire-type sensor is composed of a wire that is heated by an electric circuit. The purpose of this type of sensor is to keep the wire temperature constant. As the air passes through the sensor, the airflow increases and lowers the wire temperature. This effect triggers an increasing of the electric current to reheat the wire, and due to this current variation, the ECU can determine how much air is entering the combustion chamber, adjusting the amount of fuel in the mixture [54].  
The vane-type sensor is older than the hot wire, relying on mechanical and electrical parts. This sensor consists of a flap that partially blocks the air intake of the vehicle. The flap is attached to a potentiometer (adjustable resistance) that changes position as the air flows through the intake, varying the resistance, which, in turn, generates a variation of voltage. Similar to the hot wire sensor, the voltage variation is interpreted by the ECU to determine how much air is entering the combustion, thus adjusting the amount of fuel in the mixture [54].  
For vehicles that have this sensor, it is possible to obtain the MAF value via OBD-II, and it can be used directly in the estimation methodology presented in Section 4.2, obtaining the mass of CO\({}_{2}\) generated during the combustion.
# Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency
## 2 Method
### Cross-Domain Feature Maps Consistency  
Similar to the adversarial approach, we propose to remove style-specific kernel information from the feature maps. However, we additionally exploit the paired nature of the unlabeled dataset \(S_{2}\). Instead of the adversarial loss, we minimize the distance between feature maps of paired images. We use the same notations \(H_{f}\), \(H_{p}\), \(\theta_{f}\), \(\theta_{p}\), and \(\hat{y}=H_{p}(H_{f}(x;\theta_{f});\,\theta_{p})\) as in Section 2.3. Further, we denote the feature vector for every image \(x\) as \(f\), \(f=H_{f}(x;\,\theta_{f})\). For the paired image \(\hat{x}\), we use the similar notation \(\hat{f}\).  
During the training, we minimize the sum of segmentation loss and distance between paired features (\(f\) and \(\hat{f}\)). Thus, the optimization problem is  
\[E(\theta_{f},\theta_{p}) =\sum_{i=1}^{N_{s}}L_{s}(H_{p}(H_{f}(x_{i};\,\theta_{f});\,\theta_ {p}),\,y_{i})\] \[+\,\alpha\sum_{j=1}^{N_{2}}L_{c}(H_{f}(x_{j};\theta_{f}),\,H_{f}( \hat{x}_{j};\theta_{f})) \tag{4}\] \[=\sum_{i=1}^{N_{s}}L_{s}(\hat{y}_{i},\,y_{i})\,+\,\alpha\sum_{j=1 }^{N_{2}}L_{c}(f_{j},\,\hat{f_{j}}),\] \[(\hat{\theta}_{f},\hat{\theta}_{p}) =\operatorname*{arg\,min}_{\theta_{f},\theta_{p}}E(\theta_{f}, \theta_{p}), \tag{5}\]  
where \(L_{s}\) is the segmentation loss (BCE) and \(L_{c}\) is the _consistency_ loss. For the consistency loss, we use mean squared error (MSE) between paired feature maps. Parameter \(\alpha\) regulates the trade-off between two objectives. We call this method _F-Consistency_ since it enforces the consistency between paired feature maps.  
Along with DANN, we present our method schematically in Figure 1(5). Note that we do not need any additional model, e.g., discriminator \(H_{d}\), in the case of F-Consistency. The features are aggregated from the same encoder layers as in DANN.
## 5 Conclusions  
In this study, the effect of hydrophilic and hydrophobic surface coating on the pressure drop, heat transfer, and water splash of fin-and-tube heat exchangers was examined experimentally. Key observations are as follows.  
1. Hydrophilic-coated fins lead to a smaller pressure drop by about 20 to 50% as compared to those of the uncoated cases.
2. Hydrophilic surface coating also leads to slightly smaller heat transfer rates in reference to the uncoated cases and is insignificant due to thin coating.
3. Hydrophilic surface coating is an effective method of mitigating water splash water as the reduction in one to two orders of magnitude and about two times, respectively, with the hydrophobic coated and uncoated surfaces can be achieved. In addition, the threshold splashing speed can be increased from about 2.5 m/s (uncoated) to 3 m/s.
4. Hydrophobic surface coating leads to higher pressure drops (about 55 to 75% for the pitch of 1.5 mm) and larger water splash (about one to two orders) and should be avoided.  
The present results indicate that hydrophilic surface coating with a thickness of about 5 \(\upmu\)m is effective in reducing both water splash and pressure drop while not significantly adversely affecting the heat transfer rate. Thus, the goal of reducing both water splash and energy consumption (by a smaller pressure drop) can be effectively achieved by the hydrophilic surface coating. These findings can be readily applied in industrial HVAC systems to prevent corrosion due to water splash to prolong their lives and also to reduce energy consumption by operating a lower air speed.  
On the other hand, the optimum surface condition, the unexpected pitch effect, and the water splash effect on heat transfer were not investigated in this study. These issues can be further researched so that optimum performance for industrial applications can be implemented to utilize energy effectively and reduce carbon emissions. In addition, the corrosion behavior under humid operation can also be studied to enhance the reliability of HVAC systems.  
Conceptualization, J.-S.C.; Methodology, J.-S.C.; Software, J.-S.C.; Validation, J.-S.C.; Formal analysis, J.-S.C.; Investigation, J.-S.C.; Resources, J.-S.C.; Data curation, J.-S.C., S.-Y.C. and C.-C.C.; Writing--original draft, J.-S.C.; Writing--review & editing, J.-S.C.; Visualization, J.-S.C.; Supervision, J.-S.C.; Project administration, J.-S.C.; Funding acquisition, J.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, Republic of China, grant number MOST 109-2221-E-017-001.  
The authors declare no conflict of interest.
# Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation
## 6 Description and Outcomes of the Simulations
### Incident 3 (A Three-Phase Fault under Islanding Mode)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **EBSABA** & **LMSRE** & **SFO** & & **PSO** \\ \hline \hline \multicolumn{6}{c}{Incident 3 DGR 1} \\ \hline \multirow{4}{*}{Optimum size} & \multirow{4}{*}{online} & \multirow{4}{*}{online} & \(\mathrm{U_{1}}\) & 6.1343 & \(\mathrm{U_{1}}\) & 2.1084 \\ \cline{3-6}  & & & \(\mathrm{U_{2}}\) & 0.0045 & \(\mathrm{U_{2}}\) & 0.0062 \\ \cline{3-6}  & & & \(\mathrm{U_{3}}\) & 2.4984 & \(\mathrm{U_{3}}\) & 2.575 \\ \cline{3-6}  & & & \(\mathrm{U_{4}}\) & 0.1215 & \(\mathrm{U_{4}}\) & 0.113 \\ \hline MPUST & 92.05\% & 92.155\% & 91.65\% & & 93.11\% \\ \hline MPOST & 10.5\% & 12.36\% & 11.69\% & & 11.97\% \\ \hline \(\mathrm{T_{st}}\) & 0.22 s & 0.4912 s & 0.566 s & & 0.812 s \\ \hline \(\mathrm{E_{st}}\) & 0.19\% & 0.257\% & 0.47\% & & 0.549\% \\ \hline \multicolumn{6}{c}{Incident 3 DGR 2} \\ \hline \multirow{4}{*}{Optimum size} & \multirow{4}{*}{online} & \multirow{4}{*}{online} & \(\mathrm{U_{5}}\) & 6.23 & \(\mathrm{U_{5}}\) & 2.179 \\ \cline{3-6}  & & & \(\mathrm{U_{6}}\) & 0.0044 & \(\mathrm{U_{6}}\) & 0.006 \\ \cline{3-6}  & & & \(\mathrm{U_{7}}\) & 2.515 & \(\mathrm{U_{7}}\) & 2.554 \\ \cline{3-6}  & & & \(\mathrm{U_{8}}\) & 0.1193 & \(\mathrm{U_{8}}\) & 0.099 \\ \hline MPUST & 91.59\% & 92.07\% & 91.598\% & & 93.09\% \\ \hline MPOST & 10.31\% & 12.31\% & 11.64\% & & 11.89\% \\ \hline \(\mathrm{T_{st}}\) & 0.214 s & 0.488 s & 0.556 s & & 0.806 s \\ \hline \(\mathrm{E_{st}}\) & 0.187\% & 0.2521\% & 0.4687\% & & 0.546\% \\ \hline \multicolumn{6}{c}{Incident 3 DGR 3} \\ \hline \multirow{4}{*}{Optimum size} & \multirow{4}{*}{online} & \multirow{4}{*}{online} & \(\mathrm{U_{9}}\) & 6.122 & \(\mathrm{U_{9}}\) & 2.23 \\ \cline{3-6}  & & & \(\mathrm{U_{10}}\) & 0.0046 & \(\mathrm{U_{10}}\) & 0.0063 \\ \cline{3-6}  & & & \(\mathrm{U_{11}}\) & 2.475 & \(\mathrm{U_{11}}\) & 2.513 \\ \cline{3-6}  & & & \(\mathrm{U_{12}}\) & 0.1213 & \(\mathrm{U_{12}}\) & 0.0108 \\ \hline MPUST & 91.17\% & 91.86\% & 91.43\% & & 92.88\% \\ \hline MPOST & 10.14\% & 12.22\% & 11.59\% & & 11.83\% \\ \hline \(\mathrm{T_{st}}\) & 0.212 s & 0.483 s & 0.551 s & & 0.795 s \\ \hline \(\mathrm{E_{st}}\) & 0.184\% & 0.25\% & 0.4679\% & & 0.5423\% \\ \hline \end{tabular}
\end{table} 
Table 7: The outcomes of the three DGRs for Incident 3 with EBSABA, LMSRE, PSO, and SFO techniques. Figure 8: (**a–c**) The reference voltages for Incident 3 in the three DGRs with EBSABA, LMSRE, PSO, and SFO techniques. Figure 9: **(a-c)** The complex load powers for Incident 3 in the three DGRs with EBSABA, LMSRE, PSO, and SFO.
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 2 Aircraft Dynamics Model  
### Configuration Description  
Figure 1 depicts the flying-wing UAV that was utilized in this study. The UAV is equipped with two lift fans and a thrust vector mechanism for pitch and yaw control. The lift fans, which are powered by Li-Po batteries, are positioned at a fixed angle at the front of the fuselage. The thrust vector mechanism, consisting of a turbojet engine and a 3 BSD nozzle, is similar to that used in the F-35B [5]. The thrust-to-weight ratio of the UAV, despite having two lift fans, is approximately 0.69. Although the UAV is unable to perform vertical landing as the F-35B does [25], it can accomplish short takeoff and short landing (STOSL) by using its lift fans and vectored thrust. The nozzle pitch angle ranges from 0 to 90\({}^{\circ}\), while the yaw angle ranges from \(-\)15 to 15\({}^{\circ}\). The use of vectored thrust is restricted to the takeoff and landing phases, thus resulting in an improved takeoff and landing performance. During cruise flight, the nozzle remains level, and the rear engine door is shut, allowing the UAV to function as a standard flying-wing aircraft.  
Figure 2 displays the aerodynamic control surfaces utilized in this study; they consist of four elevons for controlling pitch and roll, segmented into elevators and ailerons. In addition, two spoiler-slot deflectors (SSDs) are employed to govern yaw motion. Relevant aircraft parameters are concisely summarized in Table 1.  
Figure 1: General diagram of the configuration of the subject flyingwing UAV.
## 3 Experiment Analysis of Transition Zones
### Measurement Results
The moisture susceptibility of the ballast and the subballast is significantly increased, both at the bottom of ballast (No. 11) and  
Figure 10: Measurement results of Transition Zone C: (**a**) photograph of the track; (**b**) the moisture profile; and, (**c**) the longitudinal track level. Figure 9: Measurement results of Transition Zone B: (**a**) photograph of the track; (**b**) the moisture profile; and, (**c**) the longitudinal track level. bottom of subballast (No. 10 and No. 12), as seen in Figure 10b. Severe track irregularity also appears through the entire transition zone, as shown in Figure 10c. In summary, high moisture areas appear mostly before and after the bridges in all of the transition zones. They can be detected sometimes at the bottom of the ballast layer, bottom of subballast layer, and above the abutments. Track irregularities can also be found at corresponding locations, which implies a connection between high moisture condition and track degradation. When the moisture is high, the track irregularities are higher (see the embankment-bridge side of Transition Zone C, in Figure 10); when the moisture is low, the track irregularities are also low (see the bridge-embankment side of Transition Zone B, Figure 9). Since the theoretical findings in Section 2 suggest that a high moisture in ballast leads to a reduction of stiffness, the high moisture in the structure can be considered as one of the sources for the extra degradation in transition zones. It should be noted that the high moisture is not the only source leading to the extra degradation, for instance, the large track irregularity above the approaching slab in the embankment-bridge side in Transition Zone B (Figure 9) is not caused by the moisture condition.
## 2 Problem Formulation and Preliminaries
### Lemmas and Assumptions  
Consider the Following Dynamical System  
\[\dot{x}(t)=\boldsymbol{f}(x,t),\ \boldsymbol{f}(0,t)=0,x\in\mathbb{R}^{n} \tag{1}\]  
where \(\boldsymbol{f}\colon\mathbb{R}^{n}\times\mathbb{R}^{+}\to\mathbb{R}^{n}\) is a continuous nonlinear function, and the initial condition is denoted as \(x(0)=x_{0}\).  
If there exists a predetermined constant \(T_{\max}>0\) and \(\delta>0\), such that any solution \(x(t,x_{0})\) of the system satisfies \(\|x(t;x_{0})\|\leq\delta\) for all \(x_{0}\in\mathbb{R}^{n}\) when \(t\geq T_{\max}\), then the origin of the system is said to be uniformly globally stable.  
**Theorem 1**.: _Assume there exists a Lyapunov function \(V(x,t)\) defined on \(\mathbb{R}^{n}\times\mathbb{R}^{+}\) that satisfies the following conditions_  
\[\dot{V}(x)\leq-\frac{\pi}{\beta T_{c}}V^{1+\frac{\beta}{2}}-\frac{\pi}{\beta T _{c}}V^{1-\frac{\beta}{2}}+\mathrm{D} \tag{2}\]  
_In this case, \(0<\beta<1,\mathrm{D}>0\) are constants. The formula \(T_{c}=\left(\sqrt{2}\right)^{-1}T_{\max}\) indicates that the solution of the system remains stable within a predefined-time interval [11]. The system state converges to the following set of differences:_  
\[\Omega=\left\{\lim_{t\to T_{\max}}x\bigg{|}V\leqslant\min\left\{\left(\frac{ 2\beta T_{c}\mathrm{D}}{\pi}\right)^{\frac{2}{2-\beta}},\left(\frac{2\beta T_ {c}\mathrm{D}}{\pi}\right)^{\frac{2}{2+\beta}}\right\}\right\} \tag{3}\]  
**Theorem 2 ([19]).**_Define the variables \(y_{1},y  
**Theorem 4.**_For any defined \(x>0,h>0\), and \(y>0\), the following inequality holds_  
\[x^{2h}-y^{2h}\leq-(x-y)^{2h}+2x^{2h} \tag{7}\]  
**Theorem 5 ([21]).**_Young's Inequality_  
_For positive values \(a,b\geq 0\) and real numbers \(p,q>1\), the following inequality holds_  
\[ab\leq\frac{a^{p}}{p}+\frac{b^{q}}{q} \tag{8}\]  
**Theorem 6 ([22]).**_Radial basis function networks (RBFNs) can be used to approximate continuous functions \(f_{NN}\), expressed as follows_  
\[\hat{\mathbf{f}}_{NN}(\mathbf{x})=\hat{\mathbf{W}}^{T}\mathbf{h}(\mathbf{x})+\mathbf{B} \tag{9}\]  
_where \(x=[x_{1},x_{2},\ldots,x_{n}]^{T}\) is the input vector of the RBFNNs, and \(\mathbf{h}_{j}(\mathbf{x})=\exp\left(-\frac{\|\mathbf{x}-\mathbf{\mu}\|^{2}}{2b^{2}}\right)\) is the radial basis function. For RBFNs, the following applies: let \(\mathbf{B}=\left[b_{1},b_{2},\ldots b_{m}\right]^{T}\) be the output bias, and \(\hat{\mathbf{W}}\) is used for estimating optimal weights. In the weight estimation, there exists an optimal weight vector \(\mathbf{W}^{*}\) such that_  
\[\mathbf{f}_{NN}=\mathbf{W}^{*T}\mathbf{h}(\mathbf{x})-\mathbf{\varepsilon} \tag{10}\]  
_where \(\mathbf{\varepsilon}\) represents the approximation error of the neural network, satisfying \(|\mathbf{\varepsilon}|\leq\overline{\mathbf{\varepsilon}}\)._
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 2 M-Health Environments and Device-to-Device (D2D) Communication
This issue has become more relevant because authentication protocols designed by traditional communication systems (e.g., 3 GPP Evolved Packet System-Authentication and Key Agreement (EPS-AKA)) are not adequate for common requirements of 5G networks (e.g., higher data rate, low delay, low energy consumption and traffic offloading). Another important characteristic of D2D communication is possible coverage extension (see Figure 1, where a D2D-capable device outside the coverage area of the cellular network can be aided by another device inside that coverage area).
## 5 Conclusions  
In this study, an experimental test setup for the FSO link performance during winter, spring, pre-summer, and summer seasons of Qatar in terms of PDR was presented. It was found that the FSO link performs well in winter than in summer. Also, the behavior of FSO link with the strength of atmospheric turbulence was correlated. The variation of refractive index with Qatar's weather factors was also studied. It was found that even during winter, the strength of atmospheric turbulences is considered high but not high enough to alter the FSO performance. Furthermore, considering the high temperature and solar irradiance recorded during summer, it was concluded that they are the two major environment factors affecting the FSO performance and should be considered critical during installation and operation of FSO communication links. The results were validated with statistics and the statistics were used to provide an accurate closed form expression to estimate the PDR using the environmental parameters during summer in Qatar. Many techniques can be used to improve the performance such as adding an RF link or using channel coding. The authors are investigating the implementation of channel coding technique such as the Raptor codes, which belongs to the family of fountain codes. Hybrid FSO/RF system for soft switching configuration based on channel coding can also be implemented is also considered.  
Experiments were designed by [PERSON],[PERSON],[PERSON],A.K.,A.B.; Experiments were performed by [PERSON], Results were analyzed by [PERSON],A.T.,F.T.,A.A.,A.B.; All authors were involved in interpretation of data and paper writing.  
The publication of this article was funded by the Qatar National Library.  
The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 3 Data Sources and Quality  
### Common Sources of Data  
The most common source of data for crash analysis are reports filed by Law Enforcement Officers at the scene of a crash. Several researchers have also used hospital records of traffic injuries, and in some countries, this is standard practice, e.g., the Netherlands and Sweden. Insurance records are another, less commonly used source of crash data [14].  
Naturalistic driving data, i.e., real-world driving data collected without experimental controls or driver awareness, are also available for limited time periods in a small number of locations. Video of highways [15; 16] and intersections [17] are examples of studies based on naturalistic data. A key challenge with naturalistic data is that they are expensive to collect and therefore not available for comprehensive analysis across geographies and time.  
Another emerging source is telematics--data collected automatically by vehicles or electronic devices installed in vehicles. This type of data is often collected at the initiative of insurance companies in return for insurance premium discounts and provides real-time information about driving behavior and vehicle performance. One provider of telematics data has looked into whether drivers change their behavior to take advantage of discounts [18], but further research should be performed to determine the usefulness of such data at scale.  
A significant development in the collection of crash data is the mandate that all new cars sold in the European Union (EU) after 7 July 2024 must be equipped with an Event Data Recorder (EDR), an onboard device that records information about a vehicle's operation before, during, and after a crash. At the time this paper was written, research on EDR data was limited, but preliminary results [19] show promise. Figure 2 provides an overview of data sources used in crash research.
# Behaviour of the Steel Welded Grid during a Simplified Pullout Test in Fine Sand
###### Abstract
the force is transmitted along the wires and their cross-sectional area directly affects the mesh elongation during its stretching. It turns out that after building in the soil medium, both types of mesh (woven and welded) are characterized by similar values of relative deformation. Pullout tests are dedicated to the determination of tensile forces, and consist of building a reinforcement mesh in a box with soil, applying a vertical load, simulating the weight of the entire geotechnical structure, and then slowly ejecting the reinforcement through the slot in the box, while simultaneously measuring both force and displacement. In this type of research, woven meshes exhibit a different mechanism of cooperation with the ground from welded meshes [29]. All these premises were the starting point for this research paper, which focuses on the observation of the behaviour of welded steel mesh during a simplified pullout test in fine sand. Pullout tests are performed to determine the coefficient of friction between the reinforcement and the soil, the value of which is essential for the correct and safe design of reinforced soil structures [30]. In construction site practice, woven and welded meshes are commonly used in the construction of gabion retaining structures. In the case of retaining structures with soil reinforcement, a double-woven mesh is typically used if the baskets are also made of it, or geosynthetics, regardless of the material of the gabion basket. Gabion welded mesh for soil reinforcement was not used. Although welded mesh has been successfully used to reinforce soil in light retaining structures since the 1980s, it is made of wires with a much larger diameter than those used for gabion basket production. The essential issue in the design, performance, and durability of the use of a structure with soil reinforcement is the connection of the wall facing with the soil reinforcement. This connection is often the weakest point in terms of strength, effectively limiting the use of the total load-bearing capacity of the soil reinforcement, and is the site of potential failure during use. Integrating the facing with the reinforcement (e.g., extending the base of the gabion to such a length that it is also the reinforcement of the soil) makes it possible to eliminate the problematic connection, as is currently the case in structures where woven meshes are used to make gabions and reinforce soil. The use of welded steel meshes as backfill reinforcement seems to be, of course, the next step in the development of this technology. This is important because welded meshes can achieve higher tensile strength than braided meshes due to the technological possibility of using thicker wire for their production.