esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Lane Control Strategy Based on Truck Platooning Simulation
### Simulation Platform Construction
#### 3.1.1 Simulation Platform Construction  
In this study, the driving behavior parameters of small cars and manually operated trucks were adjusted in the VISSIM simulation platform. A comprehensive evaluation function of model parameters related to traffic flow and speed was established to minimize the error between simulation outputs and actual surveyed figures. The corresponding values were also taken as driving behavior correction data. Subsequently, time-varying diagrams of traffic flow and speed distribution of the cross section obtained from the simulation were drawn. Corresponding parameters, such as flow rate and speed, were also set appropriately.  
Traffic flow in VISSIM is measured in units of veh/h. The following seven traffic flows were employed: 1000, 2000, 3000, 4000, 5000, 6000, and 7000. CAR denotes ordinary cars, HDT represents manually operated trucks, and ATP (automated truck platform) refers to platooning trucks.  
Based on actual road traffic and truck platooning information obtained in prior studies, the speed distributions of the three aforementioned vehicle types are listed in Table 1.
## 3 Results  
Locations on GPR transects where potential buried drainage pipes were detected are shown (with red dots) in Figure 7. At Site 1, to the west of the white dashed line in Figure 7a, the overall soil profile had a relatively homogeneous appearance regarding GPR response (as exhibited in GPR profiles). Conversely, to the east of the dashed line, the overall soil profile had a far more heterogeneous appearance regarding GPR response. The overall GPR appearance of the soil profile (i.e., homogeneous vs. heterogeneous) definitely impacted Site 1 drainage pipe detection. In the more homogeneous soils on the west side of Site 1, reflection hyperbola potential drainage pipe responses were fairly easy to isolate within GPR profiles (Figure 8a), while in the more heterogeneous soils on the east side of Site 1, isolating the reflection hyperbola potential drainage pipe responses from the surrounding clutter was much more difficult (Figure 8b).  
Figure 7: GPR measurement transects (yellow lines) and potential drainage pipe locations (red dots): (**a**) Site 1; (**b**) Site 2; (**c**) Site 3.  
Drain lines were interpreted to exist in places where mapped locations of potential drainage pipe responses clearly followed a line. Again, a potential drainage pipe location that is isolated likely implies that the corresponding GPR profile reflection hyperbola response is not due to a drainage pipe, but rather some solitary buried object, such as a large stone. The interpreted drain line patterns (blue lines) are depicted in Figure 9 for all three sites. The GPR-RTK/GNSS data collected in this study delineated a complex rectangular drainage pipe system at Site 1 (Figure 9a), with a set of southwest-northeast drain lines present on the west side of the site and a southeast-northwest drain line set present on the east side of the site. Figure 9b indicates that although not all the drainage pipe present at Site 2 was detected, GPR integrated with RTK/GNSS did find a sufficient number of drain lines to unambiguously determine that a herringbone subsurface drainage system had been installed within this agricultural field. Random drain lines were found at Site 3 (Figure 9c), which are likely to be very old and installed for the purpose of draining wet areas in the hay field.  
Figure 8: GPR profiles showing reflection hyperbola potential drainage pipe responses highlighted by yellow line ovals; **(a)** homogeneous soils on west side of Site 1; **(b)** heterogeneous soils on east side of Site 1. The reflection hyperbola potential drainage pipe responses are far easier to discern in the homogeneous soils as opposed to the heterogeneous soils.
### State of the Art: Control  
The control of non-prehensile macroscopic planar manipulation systems using rotors has been addressed in various studies [1; 10; 11; 15; 16; 17; 18], yet it remains an open research topic and is highly dependent on the specific characteristics of each system [16]. Existing studies differ in their approach, ranging from the modeling of the object's motion to the specific control strategies employed. Among the pioneering works, in [15], the friction between the rotors and the transported materials is described using a viscous slip contact model, enabling the formulation of a comprehensive dynamic model of the object's motion in relation to the rotor velocities. This approach has proven to be effective when implemented with feedback control. In [10], a kinematic model relating the rotor velocities to the object's motion was derived using the power dissipation method (PDM) to describe object movement. Similar to the previous method, it ensures stability only when feedback is applied; however, it is limited to quasi-static scenarios. This constraint necessitates its combination with elliptical velocity fields, which adjust the object's trajectory, while the PDM governs its orientation. A similar combination of an elliptic field with feedback for residual errors was also proposed in [18], in which the control law is designed based on the object's kinematics under the no-slip contact hypothesis. Another interesting strategy is presented in [17], where surface manipulation was investigated using a fuzzy logic approach to assign rotor velocities. Lastly, the most commonly used control methods employ PID or PD controllers [1; 11], which are applied to rotor velocities and, in some cases, to rotor angles [1], assuming direct kinematic relationships between the object's motion and these parameters.  
When applying these strategies to the under-actuated surface in question, a dynamic modeling approach is preferred. In particular, the PDM is ruled out, as the system does not operate under quasi-static conditions, and inverse kinematics cannot be defined. Likewise, other methods are not directly applicable due to the lack of control over rotor velocity. Therefore, tailored methodologies adapted to the system's design are proposed and analyzed in this paper, focusing on the determination of the rotor axis orientation angle (\(\ell_{r}\)) within the surface plane, which is the only available control variable.
## 5 Case Study
### Model Selection  
As seen in Section 2.1, many models can be applied to forecast HVAC system temperatures, each with its pros and cons. In this study, data-driven models were chosen due to the quantity and quality of the data collected. To model the thermal systems, SVR was chosen for its robust performance in handling high-dimensional and non-linear data, which are characteristic of thermal systems. SVR employs a kernel to effectively map input features into higher-dimensional spaces where linear separation is feasible, thus capturing the complex, non-linear relationships of thermal dynamics. Additionally, SVR's ability to minimize the generalization error ensures that the model remains accurate and reliable even with noisy data, which is common in real-world thermal measurements. Its intrinsic regularization mechanism prevents overfitting, making it suitable for predicting thermal responses under varying conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Parameter** \\ \hline Features & Temperature at time \(t-1\) \\  & Temperature at time \(t-2\) \\  & Tendency at time \(t\) \\  & Outdoor temperature at time \(t\) \\  & Gaussian number of people at time \(t\) \\  & Temperature at time \(t\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Variables used to train the laboratory’s model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Parameter** \\ \hline Features & Temperature at time \(t-1\) \\  & Temperature at time \(t-2\) \\  & Tendency at time \(t\) \\  & Temperature at time \(t\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Variables used to train the tank’s model.  
The methodology is not restricted to SVR or a specific type of model. Instead, it is flexible and can accommodate any data-driven model that suits the characteristics of the data and the system being studied. This adaptability allows researchers to select models that align best with their data's structure and the system's requirements, fostering the exploration of diverse techniques for improved predictive performance.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 5 Blockage in Industrial Deployments
### UE to UE Communications  
We start with the simple case of UE to UE communications, where the height of the UEs, \(h_{U}\), is always lower than the machine height, \(H_{B}\). In these conditions, the LoS path might be occluded by any machine it intersects with.  
Consider projection of the LoS path to the \(x\)- and \(y\)-axes. Observe that number of cells it crosses equals the sum of the number of lines over both axes contained within LoS path projection. Thus, we can consider the occlusion of the LoS path independently over the \(x\)- and \(y\)-axes. Further, observe that in D2D case \(h_{U}<H_{B}\), the occlusion of the LoS path is independent of the height of machines and LoS height. Since the cell contains a machine with probability \(\
u\) while each machine occludes LoS path with probability \((1-\kappa)\), where \(\kappa\) is the transparency of a machine estimated in Section 4, the task is thus to determine the intensity of machines over the \(x\)- and \(y\)-axes occluding the LoS path.  
The abovementioned task can be efficiently solved by utilizing thinning of the Poisson process over individual axes [29]. Indeed, the actual intensity of lines over \(x\)-axis resulting in LoS path occlusion is simply given by thinning the initial intensity with a factor of \(\
u(1-\kappa)\). Thus, The blockage probability induced by lines over \(x\)- and \(y\)-axes are given by void probabilities of the Poisson processes  
\[p_{B,x}=e^{-\lambda_{x}(1-\kappa)I_{x}\
u},\;p_{B,y}=e^{-\lambda_{y}(1-\kappa )I_{y}\
u}. \tag{1}\]  
By recalling that line processes over \(x\)- and \(y\)-axes are independent of each other, the resulting LoS blockage probability takes the following form  
\[p_{B}(l_{x},l_{y})=e^{-\lambda_{x}\kappa I_{x}\
u}\times e^{-\lambda_{x}\kappa I _{x}\
u}=e^{-\kappa\
u(\lambda_{x}I_{x}+\lambda_{y}l_{y})}. \tag{2}\]  
Figure 4: The side view of the BS-UE communications scenario.
## 4 Results and Discussion
### Comparison of Natural Gas and Hydrogen
#### 4.2.6 Thermal Capacity of Surrounding Rock  
We set the specific heat capacities of the salt rock to 920 J/kg.\({}^{\circ}\)C, 820 J/kg.\({}^{\circ}\)C, and 720 J/kg.\({}^{\circ}\)C, respectively. The temperature variations around the cavity under these three different specific heat capacities are illustrated in Figure 12. For both hydrogen and natural gas, as the specific heat capacity of the surrounding rock decreases, the peak and trough temperatures around the cavity progressively rise. Under the same specific heat capacity of the surrounding rock, the temperature is consistently higher when storing hydrogen compared to storing natural gas.  
Figure 13 illustrates the temperature distribution maps of surrounding rock with different specific heat capacities after three years of injection and withdrawal cycles. When storing the same type of gas, variations in specific heat capacity also alter the extent of thermal effects; a lower specific heat capacity results in a deeper penetration of thermal effects into the rock. The reduction in specific heat capacity has a similar effect to an increase in thermal conductivity, as both influence temperature by affecting heat transfer. Generally, the thermal effect of hydrogen is higher than that of natural gas. The reason is that, under conditions of equal specific heat capacity, the thermal effects generated by the injection  
Figure 11: Temperature distribution of surrounding rock with different thermal conductivities.  
Figure 12: Temperature variation around the cavity with different thermal capacities.  
and extraction of hydrogen are significantly greater, resulting in a higher surrounding rock temperature during hydrogen storage compared to that of natural gas.
# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 3 Results and Discussion
### Electronic Nose Compensation
#### 3.3.2 Artificial Neural Network Compensation  
In this experiment, the artificial neural network was used to compensate the electronic nose, and the compensation results are as follows (Table 4). All the tea classification results in the temperature and humidity compensation ranges are improved, with the highest improvement of 16%, which can improve the classification ability of the electronic nose to a certain extent. However, in order to further improve the classification performance of the electronic nose and the influence of the drift factor, we propose a secondary feature optimization processing to further reduce the drift influence.
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 3 Materials and Testing
### Factors Influencing Permanent Deformation  
An extensive experimental program was undertaken to comprehensively evaluate the factors influencing permanent deformation in asphalt mixtures. A total of 306 cylindrical specimens were prepared and tested under repeated uniaxial compression loading. Three replicates were produced for each test sample, and the average results were calculated. The study aimed to analyze the impact of various parameters on the permanent strain behavior of asphalt mixtures. These parameters were categorized into 12 predictor variables, representing environmental conditions, traffic loading, and mixture properties. The predictor variables included testing temperature, loading conditions (traffic repetitions, stress level, and load duration), mixture properties (asphal viscosity and content, aggregate gradation using aggregate percent passing \(\
icefrac{{1}}{{2}}\)\" (PP1/2) and 4.75 mm sieves (PPNo.4)), and mix volumetric properties. The experimental phase resulted in the collection of 3214 data points. The experimental design incorporated a comprehensive range of factors and levels to capture the variability in asphalt mixture performance under different conditions. Table 4 summarizes the predictor variables and their respective levels.  
Figure 2: Marshall properties for surface course mixes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Levels** & **Description** \\ \hline Asphal content & 3 & OAC and OAC \(\pm\)0.6\% \\ Asphal viscosity & 3 & 40/50, 60/70 and 85/100 \\ Aggregate gradation & 5 & WCG 1, WCG 2, WCG 3, BiCG and BaCG \\ Temperature & 7 & 20, 25, 40, 55, 60, 65 and 70 \({}^{\circ}\)C \\ Stress level & 6 & 44.82 (6.5 psi), 68.95 (10 psi), 89.63 (13 psi), 137.89 (20 psi), \\  & & 186.15 (27 psi), and 206.84 kPa (30 psi) \\ Volumetric properties & Variables & AV, VMA, Gmm, GmbH, Gsb \\ \hline \hline \end{tabular}
\end{table}
Table 4: Predictors variables and their levels.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 4 Testing and In-Orbit Results
### _Firmware Integrity Tests_  
The three integrity checks during boot shown in Figure 4 are independently verified.  
The triple redundancy of the SSBL image file is verified by sequentially corrupting each of the three SSBL images by erasing 10-byte chunks of the file on the QSPI Flash and attempting to boot. The results are as follows:  
* Test 1: No data corrupted: System boots correctly.
* Test 2: First copy of SSBL corrupted: System boots correctly.
* Test 3: First and second copy of SSBL corrupted: System boots correctly.
* Test 4: All three SSBL corrupted: System fails to boot.  
The tests show that the checksum mechanism is able to detect the corrupted bootloader and proceed to load the backup until all are corrupted.  
The test to verify automatic fallback to the golden image is done by simulating bitflip in the primary image file and by ejecting the SD card to simulate a hardware issue. In addition, boot counter overflow is simulated by scripting five power cycles of the payload with a period shorter than the boot time. On the sixth power-on event, the OPU is left to boot fully. The results are as follows:  
* Test 1: No data corrupted: Primary image boots correctly
* Test 2: SD card not present: Golden image boots correctly
* Test 3: Primary image is corrupted: Golden image boots correctly
* Test 4: Five power cycles: Golden image boots correctly  
For development and debugging, the boot image is configured to include Dynamic Host Configuration Protocol (DHCP) and an Secure Shell (SSH) server. For deployment, these are disabled. With them, booting takes 37 s, and the application software is not  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SD Card** & **eMMC** \\ \hline Boot image & Boot image (golden) \\ FPGA image & FPGA image (golden) \\ _HSI capture data_ & \\ _log and telemetry files_ & \\ _updated main application files_ & \\ _updated FPGA modules_ & \\ _updated pipeline application files_ & \\ _pipeline configuration files_ & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of the most important files and kinds of files stored, and intended to be stored on permanent storage available on the OPU. _Italics_ means files uploaded or generated post-launch.  
started automatically. Instead, a serial terminal is available for login and command line shell access. Booting a deployment image takes 19 s due to the omission of DHCP discovery and generation of the Rivest-Shamir-Adleman (RSA) key pair for SSH.
## 1 Introduction
In order to minimize the rms (root mean square) phase current and the maximum absolute value of the rate-of-change of the flux linkage (MAV-RCFL), the turn-on and the overlap angle were extracted based on a genetic algorithm optimization method to select the optimal TSF. In [20], for reducing the losses of the switched reluctance motors, a piecewise cubic TSF was optimized with six degrees of freedom. In [21], an offline TSF with a wide torque-speed range for reducing torque ripple in an SRM was studied; the objective function adopted a Tikhonov factor among two objectives to minimize the maximum absolute value of RCFL and the copper loss. In [22], an extended-speed low-ripple torque control in two modes using the TSF in an SRM was introduced. Because of the imperfect tracking of the phase current in high motor speed, a proportional-integral controller with a torque-compensation was added to the torque reference during the commutation range of two adjacent phases. In [23], a novel and simple nonlinear logical TSF was proposed to reduce the torque ripple and improve the efficiency. The logical TSF was used online and was not fixed compared with the other TSFs. The incoming phase current was controlled in a ladder-like increase, and the current of the outgoing phase was tracked in the opposite direction. Therefore, torque sharing between the incoming and outgoing phases was achieved with a minimum current crossover. However, a theoretical analysis of the RCFL was not provided for this study. In this paper, taking into account the requirements for the maximization of the torque-speed range and the minimization of copper loss and balancing these secondary objectives by including a weighing factor, a family of TSFs deduced by an invertible torque function is proposed. Because the proposed TSF provides phase currents throughout the whole region, the torque-speed capability is improved as well. The solution of the TSFs selection becomes a multi-objective optimization problem. And then, the evaluation indices such as the peak phase current, the rms phase current, and the torque ripple factor are compared between the proposed and the classical TSFs by using a simulation. Finally, the simulation and experimental results show that the proposed control algorithm has advanced performance in reducing the copper loss and improving torque-speed performance.
# Data Anonymization: An Experimental Evaluation Using Open-Source Tools
## 1 Introduction
Sensitive information is the information that an individual does not want to turn public, such as information about a disease, Social Security Number, salary, and other things [4]. In this work, it is possible to confirm that data anonymization is an important technique to protect personal data and to prevent attackers from having access to them. We assess open-source anonymization tools which help users to anonymize their information. ARX Data Anonymization and Amnesia are the most popular tools in data anonymization. In this paper, the authors evaluated these tools using the OSSpal methodology and with a public dataset. The OSSpal methodology has the purpose of evaluating open-source tools to help users and organizations to find the best solutions using a set of categories. In the context of this work, the categories used are functionality, operational software characteristics, support and services, documentation, software technology attributes, community and adaption, and development process. Using the OSSpal methodology, it is possible to verify that ARX Data Anonymization has a higher score than Amnesia. Each one of the tools was experimentally evaluated using a public real dataset to perform the anonymization. The dataset has the most recent tweets about the Pfizer and BioNTech vaccine. After anonymizing this dataset with the referred tools, it is possible to conclude that ARX Data Anonymization is the best tool to use. Amnesia simplifies the anonymization process, but the tool has some errors, and the results are confusing because some solutions are not completed. ARX Data Anonymization does not show to the user all the steps that need to be performed to anonymize a dataset, but it does not have inaccuracies and the solutions at the end are simpler to visualize. The main contributions of this work are the following:  
* Data anonymization tool assessment using OSSpal methodology;
* Data anonymization tool experimental evaluation using one public dataset;
* Best tool to use according to dataset characteristics;
* Main weaknesses and difficulties in the practical use of each tool. The remaining sections of this paper are structured as follows. Section 2 presents the related work. Section 3 describes the anonymization tools. Section 4 presents the OSSpal methodology. Section 5 presents the tool assessment using the OSSpal methodology, and Section 6 describes the assessment using a real dataset. Section 7 discusses the results of the experiments and suggests some recommendations. Finally, Section 8 concludes the paper and presents future work.
# Research on a Simplified Estimation Method for Wheel Rolling Resistance on Unpaved Runways
###### Abstract  
Aiming at the practical difficulties (e.g., high cost of full-scale tests) in testing the rolling resistance of aircraft wheels on unpaved runways, this study establishes a theoretical calculation formula for wheel rolling resistance based on the Bekker model, following an analysis of the development and application of wheel-soil interaction models. Global sensitivity analysis using the Sobol' method was performed on the theoretical formula to derive a simplified calculation model. Aircraft load simulation tests under 80 kN, 100 kN, and 120 kN loading conditions were conducted using a specialized loading vehicle to determine parameters for the simplified prediction model. The resistance values obtained from this model were then applied to calculate aircraft takeoff roll distance. The accuracy of resistance estimation was verified by comparing the calculated results with takeoff distances reported in relevant literature. This research provides a novel approach for estimating wheel rolling resistance of transport aircraft on unpaved runways and offers valuable reference for determining the required length of unpaved runways.  
Keywords:unpaved runway; global sensitivity analysis; wheel-soil interaction model; takeoff roll distance calculation; wheel rolling resistance prediction +
Footnote †: journal: Applied Sciences