esa-sceva/satcom-chunk-collection huggingface dataset:
# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 2 Related Works
\begin{table}
\begin{tabular}{l l} \hline \hline
**Work** & **OBD-II** \\ \hline
1. [42] & Not used, readings were performed directly from CAN instead \\
2. [43] & It was used to perform the vehicle sensor readings \\
3. [46] & It was used to perform the vehicle sensor readings \\
4. [47] & Not used. Instead, they used a sensor coupled to the exhaust \\ \hline
**Work** & **Real Time** \\ \hline
1. [42] & Does not work in real time. The authors performed the estimation after a collection period \\
2. [43] & Does not work in real time, and the data were processed offline \\
3. [46] & Works in real time. The CO\({}_{2}\) amount was estimated as the data were collected \\
4. [47] & Does not work in real time, and the data were processed offline \\ \hline
**Work** & **Data Storage** \\ \hline
1. [42] & Data stored on a local server \\
2. [43] & Data collected on an SD card and stored on a local server \\
3. [46] & Stored directly on a computer that was inside the vehicle and connected to the output of the OBD-II adapter \\
4. [47] & Stored directly on a computer that was inside the vehicle and connected to the sensor output \\ \hline
**Work** & **Observed Sensors** \\ \hline
1. [42] & Fuel level and RPM \\
2. [43] & Fuel level, MAE, speed, acceleration, and throttle position \\
3. [46] & MAF \\
4. [47] & Gas analyzer \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works. * Calculating the level of pollution using methods that rely on indirect sensor data (i.e., using sensors that do not directly calculate the emission of CO\({}_{2}\)).
# Mechanical Behavior of Steel Fiber-Reinforced Lightweight Concrete Exposed to High Temperatures
## 3 Test Results and Discussions
### Energy Absorption Capacity (Toughness)  
In the current work, the stress-strain curves for concrete were recorded until the load was decreased to one third of the peak load (i.e., 0.33\(f_{\text{c0}}\) of the descending branch, known as \"0.33\(f_{\text{c0}}\) point\"), and the concrete energy absorption capacity (toughness) under uniaxial compression was determined based on the area under stress-strain curve before the 0.33\(f_{\text{c0}}\) point. Figure 12 shows the toughness test results under different temperatures. The coefficient of variation of the tests was generally less than 15%. and for some cases the coefficient was slightly higher than 15%. However, for all cases the coefficient of variation was less than 20.6%. Figure 12 demonstrating the energy absorption capacity of NWC was higher than LWC at room temperature. After adding steel fiber, the energy absorption capacity of the LWC is enhanced, and the toughness improvement of LWC by adding HF was the most significant. With the temperature increase, the energy absorption capacity of all the concrete increased first and then decreased. All the concrete samples reached the correspondingly most massive energy absorption capacity at a temperature of 400 \({}^{\circ}\)C, except for HFAL. By comparing the data given in Figure 12, it can also be found that, after exposure temperature at 800 \({}^{\circ}\)C, the energy absorption capacity of the SLWC and NWC, respectively, decreased to 45.5% and 67.7% compared to that at room temperature. In contrast, the energy absorption capacity of ALWC was 40% higher than that at room temperature. The corresponding percentages for the SLWC reinforced by CF and HF were 71.4% and 83.3%, respectively, while those for the ALWC reinforced by CF and HF were 135.7% and 126.3%, respectively. The above phenomenon shows that the concrete residual energy absorption capacity under high temperatures was primarily related to the aggregate type.  
Specific toughness is defined as the ratio of toughness to the compressive strength of the same specimen. Comparing the data given in Figure 13, the coefficient of variation of  
Figure 11: Temperature effect on compressive peak strains: (**a**) absolute compressive peak strains; (**b**) relative compressive peak strains.  
the tests was between 0.8% and 20.7%. However, the coefficient of variation of the tests was mostly less than 15%. It can be found that the presence of steel fibers increased the specific toughness in the range of 25 to 800 \({}^{\circ}\)C. Furthermore, the specific toughness was enhanced as the temperature increased. It must be noted that the increase in the specific toughness of LWCs was greater than that of NWC. For example, the specific toughness of ALWC was 3.17 and 3.96 times at 600 and 800 \({}^{\circ}\)C of that at room temperature, SLWC was 3.08 and 3.34 times, and NWC was 2.42 and 3.06 times, respectively. This was mainly because the concrete strength degradation rate was greater than the toughness degradation rate in the evaluated temperature range.
# Intelligent Total Transportation Management System for Future Smart Cities
## 2 Materials and Methods  
### Systematic Description of the Urban Transportation System  
An urban transportation system is a sub-system of the overall transportation system that guarantees the safe, environmentally friendly, effective, and sustainable mobility and transportation of goods in urban/city areas. It is a sub-system, only, but it has fast interconnections with global transportation systems, including, e.g., the highway passing through/nearby the city, railways having a station in the city, and airports connecting to the cities.  
Urban transportation might be classified depending on the transportation means and on the observation and management applied.  
Figure 1 shows all the transportation means, such as road, rail, water, and air transports, which are operated by cities. The transportation means may include very different transportation vehicles/solutions. For example, rail transportation operates trans, underground, overhead, and cogwheel railways in urban areas; high-speed rail passing in/nearby cities; and trains and rail/magnetic rail connecting the airport with city centers. Nowadays, pedestrian transportation, small personal vehicles (bicycle, scooters, segway), standing vehicle transportation (parking), and new forms of transport such as urban air transportation (drones, air taxies) are the elements of the urban transportation system. Such elements must be integrated into the total transportation/total transportation management.  
In future, the introduction of autonomous vehicles in everyday operations and highly automated traffic management systems will mean operator roles will shift from active control to passive observation  
Figure 1: Urban total transportation system: I—industrial area (factories), II—forest area, III—urban area, IV—airport area, 1—underground, 2—road, 3—upper ground, 4—path, 5—railway, 6—highway, 7—freight transport, 8—urban air transport, 9—water transport.  
and require active control in case of special and emergency situations, only. Such management requires the collection of enough data to enable the management of the total transportation system in optimized form. The distributed sensor systems might be organized into a single smart transportation IoT-based system.
## 5 Evaluations
### Performance Evaluation
#### 5.2.2 Performance versus Traffic Type  
In this experiment, we compare the scheduled results, execution time, and the sum of end-to-end delays of the four techniques for different traffic types. In total, 100 test cases are handled with 20 flows in the MM network. The scheduled results and execution time results are shown in Figure 11, whereas a comparison of the sum of end-to-end delays is shown in Figure 12. The \(x\)-axis represents the flow group index and its below corresponds to the algorithm. The average periods and DDL constraints of flows in flow group 1 are larger than flows in flow group 2, while smaller than flows in flow group 3. The flow period correlation decreases sequentially. There is not much variability in the periods in flow group 1 and the existing algorithms can already solve most of the problems. However, for applications in flow group 2 or 3, these exiting techniques are no longer able to tackle them. Contrarily, the results demonstrate the well scalability of the MPFRS-FC and APCRS-FC. Figure 11 shows the superior success rate and faster execution time of the proposed technique over SPRF-EtoED and LBF-EtoED. We noticed that the M algorithm can solve the vast majority of scheduling problems and gives the result of whether or not scheduling is possible in a relatively short time in scenarios where network connectivity is high and flows are poorly correlated. Additionally, it does not have a significant performance loss in terms of end-to-end delay compared with APCRS-FC. The APCRS-FC algorithm takes time the exploration of the compensation factor, resulting in a large number of timeouts when the flow gap is relatively large. Since the terminated cases due to the time limit and infeasible cases do not contribute to the sum of end-to-end delays, SPRF-EtoED has superior overall end-to-end delay performance.  
Figure 10: Success rate and the sum of end-to-end delays of the SPRF-EtoED, LBF-EtoED, MPFRS-FC, and APCRS-FC for 100 synthetic test cases with different number of flows chosen from flow group 2, tested in MM network. (**a**) Technique 1: SPRF-EtoED. (**b**) Technique 2: LBF-EtoED. (**c**) Technique 3: MPFRS-FC. (**d**) Technique 4: APCRS-FC.
# Q-Learning Approach Applied to Network Security
## 6 Conclusions  
This paper demonstrated a noteworthy tool for the IRS and outlined the importance and necessity of creating novel, robust models and perspectives in the existing literature. We outlined the algorithmic structure of Q-learning from the perspective of a probabilistic model and proved its robustness. We simulated an MDP and created a decision agent whose decisions and actions depend on the environment's interaction. We provided an example of a reward function so the agent can find an optimal policy and the action that maximizes its calue and action-value functions. The sensitivity analysis provides understanding of the decision nature of the agent, and the goodness of fit estimation demonstrates the soundness and efficiency of the model. The main advantage of the Q-learning model over current algorithms used in IR systems is the existence of a decision entity that can operate independently in a completely unknown environment and provide an automated response to malicious activities. The agent does not need to know any previous information about the network, and it learns fast with minimal assumptions based on the Markov Decision Process. Our future work is related to making the model more realistic regarding the reward function; we need to create an updatable reward mechanism that depends on the cost function of the decision agent and other factors of the security environment that are changing over time. Including more agents who may work in a competitive or cooperative network setup will be very beneficial. This approach will provide more insights about the entity \"attacker\" and model its behavior sophisticatedly so a game theory aspect of the environment can be applied. We also need to create a grid search mechanism to tune the parameters more appropriately and to create advanced performance metrics, besides MSE, that will serve as an evaluation tool and be reported at any time in the IR systems. Many new deep Q-learning approaches would also benefit our decision agent, and they need to be explored further in our analysis. Our goal for this paper is to provide a complete outline of the probabilistic structure and application of the model to network security, and our future work will be devoted to researching the individual components and steps of the Q-learning algorithm in a deeper context so that the overall model can be further improved and efficiently serve society.  
Figure 9: Intrusion detection and intrusion response module.  
**Author Contributions:** Conceptualization and methodology, Z.U.; software and validation A.O.; formal analysis and investigation Z.U. and A.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Proposed Approach
### Algorithm  
FANA-CF algorithm is very straightforward, as is described in Figure 3. FANA-CF takes inputs from a target user (\(u_{t}\)) and a location (\(l_{d}\)) that the target user wishes to go to and returns the list (\(R_{fn}\)) of top-k recommended items.  
The algorithm starts by classifying the two groups of users, friends and natives. The system will then check on the hometown location (\(l_{i}\)) of every user (\(u_{i}\)). If the hometown location (\(l_{i}\)) of the user (\(u_{i}\)) equals the destination (\(l_{d}\)) the target user (\(u_{t}\)) wishes to go to, then the user (\(u_{i}\)) will be classified as a native. If user (\(u_{t}\)) is linked to the target user (\(u_{t}\)) through a social graph; then the user (\(u_{t}\)) will be classified as a friend.  
Next, the system will check the rating's location (\(\text{rating}_{f}\)) through friends. If the location of \(\text{rating}_{f}\) is equal to the location (\(l_{d}\)) that the target user (\(u_{t}\)) wishes to go, then \(\text{rating}_{f}\) will be assigned to the friends' rating (\(r_{f}\)). The system will then check on the location's rating (\(\text{rating}_{n}\)) by natives. If the location of \(\text{rating}_{n}\) is equal to the location (\(l_{d}\)) that the target user (\(u_{t}\)) wishes to go to, then \(\text{rating}_{n}\) will be assigned to the friends' rating (\(r_{n}\)).  
Friends' rating, \(r_{f}\), and natives' rating, \(r_{n}\), will use collaborative filtering to obtain friends' recommendations, \(R_{f}\), and natives' recommendations, \(R_{n}\) respectively. User supplied weights (\(\alpha\)) will then be applied on \(R_{f}\) and \(R_{n}\), in order to obtain a new recommendation rating through the combination of weights with recommendations of friends and recommendations of natives, \(R_{fn}\).  
Finally, the system will return top \(R_{fn}\).
# Improving Accuracy of Impervious Surface Extraction Based on a Threshold Hierarchical Method (THM)
## 3 Methods
### Lsma  
The LSMA method was adopted for unmixing pixels. Suitable results were obtained by [PERSON] and [PERSON] when frictions of impervious surface were estimated by analyzing low and high albedo endmembers [7]. Here, four endmembers, i.e., high-albedo objects, low-albedo objects, vegetation, and soil, were used for unmixing the multispectral images into these fractional images. Following [PERSON]'s model, impervious surfaces could be estimated by the addition of low-albedo and high-albedo fractions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Type** & **Sensor Type** & **Resolution** & **Longitude/Latitude** & **Acquire Time** \\ \hline Landsat 8 & OLI & 30 m & E 113.5/N 23.1 & 16 November 2014 \\ GF-2 & PMS1 & 4 m & E 113.5/N 23.1 & 13 January 2015 \\  & PAN & 1 m & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detail of remote sensing data.  
Figure 3: Distribution of test samples.  
The mathematical model of LSMM could be expressed as following:  
\[R_{i}=\sum\limits_{k=1}^{n}f_{k}R_{ik}+\varepsilon_{i} \tag{1}\]  
where \(i=1,\,\ldots\,\), (number of spectral bands); \(k=1,\,\ldots\,\), \(n\) (number of endmembers); \(R_{i}\) is the spectral reflectance of band \(i\) which contains one or more endmembers; \(f_{k}\) is the proportion of an endmember \(k\) to the pixel; \(R_{ik}\) was the known spectral reflectance of endmember \(k\) with the pixel on band \(i\); and \(\varepsilon_{i}\) was the error for band \(i\).  
A constrained least-squares solution was used here, assuming that the following two conditions were satisfied simultaneously:  
\[\begin{array}{l}\sum\limits_{k=1}^{n}f_{k}=1\\ 0\leq f_{k}\leq 1\end{array} \tag{2}\]
# Autonomous Balloon Controls for Protection against Projectiles with Known Destinations
## 5 MATLAB Simulation Results  
We verify the effectiveness of our multi-balloon systems using MATLAB simulations.  
Simulation settings are as follows. The sampling interval is \(dt=0.01\) s. We use \(\eta=2\) in Algorithm 1. Furthermore, we set \(Thres=1\) m. We say that the EP is _neutralized_ by a balloon as the distance between the EP and the balloon is shorter than 10 m. In the case where the EP is not neutralized by any balloon, it hits the ground in the end.  
The initial position of the EP is at the origin. The initial EP velocity \(V_{0}\) is 326 m/s. The destination of the EP is located at \((7000*c(20*\pi/180),7000*s(20*\pi/180),0)\).  
In simulations, the true EP's path uses its proportional factor as \(\kappa=10^{-7}\frac{s^{2}}{m^{2}}\). The EP's path is built with yaw angle \(\psi^{D}+n_{\psi}\), where \(n_{\psi}\) is a Gaussian noise with zero mean and variance \(var_{\psi}\). The EP's path is built with the initial velocity \(V_{0}+n_{V}\), where \(n_{V}\) is a Gaussian noise with zero mean and variance \(var_{V}\). The EP's path is built with the initial shooting angle \(\theta_{0}^{D}+n_{\theta}\), where \(n_{\theta}\) is a Gaussian noise with zero mean and variance \(var_{\theta}\).  
Figure 2 shows the case where \(N=500\) balloons are deployed in total. In this figure, we use \(var_{\psi}=0.5\) degree, \(var_{\theta}=0.5\) degree, and \(var_{V}=0.1\) m/s. The EP's path is depicted with a green curve. \(N=500\) balloons are marked with blue diamonds. See that balloons are deployed to block the EP's path. The destination of the EP is marked with a red circle. The EP is neutralized by a balloon, thus it cannot reach its destination (red circle).  
Next, we show the effect of varying the noise factors \(var_{\psi}\), \(var_{V}\), and \(var_{\theta}\). In Figure 3, we use \(var_{\psi}=0.1\) degree, \(var_{\theta}=0.1\) degree, and \(var_{V}=0.1\) m/s. Figure 3 shows the case where \(N=500\) balloons are deployed in total. The path of the EP is depicted with a green curve. \(N=500\) balloons are marked with blue diamonds. Compared to Figure 2, the dispersion of the balloons decreased because we used decreased noise factors.  
Figure 2: Simulation result using \(var_{\psi}=0.5\) degree, \(var_{\theta}=0.5\) degree, and \(var_{V}=0.1\) m/s.  
Next, Table 1 shows the effect of changing \(N\). While changing \(N\), one uses \(var_{\psi}=0.5\) degree, \(var_{\theta}=0.5\) degree, and \(var_{V}=0.1\) m/s. We run 20 Monte Carlo (MC) simulations and check how many times the EP is neutralized successfully. Let BlockNum denote how many times the EP is neutralized among 20 MC simulations. Table 1 shows that as the number of balloons increases, \(BlockNum\) increases.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 5 Impact of Water Vapor Variability on Meteorology and Climate
### Assimilation
One experiment was performed using the Regional Atmospheric Modeling System (RAMS) 3D variational data assimilation system to assimilate GNSS-ZTD from single-frequency receivers. It was concluded that, although results are preliminary and some issues must be addressed, the assimilation was very positive for both ZTD and IWV fields [130].
## 2 Materials and Methods
### Zero-Dimensional Resistance Model  
The image-based modeling method has been widely used to create geometrical vessel models for blood flow CFD analysis [17; 18; 19]. However, the peripheral vessels that are not depicted by medical images cannot be included in the models by this method, even though these vessels affect the simulation result. Therefore, in the previous study [14], a zero-dimensional (0D) resistance model was developed based on anatomical and physiological knowledge of human vessels to analyze blood flow in the carotid artery. It was demonstrated that physiologically accurate blood flow can be reproduced in the CFD analysis with the 0D resistance model [14]. The 0D resistance model returns the pressure (mmHg) at outlets in the 3D region according to the mass flow of blood and agent (kg/s) (Figure 5). In the 0D resistance model, the number of bifurcations in each five-vessel group, namely, large artery, the main artery, terminal artery, arteriole, and capillary, was calculated according to the area of outlet in the 3D region. According to the number of bifurcations, the terminal quantity of blood and agent was calculated. The terminal pressure at the capillary was fixed at 30 mmHg, which is assumed as the continuous pressure of the vein. Hence, the pressure at the outlet in the 3D region can be calculated backward.  
Figure 5: Conceptual diagram for the zero-dimensional resistance model.
## 2 Materials and Methods
### The Hydrological Modeling Chain: Flood-PROOFS  
Flood-PROOFS (Flood PRObabilistic Operative Forecasting System) is a system designed to support decision makers during the operational phases of flood forecasting, flood monitoring, and water resource management. Its main goal is to protect the population and infrastructure from damage caused by intense precipitation events. The system is operational for the Italian National Civil Protection Department and other hydro-meteorological offices in different countries (e.g., Bolivia, Caribbean, plus ongoing implementation in the Greater Horn of Africa and Mozambique). The Flood-PROOFS system manages the data flow deriving from various modeling tools developed by the CIMA Research Foundation to return a quantitative assessment of the effects that severe weather conditions can have in terms of river flow and probability to overcome critical thresholds.  
Flood-PROOFS' core is the distributed hydrological model Continuum [23; 24]. The Continuum solves both mass and energy balance equations on a regular grid, using hydro-derivatives (flow direction, flow accumulation, and slope) extracted from a Digital Elevation Model (DEM), as well as land cover and soil texture information. Within Flood-PROOFS, the Continuum is forced by meteorological observations from Automatic Weather Stations (AWS), meteorological radar, satellite, and meteorological models' forecasts. A description of the forecasting chain and its application on the whole of Italy can be found in [25]. The Continuum is both an operational and research model; some studies that involve Continuum are: [26; 27; 28; 29; 30; 31]. Flood-PROOFS and the Continuum are open-source tools available at [[https://github.com/c-hydro](https://github.com/c-hydro)]([https://github.com/c-hydro](https://github.com/c-hydro)) (accessed on 11 December 2022).