esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Conceptual Background  
### Characteristics of the Automotive Sector  
In the past, automobiles were merely a basic means of transportation with machinery as the primary component. With recent advancements in ICT technology, sophisticated technologies such as electronic devices, electronic control devices, and ITS have emerged, providing car users with safer and more convenient features [24]. As a result, automobile manufacturers are gradually transitioning from individual module-based control to an integrated control approach at the component level, and new product development is also increasingly focused on developing electronic components for safety and convenience purposes, rather than solely on design-oriented development [25].  
As the use of electronic devices continues to expand, the software complexity and integrated control modules for overall vehicle control are also increasing [26]. Furthermore, as the number of lines of code (LOC) in software increases with module coupling and cohesion, complexity also increases, leading to more vehicle safety accidents and recalls due to software defects [27]. Vehicle software can be categorized based on function and has been updated and mass-produced individually to meet the needs of each field without taking into account the vehicle architecture [28; 29].  
To summarize, automakers have been developing electronic systems and application services based on firmware for each function without thoroughly considering the overall vehicle architecture (Table 1). This structure, which closely and intricately combines hardware and software, has been developed and updated only for specific functions and specific hardware, making it difficult to reuse software. This slows down the development cycle of automotive electronic parts and acts as a factor that lowers the level of integrated control.  
Recently, the need for automotive companies to develop a comprehensive architecture has been further increased by the interlocking of vehicle convenience devices with mobile devices and IoT devices, as well as the expansion of user convenience through connections with vehicle peripheral facilities, transportation infrastructure, OTA, and so on. To improve the efficiency of automotive electronic embedded software development, automakers first define and apply a standardized structure based on a protocol called AUTOSAR (AUTomotive Open System ARchitecture) [30]. This standardized software enables collaboration and division of labor among different companies for each module, shortening the development schedule, and modularizing frequently used functions for horizontal deployment to other vehicle models [31]. However, at this time, legal issues regarding liability arise in case of quality problems if additional contracts are not made with parts companies according to the horizontal development of each vehicle type.
## 5 Numerical Simulation of Interaction of Tunnel Lining with Rock Mass under Seismic Loading
The location and numbering of the characteristic points of the tunnel lining are shown in Figure 12. The curves of changes of the highest and lowest principal stresses at the characteristic points of the tunnel lining made of non-reinforced monolithic concrete are shown in Figure 14.  
The analysis of changes of the stress state of the tunnel lining at characteristic points over time allows confirming the assumption about the formation of tensile stress in zones of damage appearing before damage accumulation. The compressive stress is not high enough to cause the material damage. The curves' damage accumulation at the characteristic points of the tunnel lining over time are shown in Figure 15.  
Figure 14: Curves of the maximum and minimum principal stresses in the tunnel lining made of non-reinforced concrete under seismic load for the points of the inner contour (**a**) and the outer contour (**b**). The localization of damage zones and the sequence of their formation over time were stated as a result of modeling. First of all, the zones of greatest damage with length about 3.5 m are formed in the lining along the tunnel contour. The zones are located symmetrically relative to the tunnel axis at the angle about 45\({}^{\circ}\) counter clockwise to the vertical. Second, the formation of damage zones with a length of about 2.5 m occurs along the tunnel contour. The zones are located symmetrically relative to the tunnel axis at the angle about 50\({}^{\circ}\) clockwise to the vertical. Thus, the lining of non-reinforced monolithic concrete as a result of the calculated seismic impact from an earthquake is not destroyed, but the accumulation of structural defects occurs in the form of separation cracks, which locally weaken the rigidity and load-bearing capacity of the lining by 8-30% relative to the initial indicators. Most likely, repair work to eliminate the separation cracks formed in the material [33; 34; 35; 36; 37] would be required in the tunnel lining in such conditions in practice. Calculations of tunnel lining made of fiber-reinforced concrete with polymer macrofiber amount of 7 kg/m\({}^{3}\) show that the presence of a sufficient amount of fiber in the concrete composition allows avoiding damage in the tunnel lining under dynamic loading. The fiber amount of less than 7 kg/m\({}^{3}\), namely 3 and 5 kg/m\({}^{3}\), reduces the level of damage to the lining structure after seismic impact. This can be explained by the fact that the presence of fiber increases the total amount of energy that must be spent on concrete destruction in comparison with non-reinforced concrete of the same class of compressive strength. This can significantly increase the seismic resistance of the tunnel lining. In practice, repair work in the tunnel after a seismic impact not exceeding the calculated magnitude would not be required if the lining made of fiber-reinforced concrete with the fiber amount of 7 kg/m\({}^{3}\) was used, since separation cracks in the structure of the lining material are not formed.
## 3 Proposed OCC IoMT Platform (Design and Implementation)
### OCC-IoMT Platform  
The OCC-IoMT platform facilitates the transmission of health data from IoMT devices through optical signals to a camera, where the data are then analyzed and stored in the cloud, easing access and data management. Patients can register and create a \"channel\" that allows them to organize data, perform analysis, and provide intuitive data visualization, thus supporting real-time monitoring of patient health conditions. Based on the application architecture of the OCC-IoMT platform, as depicted in Figure 5, the transmitter part, the MAX30102 sensor, collects vital patient data such as oxygen saturation, heart rate, and body temperature. as well as the panic button value. These data are processed and converted into signals suitable for transmission through an LED matrix. This process uses OOK modulation, in which the data is encoded into light patterns that can be transmitted. On the receiver side, the edge server acts as a point of collection and data processing. This server receives the light signals captured through a camera or optical sensor and converts these signals back into digital data. Subsequently, the processed data are sent to the backend system via a REST API built using CodeIgniter. This API manages data interaction between the edge server and the main database. The data are then stored in a MariaDB database. On the client side, a web application enables patients to access data through a browser. The web server, running on Nginx and Apache, handles requests and responses between patients and the server. Codelgniter is also used to manage data interaction between the web server and the database.
## 4 Fault-Tolerance Control Strategy
### Differential Drive Control
#### 4.3.3 Drive Force Divider  
The drive force distributor is a yaw torque controller solution that distributes the drive motor torque to each drive wheel in a certain way. In this section, the yaw moment rules based on the motor characteristics are assigned. The additional yaw moment is indirectly converted into driving force by increasing the driving force of the drive motor on one side and decreasing the drive motor on the other side of the drive.  
In the absence of steering motor failure, the torque of the car drive is distributed equally among the four wheels in the unmanned fully line-controlled distributed drive electric vehicle, and the total drive force is shown in Equation (26):  
\[T=\left(F_{fl}+F_{fr}+F_{rl}+F_{rr}\right)\times r \tag{26}\]  
In Equation (26), \(T\) stands for the overall driving moment; \(F_{fl}\), \(F_{fr}\), \(F_{rl}\) and \(F_{rr}\) are the vertical forces acting on the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; and \(r\) is the tire's rolling radius.  
Equation (27) depicts the extra yaw moments:  
\[M_{z}=\left(F_{fl}-F_{fr}+F_{rl}-F_{rr}\right)\times\frac{B}{2} \tag{27}\]  
\(M_{z}\) stands for the extra yaw moment in Equation (27), and \(B\) stands for the wheelbase.  
The peak torque of the car's drive motor is also bound to be limited by Equation (28) because the pavement's adhesion coefficient affects the longitudinal drive of the automobile:  
\[-\mu F_{z}r\leq T\leq\min(\mu F_{z}r,\ T_{\max}) \tag{28}\]  
where \(T_{\max}\) is the peak moment of the drive motor.  
When an additional yaw moment is imposed, the vehicle has two main states of steering motion, one is left turn understeer and the other is right turn oversteer. At this time, in order for the vehicle to be able to drive stably, the motion of the vehicle needs to be corrected by appropriately increasing the right-hand wheel drive torque and reducing the left-hand wheel drive torque. At this time, the torque distribution of each wheel is as Equations (29) and (30):  
\[T_{fl,rl} =\frac{T}{4}-0.25|M_{z}|/\frac{B}{2}\times r \tag{29}\] \[T_{fr,rr} =\frac{T}{4}+0.25|M_{z}|/\frac{B}{2}\times r \tag{30}\]  
When the additional transverse moment \(M_{z}<0\), the vehicle mainly has two steering motion states, one is right turn understeer and the other is left turn oversteer. At this time, in order for the vehicle to be able to drive stably, the motion status of the vehicle needs to be corrected by appropriately increasing the drive moment for the left wheel and reducing the drive moment for the right wheel. The torque distribution of each wheel is as follows:  
\[T_{fl,rl} =\frac{T}{4}+0.25|M_{z}|/\frac{B}{2}\times r \tag{31}\] \[T_{fr,rr} =\frac{T}{4}-0.25|M_{z}|/\frac{B}{2}\times r \tag{32}\]
## 5 Experimental Results  
In this section, we evaluate the performance of our proposed feature selection optimization technique (AHA-AO) and compare it with five of the most effective feature selection techniques, namely, PSO, MFO, WOA, AO, and AHA. The parameter settings of each one of these methods are given in Table 1. In addition, each of these methods was run 25 times in order to have fair comparison, since they depend on random parameters.  
To verify the superiority of the performance of the AHA-AO in comparison with that of the other algorithms, we utilized four image datasets, namely, the ISIC-2016, PH2, Chest-XRay, and Blood-Cell datasets. A description of each dataset is given in Table 2, and examples are given in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameter Settings** \\ \hline PSO & \(VMax=6\), \(WMax=0.9\), \(WMin=0.2\) \\ \hline MFO \(a=2,b=1\), \\ \hline WOA & \(a=2\,to\,0\), \(a2=-1\) to \(-2\) \\ \hline AO & \(\alpha=0.1\), \(\delta=0.1\), \(\omega=0.005\) \\ \hline AHA & \(r\in[0,1]\) \\ \hline AHA-AO & \(\alpha=0.1\), \(\delta=0.1\), \(\omega=0.005\), \(r\in[0,1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings of the methods.  
Raw images were used to train the CNN models and the extracted deep features were used by the FS optimizer to select the best features from these deep features. The number of deep features from the Chest-XRay and ISIC-2016 datasets was 128 features, and 512 deep features from the PH2 and Blood-cell datasets were utilized. Optimized feature sets were used to train the three classification algorithms of DT, LDA, and SVM. For a fair comparison, we used the default settings for the three classifiers in all experiments. In total, we conducted five experiments with different datasets and different algorithms to test the proposed algorithm. Some datasets were used to build binary classifiers, such as ISIC-2016 and Chest-XRay; the PH2 dataset was used to build a three-class classifier, and Blood-Cell was used to build a four-class classifier.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset Name** & **Class** & **Training Data** & **Test Data** & **Total Images** \\ \hline _ISIC-2016_ & Malignant & 173 & 75 & 248 \\  & Benign & 727 & 304 & 1031 \\ \hline _Ph2_ & Common Nevus & 68 & 12 & 80 \\  & Atypical Nevus & 68 & 12 & 80 \\  & Melanoma & 34 & 6 & 40 \\ \hline _Chest-XRay_ & Normal & 1349 & 234 & 1583 \\  & Pneumonia & 3883 & 390 & 4273 \\ \hline _Blood-Cell_ & Neutrophil & 2499 & 624 & 3123 \\  & Monocyte & 2478 & 620 & 3098 \\  & Lymphocyte & 2483 & 620 & 3103 \\  & Eosinophil & 2497 & 623 & 3120 \\ \hline \end{tabular}
\end{table}
Table 2: Description of the datasets.  
Figure 5: Samples of medical images for the classification task from the four selected databases.
## 4 The Improved Self-Collision Detection Technology
### Determine the Position of Each Approximation Object
#### 4.2.1 Determine the Position of the Motor and the Carrying Load  
For the motor and the carrying load of the manipulator subsystem, we can directly describe the positioning of each motor by the corresponding joint-fixed coordinate frame, and we can also confirm the radius of the spherical approximation based on the basic parameters of the motor. Therefore, the corresponding spherical myopic object space is defined as  
Figure 5: Multi-element approximation of the component. (**a**) The approximation of the component in the manipulator subsystem; (**b**) The approximation of the mobile platform.  
\[\begin{cases}p_{xi}^{l}=C_{x}^{l}(i)+R_{s}^{l}(i)\cdot cos(m^{l});\\ p_{yi}^{l}=C_{y}^{l}(i)+R_{s}^{l}(i)\cdot sin(m^{l})\cdot cos(n^{l});\\ p_{zi}^{l}=C_{z}^{l}(i)+R_{s}^{l}(i)\cdot sin(m^{l})\cdot sin(n^{l});\end{cases} \tag{11}\]  
where \(p_{xi^{l}}^{l},p_{yi}^{l},p_{zi}^{l}\) represent the positioning result of the corresponding approximation of the \(i\)-th motor; \(R_{s}^{l}(i)\) represents the radius of the corresponding approximation of the \(i\)-th motor; \(C_{x}^{l}(i),C_{y}^{l}(i),C_{z}^{l}(i)\) represent the position of the center of the corresponding approximation; and \(m^{l},n^{l}\) represent the minimum divided distance. The value range of \(m\) and \(n\) is \([0,2\pi],[0,\pi]\).
## 1 Introduction and Related Works
### Transportation Mode Detection  
Transportation mode detection is crucial for categorizing raw data collected from WLT-based methods into desired modes for specific ITS applications. [PERSON] and [PERSON] [PERSON] and [PERSON] (2015) developed a fuzzy logic-based mode identification methodology for raw data collected from personal travel-survey GPS data loggers. The conventional GPS data loggers were carried by study participants for multiple days as they traveled in various modes of transportation. Then the collected data were post-processed in an attempt to estimate their travelled modes retroactively. This after-the-fact approach is suitable for transportation planning purposes rather than real-time applications, including traffic monitoring. [PERSON] et al. [PERSON] et al. (2016) also used a similar GPS dataset for identifying travel modes for transportation planning purposes. [PERSON] and [PERSON] [PERSON] and [PERSON] (2017) proposed a probabilistic method that predicted trip paths taken and their associated modes by using raw data from smartphone sensors including GPS and Bluetooth sensors. The method adopts a post-processing of data and is useful for ITS applications with longer time horizons. [PERSON] et al. [PERSON] et al. (2018) and [PERSON] et al. [PERSON] et al. (2019) developed real-time mode identification methods with a conventional GPS receiver with ANNs. Their methods used both directly-measured and indirectly-processed data. Directly-measured GPS data are location and speed values, while the indirectly-processed data are acceleration values that are estimated by observing differential speed values with known sampling intervals. The studies find that an ANN-based mode detection approach is effective with reasonable accuracies. [PERSON] et al. [PERSON] et al. (2019) enhanced the ANN-based mode detection by utilizing directly-measured acceleration values from smartphones and found that accelerometer values from smartphones did improve the mode detection performances. Recently, there are more specialized applications of mode detection. [PERSON] et al. [PERSON] et al. (2019) attempted to estimate travel modes from social media in hopes to utilize social network services as additional input data for mode detection. [PERSON] et al. [PERSON] et al. (2019) focused on detecting the transportation mode for elderly care while [PERSON] et al. [PERSON] et al. (2019) used kinetic energy-harvesting wearables for detecting the mode of transportation.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.8 Autonomous UAV Landing II  
In [76], the authors developed ROS-RL, which is a new framework for UAV landing that improves upon previous methods. From the interaction of the robot with the environment, the framework helps to determine the optimal robot control strategy. In this work, an ROS was used as a middleware layer to facilitate communication between agents and the simulation. In a different work [75], the authors used a RotorS simulator to land on a moving platform. In [76], the authors used realistic simulation tools such as Gazebo and flight control (PX4) to safely and affordably train the UAV.  
The authors in [76] integrated PX4 into Gazebo simulation to improve the realism of their UAV training. The authors evaluated different RL algorithms--DDPG, TD3, and SAC--with a focus on training the UAV to autonomously land on a specific platform. DDPG struggled with convergence in complex environments, while TD3 showed high reward values and consistent landing but required a long training time. SAC exhibited a less stable reward curve as it approached peak performance. The SAC showed that it is an effective algorithm for managing drone landings among the other three algorithms with respect to the landing task. This method shows the ability to rapidly acquire an efficient landing plan and obtain a higher reward. However, it is essential to tackle the instability in the post-convergence curve for practical applications. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work provides a valuable contribution to the field of drone autonomy by presenting a robust and adaptable framework for training drones to perform complex control tasks such as autonomous landing of UAVs. Overall, the work highlightsthe importance of considering both performance and stability when evaluating RL algorithms for UAV control tasks. Exploring more recent RL algorithms for online control applications will enhance the landing process of UAVs.
# A Sustainable Electrochemical-Based Solution for Removing Acetamiprid from Water
## 3 Results  
### Linear Polarization Curves  
Figure 1 compares the linear polarization curves of the DSA and BDD electrodes obtained at a lower concentration of commercial ACT (30 mg L\({}^{-1}\)) using 0.5 M Na\({}_{2}\)SO\({}_{4}\) as the supporting electrolyte with a scan rate of 100 mV s \({}^{-1}\). The DSA anodic polarization curve shows that the oxygen evolution reaction (o.e.r.) starts at a potential close to +1.2 V vs. Ag/AgCl, confirming the active anode behavior towards oxygen evolution [22; 23; 24; 25; 26]. Conversely, the o.e.r. is attained at about +1.7 V vs. Ag/AgCl at the BDD anode according to the polarization curve (Figure 1), presenting a higher oxygen potential than that achieved at the DSA anode. This is an expected result since non-active anodes have higher oxygen potentials than active anodes [27]. Thus, the BDD anode has a higher energy efficiency during the degradation of organic compounds in solution when compared to the DSA electrode [26].  
On the other hand, a different behavior was observed when a well-known concentration of ACT was added to the solution (Figure 1). For example, when 30 mg L\({}^{-1}\) of ACT was added to the supporting electrolyte in the electrochemical cell (dashed lines in Figure 1), a significant increase in the current response was observed at the DSA electrode, shifting the o.e.r. to lower potential values (+1.01 V). This behavior suggests that a direct electron transfer mechanism between the ACT molecule and the TiO\({}_{2}\)-RuO\({}_{2}\)-IrO\({}_{2}\) electrode occurs, revealing the possibility of partial organic molecule adsorption on its active sites [26; 28]. Conversely, at the concentration of ACT under study, the volumetric profile registered at the BDD anode in 0.5 M of Na\({}_{2}\)SO\({}_{4}\) (dashed line in the inset of Figure 1) displays a distinct behavior from that observed at the TiO\({}_{2}\)-RuO\({}_{2}\)-IrO\({}_{2}\) electrode. In this case, a large decrease  
Figure 1: Linear polarization curves of the DSA (blue lines) and BDD (black lines) electrodes in a solution of 0.5 M of Na\({}_{2}\)SO\({}_{4}\), without (bold line) or with (dashed line) 30 mg L\({}^{-1}\) of commercial ACT in solution. Scan rate, 100 mV s\({}^{-1}\) at 25 \({}^{\circ}\)C.  
in current is observed in the region of the o.e.r. as a result of the efficient degradation of ACT in solution by the hydroxyl radicals produced by water discharge and/or as a partial deactivation of the diamond surface [9; 28]. Then, a set of experiments at the batch electrochemical cell were carried out to better understand the degradation efficiency and mechanism attained.