esa-sceva/satcom-chunk-collection huggingface dataset:
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 4 Results and Discussion
### Comparison with State-of-the-Art Methods  
This paper discusses widely used methods such as deep learning-based approaches, YOLO-based face detection, and physiological signal analysis. These are compared to the proposed static and adaptive thresholding techniques that rely on the eye closure ratio (ECR) and mouth aperture ratio (MAR).  
Cost of Implementation and Efficiency Comparison  
1. Hardware and resource requirements: * Proposed method--uses standard camera inputs (e.g., webcams) and the Dlib library for facial landmark detection, which is lightweight and computationally efficient. The system can run on low-cost consumer-grade hardware such as laptops or single-board computers (e.g., Raspberry Pi). Thus, implementation cost is minimal since no specialized hardware or sensors are required;
* State-of-the-art methods--deep learning approaches such as CNN and YOLO require high computational power (GPUs or cloud-based resources) and large datasets for training. Physiological or sensor-based methods require additional hardware like infrared cameras, heart rate monitors, or wearables, which increase implementation costs;
* Computational efficiency
* Proposed method--offers fast real-time processing due to lightweight thresholding algorithms and reliance on pre-existing Dlib-based feature extraction;
* State-of-the-art methods--often slower due to large-scale inference, particularly on hardware without GPUs.  
Table 9 summarizes the strengths and limitations of each method, emphasizing where the proposed system offers improvements.  
While the proposed system effectively uses facial metrics like ECR and MAR for drowsiness detection, it is acknowledged that physiological states (e.g., stress, anger, and fatigue) can influence facial expressions and detection accuracy. Additionally, physiological parameters such as heart rate variability, eye movement patterns, and skin conductance provide valuable complementary information. Future work will aim to integrate these factors in collaboration with specialists in psychology and physiological monitoring to develop a more comprehensive and robust driver monitoring system.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 3 Simulation Analysis and Results
### Model Training
#### 3.1.2 Convolutional Neural Network (CNN)  
This paper proposes a Convolutional Neural Network (CNN) for a multi-output regression problem. The model consists of several components: a Conv1D layer, a Max-Pooling1D layer, a Flatten layer, two Dense layers, and an output layer. The input shape is (10, 3), where 10 is the number of time steps and 3 is the number of features. The Conv1D layer applies 1D convolution to the input and uses a ReLU activation function, while the MaxPooling1D layer performs max pooling to reduce spatial dimensions. The Flatten layer converts the multi-dimensional feature maps into a 1D vector, as the Dense layers require a 1D input. The first Dense layer has 100 neurons, and the second has 64 neurons, with ReLU activation and L1 regularization of 0.001. The output layer has three neurons representing the final predictions (Figure 7b). The image depicts a 1D CNN architecture that processes sequential data. It is made up of an input layer, convolution 1D layer, ReLU activation, max-pooling 1D layer, flatten layer, dense layers 1 (100 neurons), dense layers 2 (64 neurons), and an output layer (3 neurons). The first layer has 100 neurons, whereas the  
Figure 7: Block diagram of used (**a**) Multilayered Perceptron (MLP) and (**b**) Convolutional Neural Network (CNN) classifiers.  
second layer has 64 neurons with L1 regularization to avoid overfitting. The output layer represents the output classes from a classification task.
# Surface Settlement during Tunneling: Field Observation Analysis
## 4 Discussion  
The following conclusions can be drawn based on the analysis of the data obtained at the tunnel construction site:  
1. The dependence of the additional displacements of the facilities above the TBM pressure balance was the most explicit (the correlation was 0.77). The dependence of the displacements on the type of soil was less evident, but it is also necessary to take this parameter into account when designing. There was no dependence on the other factors (see Table 1).
2. In the case of insufficient earth pressure (less than 2.0 bar), the building settlement increased in the considered conditions of the construction.
3. The rate of tunneling and installation of rings in the design position (excluding the cases of halting and restarting the shield) did not significantly affect additional displacements of the monitored facility.
4. Based on the case of this construction facility, there is a need to adjust the pressure balance at a distance of at least two diameters of the designed tunnel from the facility, based on the data of the geodetic observations of the ground surface settlement, taking account of the tunnel depth, as well as the geological and engineering conditions.
5. When the pressure exceeded 2.7 bar, a rise in the deformation benchmark was observed, which indicates the need to limit the pressure in each case.  
It should also be mentioned that a well-studied tunnel enables controlling the pressure balance effectively and significantly reduces the settlement of the ground surface. Therefore, it is recommended to apply a system of advance drilling to identify the geological structure of the face in order to refine and correct the findings of the geological surveys at the initial stage of design.  
Further, the authors plan to improve the method of identifying the pressure balance to further improve the TBM performance and optimize the construction of tunnels.  
Figure 5: The dependence of the building settlement on the pressure balance during a monitoring cycle.  
**Author Contributions:** Conceptualization, methodology, R.H.C.; software, validation, I.O.I. and V.V.S.; formal analysis, writing--review and editing, all authors; investigation, A.Z.T.-M., I.O.I. and V.V.S., supervision, project administration, A.Z.T.-M., resources, R.H.C. and I.O.I.; visualization, V.V.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Characterization of DMSA/PDA/ZnO  
The X-ray powder diffraction (XRD) spectra of ZnO, PDA/ZnO, and DMSA/PDA/ZnO were measured with a Dmax-rA powder diffractometer (Rigaku, Japan), with Cu K\(\alpha\) as a radiation source at a scanning rate of 2\({}^{\circ}\)/min. Contents of elements (C, H, N, and S) in these materials were quantified by an elemental analyzer (Vario Cube; Elementar, Langenselbold, Germany). Scanning electron micrograph (SEM) was taken with a FEI-QUANTA 200 microscope, and the transmission electron microscopy (TEM) images were recorded on a JEOL JEM 2010 HT microscope (Japan Electronics, Tokyo, Japan) at an accelerating voltage of 200 kV. The FT-IR spectra of sample pellets with KBr were recorded on a Nicolet 5700 Fourier transform infrared (FTIR) spectrometer (Thermo Electron Corporation, Waltham, America). The dynamic light scattering (DLS) volume distribution and surface zeta potential (\(\zeta\)) of ZnO, PDA/ZnO, and DMSA/PDA/ZnO (0.1 g/L H\({}_{2}\)O suspension) was measured with a Zetasizer nano device (Malvern, UK).
## 4 Results Validations
### In Close to Human Body  
Later, the antenna performance in terms of S\({}_{11}\) and radiation pattern is measured close to the human body and presented in Figure 9a. For this, the antenna is located directly on the human skin to measure the antenna S\({}_{11}\) and gain spectra. Later, the measured results for the mounted prototype close to the human body are compared to the case of having the antenna in the free space environment. Therefore, the human phantom model is invoked to locate the antenna structure during the measurement process as shown in Figure 9b. It is found that the proposed antenna S\({}_{11}\) spectrum is affected moderately after locating the antenna close to the human body as seen in Figure 9c. The measured antenna gain spectrum is shown in Figure 9d. It is found that the proposed antenna shows degradation in the antenna gain due to the effects of losses by the human tissues [4]. The antenna gain and bandwidth degradations are recorded in Table 3. In general, the degradations are found to be acceptable in the scenario of having the antenna at such a close distance to the human body.  
The measured antenna radiation patterns are found to be significantly affected for the case of the antenna in a free space environment in comparison with those obtained from having the antenna close to the human body as shown in Figure 9a. It is good to mention that the antenna radiation patterns are measured with OFF states case at 2.3 GHz, 3.7 GHz, and 4.4 GHz for both antenna location scenarios at azimuth (theta = 0\({}^{\circ}\)) and zenith (theta = 90\({}^{\circ}\)) planes.  
The antenna performances in terms of frequency of operation, gain, size, and reconfiguration process is compared in detail in Table 4. It is found that the proposed antenna realizes excellent size reduction with an excellent performance in comparison to those published in the literature.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Previous Work**} & \multirow{2}{*}{**Frequency/GHz**} & **Antenna** & \multirow{2}{*}{**Gain**} \\  & & **Ddimensions/mm\({}^{2}\)** & \\ \hline
[13] & 5.5 & \(21\times 17.5\) & 3 \\ \hline
[14] & 5.01–6.12 & \(25\times 20\) & 3 \\ \hline
[17] & 2.5/3.5/5.5 & \(32\times 20\) & 1.67 \\ \hline
[18] & 2.4/5.2/5.8 & \(20\times 22\) & 3 \\ \hline
[19] & 3.5/5.5 & \(35\times 39\) & 2.2 \\ \hline This work & 2.5, 3.9, 4.8 & \(51\times 24\) & 2.9, 4, 11.6 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison between this work and previous research.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{3}{c}{**In the Free Space**} & \multicolumn{3}{c}{**On the Human Body**} \\ \hline
**Frequency/GHz** & **Gain/dBi** & **Frequency/GHz** & **Gain/dBi** \\ \hline
2.5 & 2.9 & 2.3 & 2.1 \\ \hline
3.9 & 4 & 3.7 & 3.3 \\ \hline
4.8 & 11.6 & 4.4 & 9.6 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison between antenna results at two different antenna environments.  
Figure 9: Antenna radiation pattern measurements: (**a**) Experimental setup, (**b**) 2.3 GHz, (**c**) 3.7 GHz, and (**d**) 4.4 GHz.
## 5 Results and Analysis
### Loose Coupling Solutions
#### 5.1.3 Segment 3  
In Figure 12 the trajectories obtained with the different LC approaches are shown. During the beginning of the segment, the GPS/INS shows some disagreements with the reference, due to the partial GPS outages. However, during the remaining part of the segment, with a good satellite visibility, the obtained trajectory is very close to the reference. Including the GLONASS measurements yields a reduction of the initial disagreements, as expected. With the addition of the GNSS-derived yaw aiding and the motion constraints on velocity and height, the obtained trajectory is close to the reference during the whole segment.  
Figure 11: Trajectories obtained with the loose coupling approach (Segment 2).  
Figure 12: Trajectories obtained with the loose coupling approach (Segment 3).
## 2 Materials and Methods
### CNN Architecture
The single-energy model and the 1-output dual-energy model were also trained with low-energy images in order to obtain the corresponding scatter-corrected estimations. Figure 4 shows a scheme of input and output images corresponding to each of the described models. The use of the scatter ratio as the output of the NNs ensures that the image has values in the range 0-1, which is the standard range employed for the training of NNNs and allows the use of ReLU as an activation function in the output layer. The MultiResUNet was trained on Google Colab platform [48; 49] on a Tesla T4 GPU, with 600 epochs using an Adam optimizer (with default parameters) [50], 10 steps per epoch, batch size of 24, and the mean squared error (MSE) as loss function. The original size of input and output images was equal to the resolution of the detector, i.e., \(2050\times 2050\). To avoid memory problems, images were downsized to \(128\times 128\) using bilinear interpolation. Additionally, images were cropped in the craneo-caudal direction to remove the edges (air voxels), so finally, the image size was \(96\times 128\). We split the dataset into 70 training cases and 30 validation cases. As this number of cases is not enough to properly train a  
Figure 4: Scheme of input and output images corresponding to the 3 neural network models presented in this work. The NNs differ in the amount of input and output channels used. convolutional neural network, we performed data augmentation, including vertical and horizontal flips and zoom in up to 50%. These operations still provided realistic images. The output of the network is multiplied by the uncorrected-scatter image, obtaining the scatter estimation. Then, the scatter estimation is subtracted from the input image, obtaining the estimated scatter-corrected image. The software takes 1.5 s to provide the scatter correction for each case, and multiple cases could be estimated simultaneously.
## 3 Results
### Results
#### 3.3.4 The Recognition Effect on Public Datasets  
To assess the generalization capability of PDCNER, we conducted evaluations across three public benchmark datasets: Weibo, Ontonotes, and Resume. As illustrated in Table 5, the PDCNER model achieved the highest F1-scores across all three datasets. Specifically, the F1 values of PDCNER were 9.78%, 4.33%, and 0.82% higher than those of the BERT-BiLSTM-CRF model for the Weibo, Ontonotes, and Resume datasets, respectively. Additionally, compared to the LEBERT model, the PDCNER method showed improvements of 2.53%, 0.37%, and 0.06% for the same datasets. These results indicate that PDCNER not only exhibits superior performance on the pig disease corpus but also demonstrates remarkable generalization capability across different domains.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**1\%**} & \multicolumn{3}{c}{**10\%**} & \multicolumn{3}{c}{**30\%**} \\ \hline  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline BERT-BiLSTM-CRF & 31.79 & 1.80 & 3.41 & 67.92 & 75.84 & 71.66 & 74.59 & 81.44 & 77.87 \\ LEBERT & 17.39 & 11.43 & 13.79 & 74.81 & 83.47 & 78.91 & 74.05 & 80.14 & 76.97 \\
**PDCNER (ours)** & 18.18 & 11.43 & **14.04** & 85.00 & 86.44 & **85.71** & 87.22 & 86.17 & **86.69** \\ \hline
**Model** & \multicolumn{3}{c}{**50\%**} & \multicolumn{3}{c}{**100\%**} & \multicolumn{3}{c}{**30\%**} \\ \hline  & **P** & **R** & **F1** & **P** & **R** & **F1** & & \\ \hline BERT-BiLSTM-CRF & 79.18 & 83.45 & 81.26 & 80.29 & 84.73 & 82.45 & & \\ LEBERT & 81.09 & 83.4 & 82.23 & 87.18 & 86.45 & 86.81 & & \\
**PDCNER (ours)** & 87.68 & 86.71 & **87.19** & 87.76 & 86.97 & **87.36** & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the small-sample experiments.  
Figure 4: F1-score of 3 models with different sample size.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 1 Introduction
[24] designed an automatic navigation system of orchard sprayer based on RTK-BDS, and built a closed-loop control of steering angle based on steering controller, electric pneumatic steering system and three-axis magnetometer. The field tests showed the maximum offset error was not over 0.13 m for the straight tracking at the speed of 2 km/h. However, in unstructured orchards, the terrain, illumination, and climate are complicated, which limits the performance of the orchard navigation system built on such technologies as laser, computer vision, or GNSS. In addition, processing lidar data consumes lots of computing power, and it is also difficult to extract navigation features [25]; computer vision is severely affected by ambient lighting and shadows from surrounding plants such as weeds [26]; GNSS is prone to loss signal due to occlusions [27]. To handle those problems, researchers proposed multi-sensor data fusion methods to develop orchard navigation systems [28; 29], such as combining BDS and IMU together [30; 31], where BDS features high precision, low-cost and good reliability, but it has the problem of losing signals due to interferences from the environment; IMU is isolated from the environment, running with good precision in a short time and generating a high frequency of data, but its error accumulates over time. In this study, a BDS/IMU integrated navigation algorithm based on the error Kalman filter for position and heading measurement was designed to solve the problems of the unstableness of BDS's signal and the accumulative errors of IMU's data. Combined with the advantages of the two, the reliability and accuracy of the navigation system for long-time operation in the orchard were improved. By combining the kinematics model of the spraying robot with the pure tracking model, a path-tracking auto-navigation system was developed. The accuracy and reliability of the system were evaluated in orchard experiments.
## 4 Discussion  
The best delivery rate in all cases was obtained using the TD-UAV Dijkstra algorithm since its buffer load was minimum (the data were loaded only in the stations and on UAVs belonging to the calculated route). The latency in the case of the Dijkstra algorithm was high since it delivered most of the packages. Generally, the other algorithms could only deliver over shorter distances due to buffer restrictions (Table 4). It is known that the Dijkstra algorithm assures the shortest time delivery and, therefore, the best latency.  
The algorithms Epidemic, Spray and Wait, PRoPHET, and MaxProp are classical for DTN, but in our case, as shown, the Dijkstra algorithm time-dependent adaptation was successfully used since the flight timetables were known. The advantages of the TD-UAV Dijkstra algorithm are an exact and optimum route that is a priori calculated assuring the fastest time of delivery from departure to destination if the route exists; a message is not uselessly sent in the network if no route exists from departure to destination, multiple copies of the messages are not uselessly spread through UAVs and station buffers, resulting in useless overloading of the buffers, and of course the rate of delivery success is maximum. The drawback of the TD-UAV Dijkstra algorithm is that the route was calculated using the information about the operating drones and stations at the moment of route calculation and if a drone or a station from the route is down on this route, the message does not reach the destination. All the routes passing through the station or drone that is down are compromised until the fault is detected. Moreover, this problem reappears when the drone/station is fixed until the moment this information is updated. However, the chance of this problem is low and if it appears, it is fixed in time after the drone/station is repaired or the current status of the network is updated. Using, for instance, the Spray and Wait or Epidemic algorithms presented in this paper, any message has the chance to reach the destination even if drones or stations are down since the copy of the message is spread in the network.
## 5 Results
### Classification Performance  
Using the conventional method of mean ADC change (subjects with a mean ADC increase classified as responders and those with an ADC decrease as non-responders) [2,20], the accuracy is 29.4% (25/85), with a sensitivity of 17.95% and a specificity of 60.87% (see Table 4).  
The experiment with AdaBoost involved 10 learning iterations. The RF classifier was composed of 10 trees, each of which is constructed considering five random features. The SVM classifier used non-linear polynomial kernels and normalized all features.  
The results for the experiment using only the general histogram features without GMM are shown in Table 5 with sensitivity, specificity, accuracy and area under the ROC curve (Az). The ROC curves are shown in Figure 4. The curve using conventional mean ADC was plotted by varying the threshold of the mean ADC change used for the classification, while the curve using the three ML techniques were plotted by Weka. Weka plots the ROC curves by varying the threshold on the probability assigned to the positive class.  
With GMM features added, the three classifiers with the same parameter setups were applied to the data. The results are shown in Table 6 with sensitivity, specificity, accuracy and area under the curve (Az) of the ROC curve. The ROC curves are shown in Figure 5.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Classifier** & **Sensitivity** & **Specificity** & **Accuracy** & **Az** \\ \hline Mean ADC change & 17.95\% & 60.87\% & 29.4\% & 0.33 \\ \hline \end{tabular}
\end{table}
Table 4: Performance of the conventional mean ADC classification method.  
Figure 4: ROC curve for three classifiers without GMM features.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Classifier** & **Sensitivity** & **Specificity** & **Accuracy** & **Az** \\ \hline AdaBoost & 45.45\% & 75\% & 63.53\%\({}^{*}\) & 0.61 \\ Random forest & 54.55\% & 73\% & 65.88\%\({}^{*}\) & 0.66 \\ SVM & 27.27\% & 92.3\% & 67.06\%\({}^{*}\) & 0.60 \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparison among three classifiers without GMM features.
_applied sciences_  
_Article_  
**The Intrapelvic Pressure during Retrograde Intracranal Surgery in the Setting of Ureteral Access Sheath Size: Experimental Study on 3D Printed Model**  
**[PERSON] \({}^{1,2,\star}\)\({}^{\copyright}\)**, [PERSON] \({}^{1}\) and [PERSON] \({}^{3}\)  
\({}^{1}\) Department of Normal and Clinical Anatomy, Institute of Medical Sciences, Medical College,  
Rzeszow University, ul. [PERSON] 4, 35-615 Rzeszow, Poland; [EMAIL_ADDRESS]  
\({}^{2}\) Clinical Department ofrology and Unological Oncology, Municipal Hospital in Rzeszow, ul. Rycerska 4,  
35-241 Rzeszow, Poland  
\({}^{3}\) Department of Polymer Composites, Faculty of Chemistry, Rzeszow University of Technology,  
al. Powancow Warszawy 6, 35-959 Rzeszow, Poland; [EMAIL_ADDRESS]\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract: Introduction:** There is no standardised, universal method to assess physical conditions such as pressure in the pelvicalyceal system in real time during RIRS (retrograde intracranial surgery). Therefore, the problem of increased pressure in the upper urinary tract during the procedure is underestimated. Moreover, it can potentially cause micro-damage and longer postoperative recovery. The aim of this study was to evaluate intrapelvic pressure (IPP) during RIRS procedures. **Materials and Methods:** The 3D printed models of the pelvicalyceal system were printed based on a CT scan of the real patient. They were used to perform 50 RIRS procedures with laser lithotripsy of artificially synthesised kidney stones with two different sizes of ureteral access sheath-UAS (10/12 Fr vs. 12/14 Fr) together with different energy settings generated by the holmium:YAG laser. IPP monitoring during RIRS was performed with the use of the PressureWire X Guidewire compatible with the CoroFlow system. **Results and Conclusions:** The results showed that a high IPP of up to 400 cmH\({}_{2}\)O would be achieved using a 10/12 Fr UAS, while the use of a 12/14 Fr UAS would significantly reduce the peak pressure to approximately 100 cmH\({}_{2}\)O; hence, the size of the UAS is a pivotal factor of the IPP generated during the procedure.  
**Keywords:** retrograde intracranial surgery; intrapelvic pressure; 3D printing; ureteral access sheath