esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
###### Abstract  
A personal-computer-based and a Raspberry Pi single-board computer-based virtual force sensor with EtherCAT communication for a six-axis robotic arm are proposed in this paper. Both traditional mathematical modeling and machine learning techniques are used in the establishment of the dynamic model of the robotic arm. Thanks to the high updating rate of EtherCAT, the machine learning-based dynamic model on a personal computer achieved an average correlation coefficient between the estimated torque and the actual torque feedback from the motor driver of about 0.99. The dynamic model created using traditional mathematical modeling and the Raspberry Pi single-board computer demonstrates an approximate correlation coefficient of 0.988 between the estimated torque and the actual torque. The external torque observer is established by calculating the difference between the actual torque and the estimated torque, and the virtual force sensor converts the externally applied torques calculated for each axis to the end effector of the robotic arm. When detecting external forces applied to the end effector, the virtual force sensor demonstrates a correlation coefficient of 0.75 and a Root Mean Square Error of 12.93 N, proving its fundamental competence for force measurement. In this paper, both the external torque observer and the virtual force control are applied to applications related to sensing external forces of the robotic arm. The external torque observer is utilized in the safety collision detection mechanism. Based on experimental results, the system can halt the motion of the robotic arm using the minimum external force that the human body can endure, thereby ensuring the operator's safety. The virtual force control is utilized to implement a position and force hybrid controller. The experimental results demonstrate that, under identical control conditions, the position and force hybrid controller established by the Raspberry Pi single-board computer achieves superior control outcomes in a constant force control scenario with a pressure of 40 N. The average absolute error is 9.62 N, and the root mean square error is 11.16 N when compared to the target pressure. From the analysis of the results, it can be concluded that the Raspberry Pi system implemented in this paper can achieve a higher control command update rate compared to personal computers. As a result, it can provide greater control benefits in position and force hybrid control.  
robotic arm; Raspberry Pi; machine learning; safety collision detection; hybrid position/force control; EtherCAT
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 4 SPN Models
### Extended Model C: Hot-Standby  
Figure 8 presents the proposed extended model for the Hot-Standby architecture. In contrast to cold and warm standby redundancy, the secondary machine in the Hot model is represented by simply adding a token to the component. This enhancement improves availability because when the primary machine is shut down, the secondary machine immediately provides resources, ensuring that services are always available. Improved outcomes can be achieved because both devices are fully functioning and ready to take over in the event of a failure.  
The formula for calculating availability with the Hot-standby model is shown in the equation below. In order to perform the calculation, all system components must be active, and tokens must exist in Front_ON and Back_ON.  
\[\mathbf{A}=P\{(\#Front\_ON>0)AND(\#Back\_ON>0)\} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & **Component** & **Time** & **\%** \\ \hline \multirow{4}{*}{Front-End} & Front\_MTTR\_OP & 3 & - \\  & Front\_MTTF\_OP & 22,869 & - \\  & Front\_MTTR\_OC & 3 & - \\  & Front\_MTTF\_OC & 34,303.5 & +50 \\ \hline \multirow{4}{*}{Back-End} & Back\_MTTR\_OP & 3 & - \\  & Back\_MTTF\_OP & 22,869 & - \\ \cline{1-1}  & Back\_MTTR\_OC & 3 & - \\ \cline{1-1}  & Back\_MTTF\_OC & 34,303.5 & +50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values entered in the MTTR and MTTF of the secondary machine in the Warm-Standby model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Transition** & **Index** & **Expression** & **Delay** \\ \hline \multirow{2}{*}{Storage} & SWITCH\_TIME1 & [C-01] & (\#SS\_U = 0) & 0.1 \\  & T0 & [C-02] & (\#SS\_U = 1) & - \\ \hline \multirow{2}{*}{Controller} & SWITCH\_TIME2 & [C-03] & (\#CTRL\_U = 0) & 0.1 \\  & T1 & [C-04] & (\#CTRL\_U = 1) & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Guard conditions and delay used in Warm-Standby transitions.
# Recent Applications of Deep Eutectic Solvents in Environmental Analysis
## Recent Applications of DESs in Environmental Analysis
### Analysis of Inorganic Compounds in Environmental Samples
In addition, different kinds of waters, as the sample most evaluated, as well as soils and sediments, were analysed. To a lesser extent, mining residues and incinerated sewage sludge ash as a possible source of valuable secondary resources were studied. Regarding the extraction techniques, LLME approaches in combination with different external assistance stand out, but other extraction techniques were performed, such as m-\(\mu\)-dSPE, SLE, or even GLE. The detection systems were mainly FAAS, and inductively coupled plasma (ICP)-optical emission spectrometry (OES), and only in one case, the extraction procedure was coupled to a chromatographic system. Additionally, it is worth remarking that, although many of the authors commented in the papers about the greenness of the method, in all cases they do so, it is based on the nature of the components that make up the DESs and their easy synthesis, as well as the volume of solvents used. However, in none of them an exhaustive study was carried out in which penalty points were assigned based on all the stages that come into play during the procedure, from sample pre-treatment to analysis. In this regard, it seems that authors that are focused on the evaluation of inorganic species prioritise the accuracy and selectivity of the method over the concern to follow the principles of green chemistry by conducting accuracy studies of the method using certified reference materials (CRMs), as well as evaluating possible interfering species, both organic and inorganic, despite the fact that the application of DESs shows the current trend, also in this field, of developing greener procedures.
## 3 Experimental Analysis and Discussion
### Datasets Introduction  
This paper evaluates the performance of the proposed method in addressing class imbalance using two public bearing datasets.  
1. PU Dataset: This public bearing dataset, provided by Paderborn University (PU), is one of the most commonly used validation datasets for mechanical fault diagnosis [37]. The test equipment includes a test motor, shaft, bearing module, flywheel, and load motor. Experimental data were generated by installing rolling bearings with different types of damage on the test equipment in the test module. During the experiment, two types of fault groups were created: artificial damage and real damage. Specifically, artificial damage was introduced to the rolling bearings through three processes: electrical discharge machining (EDM), drilling, and electrical engraving.
2. HUST Dataset: Provided by Huazhong University of Science and Technology, this is one of the latest public datasets in the field of fault diagnosis [38]. The bearing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Kernel Size (Number)** & **Input Size** & **Output Size** \\ \hline Input & - & (1 \(\times\) 1024) & - \\ DW Conv 1 & \(7\times 1\) (1) & (1 \(\times\) 1024) & (1 \(\times\) 1024) \\ PW Conv 1 & \(1\times 1\) (32) & (1 \(\times\) 1024) & (32 \(\times\) 1024) \\ SE Block 1 & - & (32 \(\times\) 1024) & (32 \(\times\) 1024) \\ Max Pool 1 & \(2\times 1\) & (32 \(\times\) 1024) & (32 \(\times\) 512) \\ DW Conv 2 & \(7\times 1\) (32) & (32 \(\times\) 512) & (32 \(\times\) 512) \\ PW Conv 2 & \(1\times 1\) (64) & (32 \(\times\) 512) & (64 \(\times\) 512) \\ Max Pool 2 & \(2\times 1\) & (64 \(\times\) 512) & (64 \(\times\) 256) \\ Avg Pool & - & (64 \(\times\) 256) & (64 \(\times\) 1) \\ Flatten & - & (64 \(\times\) 1) & (64) \\ FC & - & (64) & (N) \\ Softmax & - & (N) & (N) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of 1D-MobileNet.  
Figure 4: The structure of 1D-MobileNet network integrated with the SE block.  
fault experiments were conducted using the Spectra-Quest test bench, as shown in Figure 5. The test bench consists of the following: 1 Speed control, 2 Motor, 3 Shaft, 4 Acceleration sensor, 5 Bearing, and 6 Data acquisition board. The model of the acceleration sensor is TREA331. The dataset includes vibration signals from bearings in nine different health states under four distinct operating conditions. In our setup, only the data at the operating condition 65 Hz (3900 rpm) were used. The health states include normal (N), inner ring fault (I), outer ring fault (O), ball fault (B), and compound fault (C). The motor power used in the HUST experiment ranges from 0.56 to 0.75 kW. The health states include normal, inner ring fault (I), outer ring fault (O), ball fault (B), and compound fault (C). Except for the normal state, each fault state includes moderate and severe conditions. The experimental setup information for the two datasets is shown in Table 2.
###### Abstract  
This study investigated the outgoing and pollen-gathering foraging activities of _Apis mellifera jememitica_ (_AMJ_) and _Apis mellifera carmica_ (_AMC_) under a hot-arid environment in the presence of nectar-rich melliferous _Ziziphus unnumularia_ flora. The data revealed the differential effects of weather conditions and _Z. mummularia_ flora on the foraging activities of the studied honey bee subspecies in the Rawdat-Khuraim oasis in central Saudi Arabia. _Z. mummularia_ exhibited two flowering seasons, from June-July (season I) and August-October (season II), with a significantly higher mean flowering density observed during season II (404 flowers/m\({}^{2}\)) than during season I (235 flowers/m\({}^{2}\)). _AMJ_ showed significantly higher foraging activities (outgoing and pollen-gathering) than _AMC_ (exotic bees) during all months in each flowering season. The mean outgoing and pollen-gathering foraging rates of _AMJ_ (32.40 \(\pm\) 0.67 and 4.88 \(\pm\) 0.40 workers/colony/min, respectively) were significantly higher than those of _AMC_ (15.93 \(\pm\) 1.20 and 2.39 \(\pm\) 0.23 workers/colony/min, respectively). The outgoing and pollen-gathering foraging activities of the two subspecies fluctuated throughout the different times of day. Foraging activities were considerably high at sunrise (SR) and low at noon (N) during both flowering seasons. We also observed seasonal variations in the foraging activities of both bee subspecies. The mean foraging activities (outgoing and pollen-gathering) were slightly higher in season I (27.43 \(\pm\) 1.21 and 4.46 \(\pm\) 0.45 workers/colony/min, respectively) than in season II (21.71 \(\pm\) 0.86 and 3.02 \(\pm\) 0.22 workers/colony/min, respectively). The thermal window analysis revealed a significant difference between the flight activities (bees exiting and returning to the nest throughout the day) of _AMJ_ and _AMC_; _AMJ_ had a higher temperature threshold than _AMC_. The outgoing and pollen-gathering foraging activities within each bee subspecies were positively correlated. The present study can help researchers understand the performances of honeybees and the association of their performances with weather and nectar-rich flora conditions.  
Keywords:arid environment; bee subspecies; flight activity; foraging; flowering season; _Ziziphus nummularia; Apis mellifera_
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 3 Experimentation and Results
### Evaluation Indicators  
In this paper, the six most common evaluation metrics in the field of image segmentation are selected to measure the overall performance of the segmentation model, which are accuracy (A), precision (Pr), recall (Re), F1 score (F1-score), mean intersection over union (MIoU), and error rate (ER), with the following equation:  
\[\text{A}= \frac{\text{TN}+\text{TP}}{\text{TP}+\text{FP}+\text{TN}+\text{FN}} \tag{9}\] \[\text{Pr}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (10) \[\text{Re}= \frac{\text{TP}}{\text{TP}+\text{FN}}\] (11) \[\text{F1}=2\times\frac{\text{Pr}\times\text{Re}}{\text{Pr}+\text{ Re}}\] (12) \[\text{MIoU}=\frac{1}{2}(\frac{\text{TP}}{\text{TP}+\text{FP}+ \text{FN}}+\frac{\text{TN}}{\text{TN}+\text{FP}+\text{FN}})\] (13) \[\text{V}_{\text{ER}}=\frac{\text{FP}+\text{FN}}{\text{TP}+\text{ TN}+\text{FP}+\text{FN}} \tag{14}\]  
where TP is the number of positive samples that are correctly classified; TN is the number of negative samples that are correctly classified; FN is the number of positive samples that are misclassified; FP is the number of negative samples that were misclassified.  
In the experiments conducted for this paper, accuracy is directly related to the segmentation results; a higher accuracy indicates that most pixels are classified correctly. A higher recall rate means that the segmentation model does not miss many cloud pixels; a higher precision means that the areas predicted as clouds are less often misclassified as non-cloud areas. The F1 score considers both precision and recall, reflecting the balance between the precision and recall capabilities of the model. The MIoU focuses more on the accurate segmentation of cloud areas and background areas. The better the segmentation results, the higher the MIoU value, indicating the model's more precise identification of cloud areas. A higher error rate means that there are more misclassifications in the model's segmentation results, especially in the segmentation of clouds and backgrounds.
# Unmanned Aerial Vehicle (UAV) Robot Microwave Imaging Based on Multi-Path Scattering Model
## 6 Conclusions  
This article presents the simulation results of UAV robot passive imaging using the satellite communication systems based on the multi-path scattering model. In this UAV robot imaging configuration, the incident angle of the transmitter remains unchanged, while the different flight tracks of receivers loaded on different UAVs result in different zenith angles. Furthermore, the simulated signals received from the multi-path scattering model at each position are processed by a passive imaging algorithm. From the imaging results of vegetation, we can see that different zenith angles of the receiver affect the  
Figure 7: The range resolutions as a function of the zenith angle of a receiver.  
imaging results. When the receiving and transmitting antenna is located at the same side of the target area, the greater the zenith angle of the receiving antenna is, the higher the range resolution of the UAV robot passive imaging is. The combination of multi-path scattering model and the imaging algorithm provides an effective method to study the scattering mechanism based on vegetation. At the same time, the method can be utilized to study the effects of vegetation scattering. The simulation results offer a reference for the development and research of UAV robot passive imaging using the satellite communication systems in the field of ground microwave monitoring.  
[PERSON] conceived the idea and developed multi-path scattering model. [PERSON] and [PERSON] modified the model. [PERSON] performed numerical simulation and the passive imaging calculations. [PERSON] and [PERSON] wrote the manuscript, and [PERSON] revised the manuscript. [PERSON] and [PERSON] analyzed the simulation results. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Southwest Technology and Engineering Research Institute of China Cooperation fund grant number HDHDW5901010301.  
Not applicable.  
Data Availability StatementData available on request from the authors.  
The authors declare that they have no conflict of interest.
# Medical Applications of the GEMPix
## 4 Hadron Therapy
The beam enters from the left and passes through the reference ionization chamber before entering the water phantom. The GEMPix is inserted in a watertight box, which is mounted on the positioning system of the phantom. Therefore, depth scans are possible by remotely changing the distance between beam entrance window and GEMPix box. Figure 9: The upper plot shows the Bragg curve for 200 mm range in water, measured by the GEMPix and calculated with FLUKA. The lower plot shows the ratio of the experimental data to the FLUKA curve. The data points match the simulation within \(\pm\)15%.
## 3 PL Prediction and DWN-ANN Design  
### Overview of PL Prediction Scheme  
The overall PL prediction scheme is illustrated in Figure 1. In the first step, an A2G communication scenario is selected, and the measurement campaign is conducted in the chosen scenario to acquire measurement data, including the propagation distance, UAV height, carrier frequency, and the corresponding PL. Secondly, digital map reconstruction and RT simulation are carried out in the chosen scenario, and the RT pre-correction module is performed. Some of the measurement data are utilized to calibrate parameters such as the material conductivity for RT simulations. This calibration step aims to enhance the accuracy of the obtained RT simulation data significantly. Subsequently, the obtained dataset is split into the training and validation datasets, which are utilized for training the NN and assessing its performance, respectively. Finally, the DWN-ANN is constructed and the obtained RT simulation and measurement data are used for the pre-training and optimization training of the network, respectively. The trained DWN-ANN can accurately predict the PL by inputting a new propagation distance, UAV height, and carrier frequency.
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
###### Abstract
Developing a unified model allows our system to learn and cover the degradation patterns of all the different combinations of sheeting brands, sheeting material, color, observation, and orientation angle. The model outputs the expected retro-reflection value for a given year utilizing the traffic sign features. The main contribution of this study can be summarized as:  
* We present a neural network based deep learning approach for traffic sign retro-reflection prediction. Most of the existing works investigated linear polynomial regression models that are unable to effectively model the complex relationship between the different predictors including sign age and retro-reflection. * We present models that learn the retro-reflectivity degradation patterns for different combinations of traffic sign features. This is unlike previous works where separate models were used to learn the retro-reflectivity degradation pattern for different combinations of traffic sign features such as sheeting, color, or observation angle. * Feature-oriented sensitivity analysis is conducted to examine the significance of each feature for determining the sign retro-reflectivity at the end of useful/service periods. The remainder of this paper is organized as follows: Section 2 explores prior works in estimating the retro-reflection of traffic signs and identifying factors that affect the degradation of retro-reflection. Section 3 provides a description of the study area and data collection and their description. Section 4 details the methodology including the experimental configuration for the models. Section 5 highlights the study results. Finally, in Section 6 we summarize the key findings from the current work and provide an outlook for future work.
# Recent Advances in Bacterial Persistence Mechanisms
## 3 Research on the Mechanism of Bacterial Persistence  
### Biofilm  
Persistence and biofilm formation have been shown to be closely related in numerous studies [16; 17; 18]. Bacteria attach irreversibly to the surface of inert or active entities, secrete proteins and polysaccharide matrix, and form a kind of membrane by wrapping the bacterial colonies. This membrane has strong selective permeability to nutritional and antibacterial compounds [19]. As a result, the biofilm provides shelter for potential persisters to escape antibiotics and immune molecules [12]. Existing studies have mainly emphasized the physical protection effect of biofilm. However, increasing evidence indicates that hypoxia and low nutrition in the inner biofilm also contribute to persister formation [20]. The tricarboxylic acid cycle would be downregulated in persisters inside the biofilm when exposed to antibiotic-induced oxidative stress, thereby preventing the production of reactive oxygen species, an essential component of antibiotic-induced bacterial killing [21]. A real-time impedance-based technology was used to examine the kinetics of _P. aeruginosa_ biofilm growth under regular antibiotics [22]. The results showed that biofilm formation rate and outcome varied between different culture media. Although antibiotics can delay biofilm growth and reduce its amount, biofilm formation cannot be completely inhibited at a low antibiotic concentration. Based on these findings, a 'fortress model' of central persisters' growth in the biofilm was proposed [23] (Figure 2). Nonetheless, bacteria at the periphery of the biofilm can form stronger biofilms that are resistant to antibiotics due to their easier access to nutrients than those growing in the core of the biofilm [24; 25]. These studies suggest that nutrition gradients may influence the persister. However, it is still controversial whether growing at the periphery or core of the biofilm facilitates persister formation. Furthermore, the deficiency of the capsule can lead to the biofilm formation. It has been reported that in carbapenem-resistant _Klebsiella pneumoniae_ (_K. pneumoniae_), the mutants that have deletions of core capsule biosynthesis genes produce more robust biofilm compared with the wild type, enhancing epithelial cell invasion and persistence in urinary tract infections [26].