esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
As presented above, patients with erythrodermic psoriasis had a lower QoL than subjects with other disease subtypes. Systemic symptoms such as chills and fever together with malaise and weakness may be a good explanation for the low QoL in erythrodermic patients, but other factors may also play a role. Pruritus is one of the most relevant clinical parameters that may determine the degree of QoL impairment. In concordance with previous studies [27; 28; 29; 30; 31; 32], our data confirmed that pruritus in psoriasis is very frequent, reaching the prevalence of up to 100% in some psoriasis variants, e.g., small-plaque-type psoriasis, scalp psoriasis, and generalized pustular psoriasis [9]. The severity of pruritus correlated with the impairment of QoL, particularly in scalp psoriasis and palmoplantar pustular psoriasis. This observation is slightly surprising as these subtypes were characterized by the lowest mean DLQI scoring (9.8 and 10.8, respectively) among all analyzed subgroups. QoL was decreased in patients with sleeping disorders. The importance of good sleep for overall well-being is well documented and an insufficient amount of high-quality sleep affects many aspects of life [4]. In our study, the most frequent complaint was difficulty with falling asleep, signaled by 70% of patients. Additionally, more than half of them had to wake up from sleep during the night. In the majority of participants, sleeping problems usually did not appear to be strong enough to result in it being necessary to use sleeping pills. As found in our study, itching can directly affect sleep and patients with more severe pruritus had more difficulties in falling asleep, more frequently report awakenings during the night and more commonly needed to use the sleeping pills. [PERSON] et al. obtained similar results, and identified the intensity of pruritus as the major predictor of sleep impairment [33]. [PERSON] et al. [5] also noted that the difficulty in falling asleep was related to itching and explained this by the coexistence of negative emotions such as irritation and discomfort accompanying the pruritus. However, in this study, pruritus intensity did not appear to be sufficiently strong to result in awakenings during the night, which was contrary to our results. Our study had some limitations. At first, only the DLQI was used to assess QoL, without a QoL-specific instrument for psoriasis. However, DLQI is a widely used method of QoL assessment and in many studies it is the major way how QoL impairment is assessed. Furthermore, sleeping disorders were diagnosed on the basis of three simple questions instead of a validated questionnaire, such as Athens Insomnia Scale or Pittsburgh Sleep Quality Index [34; 35]; however, we have used them successfully in our previous studies in patients suffering from pruritus [9; 36; 37] and they were used also by other authors [38; 39].
## 3 System and Security Model
### System Model
#### 3.1.3 Data User/Data Owner  
The data owner generates the delegation path along with the re-encryption keys and encrypted data, then uploads them to the proxy. The data user first queries for data access, then gets transformed encrypted data from the proxy. After receiving the encrypted data, the data user only can decrypt the data and has no right to transform the encrypted data to the user who is not in the delegation path. Note that in a multi-hop proxy re-encryption, the data user can transform the encrypted data to his own delegatee.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 3 Research Method
### Subway Station Structure Element Extraction  
The scanning area of a subway station is large, and the input data always consist of hundreds of millions of points [46]. Data preprocessing is particularly important to avoid a computational burden caused by data overload. The finely registered 3D point cloud model is voxelized and down-sampled. If there are discrete points in the voxel, the set of points within the voxel is replaced by the center coordinates \(\mathbb{V}_{i}\) of that voxel. After all the voxels are searched, the down-sampled point cloud model \(\mathbb{P}^{i}\) is derived. Subsequently, element extraction is performed on the point cloud model \(\mathbb{P}^{i}\) to obtain the facade, slab, and structural column information.  
Element extraction is the process of labeling unstructured measurements in a point cloud and extracting structured information [55]. Over-segmentation can be avoided using a smoothness constraint-based segmentation method [56], which is achieved through the following two steps: (1) Normal estimation: a k-dimensional (KD) tree is constructed for the point cloud by searching the K nearest neighbors of each point, and least-squares fitting is applied to the points in the neighborhood to obtain the best-fit plane. The normal vector of each point is calculated and the residual values are used to detect regions with large curvature variations. (2) Region growth selects the point with the smallest residual as the initial seed. The points in the nearest neighbor that meet the angle threshold \(\theta_{\text{th}}\) are  
Figure 4: Principle of the PL-ICP algorithm, aiming at minimizing the sum of squared distances between the target point and the line segment that connects two adjacent points.  
marked as the current region, and the points that meet the residual threshold \(r_{\text{th}}\) are marked as the candidate seed points. After the search of all the nearest neighbors is completed, the next available seed is accessed from the list of candidate seed queues, and the above operation is repeated until all points are labeled. A region growth of subway station structure element extraction is shown in Figure 5, where \(\overset{\rightarrow}{n}_{i}\), \(\overset{\rightarrow}{n}_{j}\), and \(\overset{\rightarrow}{n}_{k}\) are the normal vector of each initial seed, and different structure surfaces, i.e., facades, slabs, and structural columns, are extracted using the region growing algorithm. Moreover, the surfaces of different structures are shown in different colors.
## 9 Conclusions  
Two feature extraction techniques such as Symbolic Dynamics (SD) and Complex Correlation Measure (CCM) were proposed as an alternative for fault diagnosis in rotating machinery. The proposed features have been used together RF classifications models, which have provided accurate classification of multi-fault of valves and roller bearings in a two-stage reciprocating compressor.  
The classification accuracy is higher than 97.8% by using features extracted from only one sensor of vibration signals. Both feature extraction techniques have a better performance regarding the statistical feature set for classifying multi-fault of valves. Furthermore, the SD feature extraction technique shows a better performance regarding the CCM technique, and it attained a classification accuracy up to 100% for combined multi-fault in valves and roller bearings. Both RF classification models EBT and ESK provided high classification accuracy, however, the ESK classification model shows a higher classification accuracy in the experimental analysis.  
Moreover, the proposed features were also tested over the dataset of a previous research using deep learning approaches, the performance has been better than the previous results. The proposed features are simple and highly accurate when used in combination with RF classification models, being an alternative to the use of deep learning approaches or such kind of rotating machinery.  
The reported test-bed includes sensors for acquiring another type of signals such as pressure, sound and currents. We plan to perform research concerning the application of the proposed features (SD and CCM) and classification models to other available signals, by extending the set to the 25 faulty conditions as well as the acquisition of a valve fault severity dataset. The proposed features could be useful for attaining the classification considering these datasets.  
Conceptualization, R.M.; Data curation, J.-C.M.; Formal analysis, M.C. and D.C.; Funding acquisition, R.-V.S.; Investigation, J.-C.M. and R.M.; Methodology, M.C., E.E. and R.-V.S.; Project administration, R.-V.S.; Software, R.M.; Supervision, M.C. and R.-V.S.; Validation, R.M.; Writing--original draft, R.M.; Writing--review & editing, M.C. and R.-V.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
This work was supported by Universidad Politecnica Salesiana through the research group GIDTEC. The authors would like to thank the anonymous reviewers for their insightful comments and useful suggestions that help to improve the quality of this work. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PHM & Prognostics and Health Management \\ SVM & Support Vector Machines \\ LSTM & Long Short-Term Memory model \\ CCM & Complex Correlation Measure \\ RF & Random Forest \\ CART & Classification And Regression Tree \\ OOB & Out of the Bag \\ EDM & Electrical Discharge Machining \\ SD & Symbolic Dynamics \\ EBT & Ensemble Bagged Tree \\ k-NN & k-Nearest Neighbors \\ ESK & Ensemble Subspace k-NN \\ \end{tabular}
## 2 Related Works
### Research Gaps  
The extensive literature on NOMA confirms its suitability for massive connectivity in next-generation wireless networks. However, most existing studies focus on two-user NOMA systems, which limits their applicability to real-world multi-user scenarios. One of the major challenges in NOMA is iSIC, leading to significant error propagation, especially in multi-user environments. Very few studies examine the impact of realistic HWDs from gNB and UE. To address these issues, HNOMA has been introduced to minimize SIC errors and HWDs while reducing system complexity. Despite allocating higher power to the WUE, cell-edge users experience severe performance degradation in terms of outage probability, SSE, and throughput. C-NOMA enhances the performance of the WUE by allowing retransmission from the SUE and introducing diversity gain. However, this comes at a cost of increased energy consumption for the SUE, limiting system efficiency. To overcome this challenge, an RIS has been integrated into C-NOMA, enabling the SUE to retransmit with lower power consumption. However, RIS-based solutions are constrained by their half-space coverage, which restricts their ability to fully optimize signal propagation. IOSs offer a promising solution by enabling full-space coverage and ensuring efficient signal steering towards the intended UE. To the best of our knowledge, the integration of IOSs in C-HNOMA systems has not been examined in the existing literature. Addressing this gap, our work aims to investigate the potential of IOS-assisted C-HNOMA to improve system performance by enhancing retransmission efficiency and reducing coverage limitations.
###### Abstract  
Autonomous unmanned aerial vehicle (UAV) landing can be useful in multiple applications. Precise landing is a difficult task because of the significant navigation errors of the global positioning system (GPS). To overcome these errors and to realize precise landing control, various sensors have been installed on UAVs. However, this approach can be challenging for micro UAVs (MAVs) because strong thrust forces are required to carry multiple sensors. In this study, a new autonomous MAV landing system is proposed, in which a landing platform actively assists vehicle landing. In addition to the vision system of the UAV, a camera was installed on the platform to precisely control the MAV near the landing area. The platform was also designed with various types of equipment to assist the MAV in searching, approaching, alignment, and landing. Furthermore, a novel algorithm was developed for robust spherical object detection under different illumination conditions. To validate the proposed landing system and detection algorithm, 80 flight experiments were conducted using a DJI TELLO drone, which successfully landed on the platform in every trial with a small landing position average error of 2.7 cm.  
micro unmanned aerial vehicle; autonomous landing; landing-assistive platform; spherical object detection Article
## 4 Experiments
### Evaluation Metric  
In the experiments, we applied three widely-used metrics to evaluate the performance of our model: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). They are defined as follows:  
\[MAE=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}|\mathbf{y}_{i,j}-\mathbf{\hat{y}}_{ i,j}| \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}(\mathbf{y}_{i,j}-\mathbf{ \hat{y}}_{i,j})^{2}} \tag{15}\]  
\[MAPE=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}\frac{|\mathbf{y}_{i,j}-\mathbf{ \hat{y}}_{i,j}|}{\mathbf{y}_{i,j}} \tag{16}\]  
Figure 7: Sensor distribution of PeMSD4 and PeMSD7 datasets.  
where \(\hat{y}_{i,j}\) and \(y_{i,j}\) are the true value and the predicted value, \(N\) is the number of detectors we select in the road network, and \(M\) is the total number of predicted samples.
# Prediction of Total Petroleum Hydrocarbons and Heavy Metals in Acid Tars Using Machine Learning
###### Abstract  
Hazardous petroleum wastes are an inevitable source of environmental pollution. Leachates from these wastes could contaminate soil and potable water sources and affect human health. The management of acid tars, as a byproduct of refining and petrochemical processes, represented one of the major hazardous waste problems in Romania. Acid tars are hazardous and toxic waste and have the potential to cause pollution and environmental damage. The need for the identification, study, characterization, and subsequently either the treatment, valorization, or elimination of acid tars is determined by the fact that they also have high concentrations of hydrocarbons and heavy metals, toxic for the storage site and its neighboring residential area. When soil contamination with acid tars occurs, sustainable remediation techniques are needed to restore soil quality to a healthy production state. Therefore, it is necessary to ensure a rapid but robust characterization of the degree of contamination with hydrocarbons and heavy metals in acid tars so that appropriate techniques can then be used for treatment/remediation. The first stage in treating these acid tars is to determine its properties. This article presents a software program that uses machine learning to estimate selected properties of acid tars (pH, Total Petroleum Hydrocarbons--TPH, and heavy metals). The program uses the Automatic Machine Learning technique to determine the Machine Learning algorithm that has the lowest estimation error for the given dataset, with respect to the Mean Average Error and Root Mean Squared Error. The chosen algorithm is used further for properties estimation, using the R\({}^{2}\) correlation coefficient as a performance criterion. The dataset used for training has 82 experimental points with continuous, unique values containing the coordinates and depth of acid tar samples and their properties. Based on an exhaustive search performed by the authors, a similar study that considers machine learning applications was not found in the literature. Further research is required because the method presented therein can be improved because it is dataset dependent, as is the case with every ML problem.  
acid tar lagoon--ATLs; total petroleum hydrocarbons--TPH; heavy metals and As; machine learning algorithms; hyperparameters +
Footnote †: journal: Applied Sciences
# Urbanization and Grain Production Pattern of China: Dynamic Effect and Mediating Mechanism
###### Abstract
However, there an in-depth understanding the effects of urbanization--one of the direct causes of cropland reduction--on the GPP and its action path is still lacking, which warrants further theoretical and econometric tests. Firstly, most of the existing studies have demonstrated the direct effect of urbanization on the GPP, while the changes in the GPP were path-dependent in the time dimension and did not occur overnight. Thus, the time cumulative effect must be considered when analyzing the causes of changes in the GPP. Secondly, the cross-regional flow of rural labor and agricultural machinery services in the urbanization process resulted in spatial spillover effects of grain production between regions, which necessitated the consideration of the spatial lag effects in the analysis of the changes in the GPP. Thirdly, the action path of urbanization on GPP is still unclear, and a comprehensive theoretical framework is required to identify the intermediary transmission paths between the two. In order to clarify the impact of urbanization on the GPP and its action path, this study characterized the GPP by the grain production concentration index (GPCI) [11] based on panel data of 30 provinces in China from 1996 to 2018 and incorporated urbanization and GPP in an explanatory framework. Upon understanding the spatiotemporal evolution characteristics of the GPP, a dynamic spatial panel econometric model considering the path-dependence and spatial spillover effects of the GPP was built to test the direction and extent of the effect of urbanization on the GPP. Then, the spatial mediating effect model was established to identify the transmission paths by which urbanization affected the GPP from the perspective of changes in mediation factors such as agricultural technology progress, cropland utilization, and planting structure. The differences in the mediation effects of different factors were compared. The above research can help us to further understand the effects of urbanization on the GPP and its action path and provide a theoretical basis and practical reference for realizing balanced coordination and sustainability between urbanization and changes in the GPP.
# Comparison of Radio Frequency Path Loss Models in Soil for Wireless Underground Sensor Networks
## 3 Results and Discussion
From the results shown in Figure 6 it can be concluded that the CRIM-Fresnel model is more suitable for estimating the attenuation in soils with a high water content (>20%) and lower clay content (<50%), while the modified-Friis model provides a better approximation for samples with a lower water content (<20%) and higher clay content (>50%). This is mainly caused by the high effect of the imaginary part of the dielectric permittivity on the estimation values of the CRIM-Fresnel model. In addition, the CRIM-Fresnel model provides a linear estimation of the attenuation of the signal with an increase of distance. This is not similar to the fundamental behaviour of the signal and will result in larger estimation errors for the signal attenuation (over estimation) at larger distances. Based on the attenuation it can be concluded that the transmission of the EM signals in soil is mostly dependent on the soil properties and in particular the percentage of clay present, volumetric water content, porosity and the bulk density in the soil. A soil composition with a high percentage of clay will tend to have more bound water and a high bulk density which adversely effects the propagation of the signal in certain distances. The available attenuation models have been found to have different applicability for these different soil conditions, thus, it is essential to choose the most appropriate model for the local soil conditions when estimating EM attenuation in soil.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 2 Materials and Methods
### Testing Process
#### 2.2.2 Ultrasonic Phased Array Detector Calibration  
Instrument calibration consists of three parts: sound velocity calibration, sensitivity calibration, and TCG calibration. CSK-IA and CSK-IIA-1 test blocks were used for this calibration, and the physical appearance of the standard test blocks is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Scan Type & Fans weep \\  & Focus Type & depths \\ Scanning parameters & Focus distance & Twice the plate thickness \\  & Starting Angle & \(37.00^{\circ}\) \\  & End Angle & \(73.00^{\circ}\) \\  & Sonic Mode & transverse wave \\  & Sampling Frequency & \(100\) MHz \\ Detection parameters & Evaluation value & \(-18.00\) dB \\  & Quantitative value & \(-12.00\) dB \\  & Judgment value & \(-4.00\) dB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection parameters.  
Figure 2: (a)Test block design; (b)Physical drawing of customized test block.  
1. Velocity Calibration: For velocity calibration using the CSK-IA test block, position the inspection probe above the center of the block's curved surface. Move the probe back and forth to locate the highest echoes of R50 and R100 on the curved surface. Secure the probe in place, adjust the gate position to encompass the highest R50 echo in the A-scan view, then click \"Measurement 1\". Adjust the gate position to encircle the highest echo of R100 in the A-scan view, click \"Measurement 2\", and finally click \"Calculate\" to obtain the measured velocity and replace the original velocity.
2. Sensitivity Calibration (ACG Correction): Place the inspection probe above the center of the curved surface of the CSK-IA test block. Move the probe to find the highest echoes of R50 and R100 on the curved surface, and adjust the gate position to encircle the highest echo in the A-scan view. Adjust the gain so that the highest echo does not exceed the full screen. The instrument would automatically obtain the envelope of the highest echo within the sector scan angle range. Finally, click \"Apply\".
3. TCG Calibration (Time Gain Correction): For calibration using the CSK-IIA-1 test block, flip the block and sequentially identify three transverse echo signals at different depths. Enclose each echo with a gate and adjust the gain so that the peak envelope of the transverse echo signal is within a certain amplitude range. Click \"Sample\" for each, obtaining three sampling points (increasing sampling points improves effectiveness, typically taking three sampling points, with the depth of the third sampling point greater than twice the thickness of the measured workpiece). The instrument automatically calculates the TCG curve.