esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Composition and Working Process of Autonomous Parking System for Driverless Vehicle  
#### 2.1.1 Composition of Autonomous Parking System for Driverless Vehicle  
The autonomous parking system of the driverless vehicle consists of the following, as shown in Figure 1.  
Figure 1: Composition of autonomous parking system.  
1. Human-driverless vehicle interface: It is a medium for passengers of driverless vehicle to convey parking intention to the vehicle central control system when they have parking needs. After receiving the instructions, the central control system operates the vehicle to complete parking task.
2. Environmental perception system: It consists of Lidar, ultrasonic radar, millimeter wave radar, camera, etc., and is responsible for the detection of vehicle driving position information, parking space, and surrounding obstacle information, etc., and input relevant information into the central control system for further decision-making. Precise environmental perception is the premise of smooth autonomous parking.
3. Central control system: As the core part of autonomous parking system, it processes and analyzes the information from the environmental perception system to judge whether the parking space can meet the parking requirements. On the premise of meeting the parking requirements, according to the real-time attitude and position information of the vehicle body and parking space information, the parking strategy is formulated and control commands, including the front wheel angle, vehicle speed, etc., are output and transmitted to the actuators to realize steering, braking, etc.
4. Actuators: The actuators based on a by-wire chassis consist of the steering-by-wire system, braking-by-wire system, and driving-by-wire system. They receive the control command information from the central control system in real time, operate the steering-by-wire system, braking-by-wire system, and driving-by-wire system according to the command, and control the front wheel angle and vehicle speed respectively.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 3 Results and Discussion
### Fixed Column Tests  
Fixed column tests were carried out to investigate the effect of the pH of the GAC by determining the phosphate breakthrough profiles. Figure 10 shows the phosphate and the pH of the effluent solution for the DI and the phosphate at the left (solid line) and the right (dash) of the y-axis, respectively. The breakthrough occurs after 580 and 720 BV, and the effluent concentration reaches that of the influent at 812 and 951 BV for the GAC and the GACA, respectively, indicating a 24% extension of operation for the GACA. In addition, the phosphate adsorption capacity of 1.45 and 1.76 mg/g is calculated to 1.45 and 1.76 mg/g for the GAC and GACA, respectively, which improves 21% which is similar with the BV increase. These continuous experimental results reinforce the batch isotherm and kinetic results and clearly confirm that the solution pH determines the phosphate adsorption capacity. The effluent solution pH initiated at 7.2 \(\pm\) 0.2 shows significantly different behavior. For the GAC, the solution pH is maintained at approximately 7. However, the initial pH was sharply decreased to 4.7 and gradually recovered to the initial pH of the GACA. The additional pH profiles included a condition for only the DI without phosphate, exhibiting a very similar profile in the presence of phosphate. Therefore, the rapid decrease of pH at the beginning of the operation could be attributed to the desorption of the hydrogen from solid to liquid and not because the ion exchange process replaces hydrogen ions with phosphate. Consequently, it is confirmed that the extended BV operation for the GACA is due to the extended pH\({}_{\text{pzc}}\) and lower solution pH.
## 7 Protocol Comparisons  
Cluster architectures have been proposed for many different purposes. All of them have the following benefits in common:  
Figure 31: Jitter measured during the test.  
Figure 30: Delay measured during the test.  
- Topology updates overhead reduction.  
- The clustering structure is self-organized and adaptable.  
- Fully distributed operation.  
- New nodes do not have to be searched or initiated.  
- Broken routes cloud is repaired locally without rediscovery.  
- Reduction in the energy dissipation.  
- Broadcasts are done only by the boundary nodes.  
- The routing is source initiated.  
- Lower memory overhead.  
- Quite scalable.  
There are many other characteristics that could be different. The cluster scheme can be applied in many different manners in order to achieve diverse benefits. This section compares our proposal with others in existence in order to show the benefits of our protocol. Only those architectures whose protocol has been available and accessible are included.
## 3 System Hardware and Software Structure
### Hardware and Software for Vehicle Perception System
#### 3.2.2 Comprehensive Environmental Perception  
The comprehensive environmental perception system is designed to leverage the capabilities of LiDAR for the analysis of collected point clouds. This facilitates the identification of dynamic information pertaining to external traffic participants. The LiDAR device is conventionally positioned on the top of the testing vehicle. After conducting scans, point cloud data are processed using a pretrained model based on the PointPillars algorithm, enabling object recognition from a BEV perspective, as shown in Figure 5. This procedure yields detection boxes and provides crucial information including target center coordinates, dimensions of the target bounding box, and target motion characteristics based on the identified categories.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 4 The Problem
### Simulator Details  
In this section, we highlight the most important features of the simulator that we developed and used. Our simulator, called AFLoRa [13], is based on a LoRaWAN simulation environment called Framework for LoRa (FLoRa) [12], albeit with significant updates and enhancements.  
FLoRa was built for the OMNeT++ network simulator by reusing components from the INET framework of the latter. FLoRa includes the main components in a LoRaWAN network, covering from physical-layer to application-layer aspects. It follows the main details of the LoRaWAN specification for class A EDs with unconfirmed transmission mode. The model used in the FLoRa implementation includes collisions and the capture effect at the physical layer and end-to-end communication between the EDs and the NS through a backhaul network [33].  
However, FLoRa does not include some important LoRaWAN network characteristics, such as confirmed transmission mode, transmission queue, losses due to full queue, and duty cycle restrictions for both the EDs and the gateway, among others. For this reason, we updated the original FLoRa simulator for the present work, leading to an enhanced simulator that we call AFLoRa. Our simulator provides a more complete implementation of the LoRaWAN specification, with confirmed transmissions, restrictions due to duty cycle regulations, and a queuing mechanism for the application layer and the MAC layer. AFLoRa also supports the alternative SF management modes considered in this paper as possible techniques to counter the SF12 Well problem (see Section 5), along with the default LoRaWAN behavior.  
Furthermore, we also fixed and/or improved several implementation details of the simulator. We corrected the RX2 time scheduling, and we fixed the idle-time values required to comply with the duty cycle restrictions. Moreover, we reorganized the original implementation of the application and the MAC layers to better match the structure described in the LoRaWAN specification, with a stronger separation of those layers, which allows an easier implementation of the new features. This action also includes an update of the message structure used at the MAC and applications layers.
## 4 Results
### Discussion  
The LSMs produced using the models were evaluated by statistical indices and ROC curves. The RF model had better sensitivity, specificity, overall accuracy, and kappa values. The AUC values of the LSMs used in this study were about 80%, indicating reasonable accuracy. The RF model had higher AUC values for the success rate and prediction rate curves than the BRT model. Thus, these models had very high predictive performance. Furthermore, the LSMs would be produced differently depending on the methods used and the landslide explanatory variables selected. The landslide explanatory variables may not make equal contributions, which can affect prediction ability. In this study, the landslide explanatory variables used made different contributions to the models. Table 4 illustrates the importance of each explanatory variable, calculated and normalized in the RF and BRT models. In general, TRI had the highest importance to the models, whereas timber diameter, timber age, and timber density had lower predictive capability.  
Figure 4: Landslide susceptibility maps produced by random forest (RF) (**a**) and boosted regression tree (BRT) (**b**) models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**Boosted Regression Tree**} \\ \cline{2-7}  & **Pixels of** & **Pixels of** & **Landslide** & **Pixels of** & **Pixels of** & **Landslide** \\  & **Class** & **Landslide** & **Density** & **Class** & **Landslide** & **Density** \\ \hline Very low & 13,034 & 2 & 0.073 & 10,671 & 3 & 0.135 \\ Low & 16,871 & 4 & 0.113 & 18,629 & 16 & 0.411 \\ Moderate & 13,854 & 16 & 0.553 & 16,860 & 35 & 0.994 \\ High & 12,917 & 36 & 1.334 & 12,930 & 41 & 1.518 \\ Very high & 10,329 & 82 & 3.799 & 7915 & 45 & 2.721 \\ Total & 67,005 & 140 & & 67,005 & 140 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Landslide density on landslide susceptibility maps produced from the different models.  
From the results, ensemble classification, such as that done by the model used in this study, can improve the performance of single (weak) classifiers and the prediction accuracy of LSM [56]. However, the models had overfitting problems, as indicated by the AUC values calculated using the training and validation datasets. The AUC values of the success rate curve were very high, almost reaching a value of 1, but the AUC values of the prediction rate curve were lower. Especially in the case of the RF model, the AUC value of the prediction rate was decreased by about 20%. This result showed that the RF model was trained excessively by the training data. This can be associated with poor generalization from training data and increased error for real data. Overfitting is a common problem affecting researchers performing machine learning and data mining. There can be many reasons for overfitting. However, in this study, the landslide explanatory variables used still included noise, despite the feature selection process. In addition, because the landslide area is very small compared to the non-landslide area, the model could not learn and predict the non-landslide area.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
###### Abstract  
CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator's experience. Therefore, telemetry data analysis is less reliable, considering the data's complexity. This paper presents a Machine Learning (ML) approach to detecting anomalies in solar panel systems. The main challenge inherited from CubeSat is its capability to perform onboard inference of the ML model. Nowadays, several simple yet powerful ML algorithms for performing anomaly detection are available. This study investigates five ML algorithm candidates, considering classification score, execution time, model size, and power consumption in a constrained computational environment. The pre-processing stage introduces the windowed averaging technique besides standardization and principal component analysis. Furthermore, the paper features the background, bus system, and initial operational data of BIRDS-4, a constellation made of three 1U CubeSats released from the International Space Station in March 2021, with a ML model proposal for future satellite missions.  
a +
Footnote †: journal: Applied Sciences  
0000-0002-2391-1900][PERSON]; [PERSON]; [PERSON]; [PERSON].  
Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels.  
_Appl. Sci._**2022**, _12_, 8634. [[https://doi.org/10.3390/app12178634](https://doi.org/10.3390/app12178634)]([https://doi.org/10.3390/app12178634](https://doi.org/10.3390/app12178634))
## 3 Experiment Results  
### Experimental Settings  
In order to measure the effect and the efficiency of the proposed method.Multiple 16 bits infrared images selected from typical scenes in databases FLIR Thermal Starter Dataset Version 1.3 [31] and LTIR Dataset Version 1.0 [32] were utilized for testing. The information of the images including image size and dynamic range is listed in Table 1. Meanwhile, four well-established methods (HE [1], CLAHE [5],MSR [23], and Reinhard [24]) and two novel approaches (AHPBC [6] and LEP [14]) were introduced for comparison. In those methods, we select the parameters as the authors advised or with experience.  
In Section 2.1.2 (Equation (4)), the three brightness intervals (bright \(S_{1}\), moderate \(S_{2}\), and dark \(S_{3}\)) are set as follows, \(S_{1}=[0.1,0.7]\), \(S_{2}=(0.7,1.5]\), and \(S_{3}=(1.5,8]\).  
In Section 2.2.1 (Equation (6)), we set the value of \(\varepsilon\) be related to the variance of the entire image, because the effect of \(\varepsilon\) is determining whether it is an edge should be preserved. In our experiment, \(\varepsilon=\sigma_{i}^{3}\), where \(\sigma_{i}\) is the variance of the entire image.  
In Section 2.2.2 (Equation (11)), the values of \(\alpha_{k}\) determine the enhancement of the details. Throughout our experiment, we obtain two detail layers, and in Equation (13), \(C=7\) and \(z=0.0001\).  
In Section 2.3 (Equation (18)), \(\rho\) determines the contribution of each block to the fused block's structure. Obviously, the contribution increases along with the strength of the block. Theoretically, \(\rho>0\) is feasible. We set \(\rho=4\) in our experiment. In Equation (21), \(\sigma_{g}\) and \(\sigma_{l}\) control the spread of the profile along \(\mu_{k}\) and \(l_{k}\). We set \(\sigma_{g}=0.2\) and \(\sigma_{l}=0.5\), a smaller value of \(\sigma_{g}\) relative to \(\sigma_{l}\) is important to generate results with good visual impression. Additionally, we set the size of blocks and the moving window stride: \(N=11\) and \(D=2\), as the author advised.  
Throughout the paper, parameters mentioned above are adopted for typical infrared images with different characteristics. Results have demonstrated that the proposed method is capable of effectively enhancing IR image.
## 4 Results and Discussion
### Angular Path Loss
The indoor 3 GPP LOS PL model estimates a PL value of 79.6 dB which is a slight underestimation of the measured omnidirectional PL of 81.8 dB, whereas the ITU-R P.1238 PL model for an office environment slightly overestimates PL, with a PL of 82.3 dB for distance 4.7 m. FSPL is a better fit based on the measurement result. The PL estimation of scenario NLOS2 using the indoor 3 GPP NLOS PL model yields 87.3 dB which corresponds to the measured PL.
## 2 Methodologies and System Description
### Particle Swarm Optimization, PSO  
PSO is a stochastic random optimization process which is inspired by the behaviors of social animals, such as a swarm of fish or a flock of birds. It is one of the most powerful evolutionary developments which is capable to optimize a non-linear equation with the least number of solution parameters. The basic concept of PSO relies on the fact of social sharing information. For each solution variable a swarm of particles are taken. The movement of each particle is controlled by its current position and velocity. At each step each particle remembers its local best position and global best position. For each variable, the optimum position of the particle is denoted as the global best, while local best denoted the last updated optimum position of the corresponding particle.  
The optimality of a particle is checked according to the problem statement [17]. For a given particle \(P_{ij}\) at the \(k\)th iteration position, the velocity updating formulae are given below.  
\[v_{ij}^{x+1}=\omega.v_{ij}^{x}+C_{1}R_{1}\left(p_{ij}^{x}-p_{ij}^{x}\right)+C_{ 2}R_{2}\left(p_{gi}^{x}-p_{ij}^{x}\right) \tag{1}\]  
\[p_{ij}^{x+1}=p_{ij}^{x}+v_{ij}^{x+1} \tag{2}\]  
Here, \(i\) and \(j\) varies from 1 to NP and 1 to NG, respectively. Where NP is number of particles in the swarm and NG is the number of variables in the problem.  
Here, \(v_{ij}^{x+1}\) = velocity of the \(i\)th particle of jth variable at iteration \(k+1\), \(p_{ij}^{x+1}\) = position of the \(i\)th particle of the \(j\)th variable at iteration \(k+1\), \(w\) = inertia weight factor, \(C_{1}\) and \(C_{2}\) are acceleration constant, and \(R_{1}\) and \(R_{2}\) are random values.
## 3 Fire Damage Diagnosis System Using Fuzzy Theory
### Process for CPD I
Figure 6: Possibility of ‘A’ grade for concrete discoloration. Figure 7: Possibility of ‘B’–’E’ grade for concrete discoloration. In the CPD I evaluation, there are three input variable items (i.e., CD, CR and SP) and five fire damage grades (i.e., Grades A-E) for each variable item, and thus, a total of 5\({}^{3}\) (=125) fuzzy rules for output parameters should be also predefined, as shown in Figure 8. CPD I\({}_{1}\) is the first fuzzy rule that is the case when the fire damage grades of all of the input variables are Grade A, while CPD I\({}_{125}\) is the last fuzzy rule, which is the case when the fire damage grades of all input variables are Grade E. As in these two cases, when the grades of all of the input variables are the same (i.e., \(CD_{A}\), \(CR_{A}\) and \(SP_{A}\)), the resulting grades of their fuzzy rule can be easily determined. When the grades of the input variables are different, however, it is not easy to determine the resulting grades of the fuzzy rule, for which the weight factors should be introduced to combine the grades of the input variables. Therefore, in this study, the weight factors of the input variables were determined as shown in Table 5, based on the survey results obtained from the 72 professionals in the field of structural design and fire safety [18]. They were 5.0, 3.78 and 3.19 for the concrete discoloration (\(CD\)), the crack width (\(CR\)) and the spalling depth (SP), respectively, and the fire damage grade of the CPD I\({}_{i}\) was then determined by the weighted average method. As shown in Figure 9, among the total 125 resulting grades, 4 of them were Grade A, 32 were Grade B, 53 were Grade C,  
The final fire damage grade of CPD I is determined using the min-max method. For example, if the possibility of the concrete discoloration belonging to Grade A, i.e., \(\mu(CD_{A})\), is 0.5, that of the crack width belonging to Grade B, i.e., \(\mu(CR_{B})\), is 0.3 and that of the spalling depth belonging to Grade C, i.e., \(\mu(SP_{\text{C}})\), is 0.4, then the corresponding membership degree of the CPD I\({}_{8}\) fuzzy rule, i.e., \(\mu(\text{CPD I}_{8})\), can be estimated to be 0.3 through the min operation, as follows:  
\[ \begin{array}{ll}\mu(\text{CPD I}_{8})&=\mu(CD_{A})\wedge\mu(CR_{B})\wedge\mu (SP_{\text{C}})\\ &=0.5\wedge 0.3\wedge 0.4=0.3\end{array} , \tag{6}\]  
Furthermore, the fire damage grade of CPD I\({}_{8}\) was determined as B by the weighted average method as shown in Figure 9. Thus, the fuzzy value CPD I\({}_{8}\) can be expressed as follows:  
\[\text{CPD I}_{8}=(B,\mu(\text{CPD I}_{8}))=(B,0.3), \tag{7}\]  
This means that the fire damage grade of CPD I\({}_{8}\) was determined as B with the possibility of 0.3. In the same way, the membership degrees of all the fuzzy values CPD I\({}_{i}\) can be calculated, ranging from 1 to 125.
## 2 Backgrounds
### Existing Vertical Federated Deep Learning (VFDL) Methods  
Vertical federated deep learning (VFDL) methods are vertical federated learning methods for deep learning models. Existing VFDL methods can be divided into two types depending on the approach for mechanism design. First, VFDL methods of the training loop parallelization approach [2, 11, 12, 13, 14, 15] parallelize the forward propagation and back propagation that constitute the training loop for the target task. Therefore, since communication for distributed learning occurs at every step of the training loop, a very large communication amount is required. On the other hand, VFDL methods of the data-driven transfer approach [3, 8, 9, 10] train the target task by transfer learning based on features trained by unsupervised representation learning at each party. It is very communication-efficient because only one step of communication is required to transfer the trained features from each party to another party.  
Figure 2 summarizes the mechanism of applying the proposed TT-VFDL-SIM and representative existing VFDL methods to a deep learning model for multivariate IoT time-series analysis. CNTR is a centralized training mechanism that is not parallelized, and the rest are VFDL mechanisms for a deep learning model with the same structure as CNTR. P-VFDL-SL is the Split Learning [2] mechanism most commonly used in existing VFDL  
methods of the training loop parallelization approach. P-VFDL-SLG is an implementation of [16], which added a local gradient update mechanism to improve the performance of Split Learning, tailored to the VFDL scenario for multivariate IoT time-series data. DT-VFDL-AE represents existing VFDL methods of the data-driven transfer approach and utilizes an autoencoder for its unsupervised representation learning. TT-VFDL-SIM was designed based on our task-driven transfer approach.