esa-sceva/satcom-chunk-collection huggingface dataset:
# A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping
## 5 Discussion
Based on the mentioned numbers, the AUV with the mapping payload (the sonar and the computer) has an estimated endurance slightly over 7 h.  
Currently, the CPP and the simulation environment are implemented in MATLAB. On a laptop, 1.8 GHz quad-core Intel i7, the run time of the CPP is 96 s. We expect that the run time will be further reduced to less than 60 s when implemented in C++ on an embedded computer, e.g., NVIDIA Jetson TX-2 (6 CPU cores and 256 GPU cores). Since the CPP only runs intermittently when the AUV is on the surface, 1 minute run time is acceptable. Currently, the inter-distance between each pair of adjacent candidate transects is 1 meter. As a result, we will have 1200 candidate transects in a 500 m by 700 m workspace. There are two options to further reduce the CPP run time. First, one could increase the inter-distance to reduce the total number of candidate transects. Second, one could use GPU computing to parallelize the reward estimation for candidate transects. To properly apply the proposed method, we shall first confirm the target coverage rate. If it is high, we can choose the setup of run #2. The resultant path will be adaptive lawnmower pattern. If the target coverage rate is low, we can choose the setup of run #7, in the meantime, replace \(min(S_{k})\) with \(mean(S_{k})\). The resultant path will have high coverage efficiency and the unexplored areas can easily be interpolated. Indeed, the weight groups tested in this paper only cover part of possible choices, they can still be optimized to get better performance if necessary.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 4 Analysis of the Influence of Vital System Parameters on System Stability  
Based on the frequency-coupled impedance model derived in Section 2 and the simplified stability analysis method proposed in Section 3, the stability of the grid-connected inverter system under the unbalanced condition is analyzed in this section. The influences of the power circuit and control parameters on the system stability are investigated. In this section, the main parameters are listed in Table 1.
## 3 Case Study and Parametric Analysis  
Using the model presented above, the lining joint leakage of the second Dapu Road Tunnel [36] was analyzed computationally in this section. The influencing factors of the lining joint leakage, including the joint gap, the joint offset, the surface roughness of the EPDM gasket, the service time, and the groundwater pressure, were analyzed, founded on the calculation results. Furthermore, the applicability, advantages, and disadvantages of this mathematical model were elaborated.  
The influencing factors of shield lining joint leakage can be reflected in the model directly or indirectly through transformation. The changing joint gap can be reflected by the contact stress provided by the changing gasket deformation. The joint offset is reflected through the length of the percolation channel, which is approximately the length of the sealing interface \(L_{x}\). Roughness is reflected by the root -mean -square roughness of the contact surface height profile \(h_{ms}\). Service time can be reflected by the decrease of contact stress over time. Groundwater is reflected directly by the \(P_{w}\) in Equation (27).
# BODIPY-Based Nanomaterials--Sensing and Biomedical Applications
## 2 Metallic Nanoparticles@BODIPY
### Cancer Diagnosis and Therapy
#### 2.2.3. Zirconium and Metal-Organic Framework Nanoparticles
Explicit hydrogens of the [Zr\({}_{6}\)(\(\upmu_{3}\)-OH)\({}_{8}\)(-OH)\({}_{8}\)]\({}^{8+}\) cluster are not shown in the structure for clarity. MOFs were adopted to deliver an imaging and therapeutic agent to cancer cells. Within the MOF class, [PERSON] and co-workers chose the MIL representatives (Materials of Institute Lavoisier) formed via trivalent metal linked with carboxylate bridges (firstly described by [PERSON] and co-workers [78; 79]). The authors used MIL based on Fe\({}^{3+}\) ions with the formula Fe\({}_{3}\)-(\(\upmu_{3}\)-O)Cl(H\({}_{2}\)O)\({}_{2}\)(BDC)\({}_{3}\), where BDC was treephthalic acid (Figure 22). The obtained nano-carrier revealed porous octahedron morphology with a diameter ca. 200 nm and surface area within the range 3700-4535 m\({}^{2}\)/g. The particles were modified with 2-aminoterephthalatic acid (17.5 mol %), which provides an anchor for a BODIPY dye (loaded with efficiency 20.9-40.3%). The BODIPY molecule deposited onto MIL did not reveal fluorescence due to quenching by the d-d conversion of iron ions, but the released molecule strongly emits light. Due to the short release time of BODIPY (t\({}_{0.5}\) = 2.5 h) and ethoxysuccinato-cisplatin loaded on MIL (t\({}_{0.5}\) = 1.5 h) in PBS (phosphate-buffered saline), the authors coated loaded nanoparticles with a silica layer. This procedure enhanced the release time to parameters suitable for medical applications--ca. 14 h for BODIPY and ca. 16 h for cisplatin. The authors performed in vitro studies with the human colon adenocarcinoma cell line (HT-29). They concluded that BODIPY derivatives were efficiently delivered into the cells in which they can be visualized, whereas the free dyes did not possess this feature. Simultaneously, it was concluded that cisplatin loaded on the particles showed activity similar to free cisplatin [79]. Figure 21: The simplified chemical structure of UiO-66 MOF decorated with BODIPY dyes [76].
## 5 Experiments and Results
### Implementation Details
The weights of localization networks were initially shared between the dual Siamese paths of the damage classification network and were incrementally updated with a low learning rate during backpropagation. In order to avoid overfitting and to be able to perform training, validation, and testing steps in a reasonable and efficient time, after trying different settings, we kept the number of epochs equal to 70 and 30 for the localization and classification stages, respectively. The localization UNet as a base network required more epochs for better convergence, while its weights can be further used in the classification stage, and the dual-UNets can be trained in fewer epochs. On the other hand, different model configurations may converge at different rates, but having a fixed number of epochs for all the considered models makes the comparison fair. In fact, the model that can achieve a superior performance in a fixed number of epochs can be considered a better model. Furthermore, data augmentation was applied to both stages for regularization purposes to make the models robust to variations and noise. Train and Tier3 images (2799 + 6369 pairs) were used simultaneously for training, while Holdout (933 pairs) and Test (933 pairs) groups were kept for validation and testing, respectively. All the experiments were performed on an NVIDIA GeForce RTX 2080 Ti GPU, and the codes were implemented in Tensorflow and Keras packages. Model training in each epoch took approximately 400-650 s for the smallest to the largest models, while the inference time for each 1024 \(\times\) 1024 \(\times\) 3 image was less than 1 s.
## 2 Proximal Humerus Anatomy and Bone Composition
### Muscles, Tendons, and Movements  
As well as the main parts of the proximal humerus anatomy, the forces that the muscles and tendons generate on the bone are equally important when running an FEA, since they are responsible for the movement of the bone in the simulation and the reaction forces caused by the combination of these loads, which allow us to perform multiple analyses using an FEA. Thus, knowing which muscle and tendons have an effect on the bone, and to what magnitude depending on the specific movement, grants us the ability to replicate the behavior of a real patient in a simulation.  
The proximal humerus is constantly subjected to torsion, compression, flexion, tension, and the combination of these, caused by combined forces and torques induced by the muscles and tendons attached to it (Figure 2), allowing shoulder movement and establishing the movement range. The humeral shaft is attached to the deltoid and pectoralis major. Attached to the greater tuberosity are the supraspinatus, infraspinatus, and teres minor, and positioned superior, posterosuperior, and posterior, respectively. For the lesser tuberosity, only the subscapularis is attached [13; 16; 36; 37; 42]. The balance between the deltoid and rotator cuff (supraspinatus, infraspinatus, subscapularis, supraspinatus, and teres minor) provides the correct variable forces to move the arm naturally.  
If we isolate the natural movements of the shoulder, we obtain three main movements and their counter-movements. First, we have the flexion and extension movements, which consist of raising the limb forward and backward from the sagittal plane, respectively. The supraspinatus and infraspinatus have significant roles in flexion, unlike in extension where the subscapularis does the work [43]. Then, there is abduction and adduction, which are the movement of the limb away and towards the midplane of the coronal plane, respectively. For abduction, the use of the muscle and tendon will vary depending on the elevation angle. The supraspinatus has a greater impact at the beginning of the elevation, then the deltoid becomes more significant. In adduction, the pectoralis major does the work [44]. Finally, internal/external rotation consists of arm rotation towards/away from the centerline of the coronal plane. The muscles and tendons used in external rotation are the infraspinatus and teres minor. In the counterpart movement, the internal rotation uses the subscapularis and pectoralis major most [45]. This series of movements is generally used to study the progress of rehabilitation, but also for biomechanical analysis and experimentation.  
Figure 1: Proximal humerus anatomy, sectioned by colors: the humeral head (blue), greater (green) and lesser (red) tuberosities, anatomical neck (magenta), surgical neck (yellow), and humeral shaft (cyan).
## 3 Jitter Modeling
### Time Interval Error Jitter  
In a high-speed serial data system, TIE jitter refers to the phase difference between the edge of the data signal and the edge of the clock signal, as shown in Figure 2[22]. When the phase deviation is significant, setup time, insufficient hold time, and bit error may occur.  
\[TIE_{k}=t_{Dk}-t_{Ck} \tag{5}\]  
where \(t_{ck}\) is the time of each rising edge of the clock and \(t_{Dk}\) is the time of the data pattern edge position corresponding \(t_{ck}\).
# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 5 Conclusions  
The pros and cons of the image-dehazing methods in recent years as well as the proposed method are summarized as follows. The DCP and CAP dehazing methods based on statistical experience are training-free methods. They obtain a certain degree of image-dehazing quality, but the resulting quality is poor compared to other approaches. AOD-Net uses a simple CNN architecture to learn a function composed of the transmission map and atmospheric light. Although the model is lightweight, efforts are still required to improve its dehazing quality. Without requiring knowledge of the physical model, ReViewNet and TheiaNet use the U-Net architecture as a prototype and integrate the appropriate CNN blocks to produce quality dehazing images. However, the dehazing quality for objects far in the distance may become a drawback of using TheiaNet. Based on the U-Net architecture, the proposed approach integrates image preprocessing blocks, inception-like blocks, SPP blocks, and AGs to achieve a better image-dehazing quality, even for images with sky and objects far in the distance. For a real hazy image, it also produces clearer dehazing results. The proposed model can be considered as a lightweight and real-time model for dehazing applications.  
Conceptualization, C.-Y.T. and C.-L.C.; methodology, C.-Y.T. and C.-L.C.; software, C.-Y.T.; validation, C.-L.C.; formal analysis, C.-Y.T.; data curation, C.-Y.T.; writing--original draft preparation, C.-Y.T.; writing--review and editing, C.-L.C.; supervision, C.-L.C.; funding acquisition, C.-L.C. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Ministry of Science and Technology, Taiwan, under grant No.
# On the Communication Performance of Airborne Distributed Coherent Radar
## 2 Materials and Methods
### System Model
#### 2.1.1 The Overall Topology of the ADCR Communication System
At this time, the receiving signal model of the communication UAV is:  
\[y_{c}=\left(\sum_{i=1}^{N_{radar}}\sqrt{\frac{P_{r}^{i}G_{r}^{i}G_{r}\left(F_{1 }^{i}\right)^{2}}{\left(L_{1}\right)^{a}}}\right)x_{r}+n_{c}, \tag{1}\]  
where \(x_{r}\) is the communication signal carrying the coherent radar echo information, and \(n_{c}\) is the noise. The receiving signal model of the base station is:  
\[y_{b}=\sqrt{\frac{P_{c}\left(F_{2}\right)^{2}}{\left(L_{2}\right)^{a}}}x_{c}+n _{b}, \tag{2}\]  
where \(x_{c}\) is the transmitting signal of the communication UAV and \(n_{b}\) is the noise. According to (1) and (2), the instantaneous SNR \(\gamma_{c}\) at the communication UAV and the instantaneous SNR \(\gamma_{b}\) at the base station can be obtained by  
\[\gamma_{c}=\left(\sum_{i=1}^{N_{radar}}\sqrt{P_{r}^{i}G_{t}^{i}G_{r}\left(F_{1 }^{i}\right)^{2}}\right)^{2}\frac{1}{N_{c}(L_{1})^{a}} \tag{3}\]  
\[\gamma_{b}=\frac{P_{c}\left(F_{2}\right)^{2}}{N_{b}\left(L_{2}\right)^{a}} \tag{4}\]
###### Abstract  
This paper examines applying machine learning to the assessment of the quality of the transmission in optical networks. The motivation for research into this problem derives from the fact that the accurate assessment of transmission quality is key to an effective management of an optical network by a network operator. In order to facilitate a potential implementation of the proposed solution by a network operator, the training data for the machine learning algorithms are directly extracted from an operating network via a control plane. Particularly, this work focuses on the application of single class and binary classification machine learning algorithms to optical network transmission quality assessment. The results obtained show that the best performance can be achieved using gradient boosting and random forest algorithms.  
machine learning; optical networks; quality of transmission; classification; imbalanced and incomplete data; practical applications
# Excavation and Construction Technology of Diversion Tunnel under Complex Geological Conditions
## 3 Calculating the Construction Length of Diversion Tunnel Based on Detection Technology  
Detection technology is the process of using the transmitting antenna of the transmitter to transmit electromagnetic waves with a frequency of (1-n) \(\times\) 100 MHz. The propagation path, electromagnetic field strength, and waveform will reflect with changes in the construction medium parameters of the water diversion tunnel, and be recovered by the reflected waves from the receiving end to the host.  
Therefore, using the received two-way travel time, amplitude, and waveform data, after processing and analyzing the detection image, the spatial orientation and length of the main cave can be inferred.  
Based on the electromagnetic wave velocity \(v_{i}\) of the underground medium in the water diversion tunnel and the two-way travel time \(t_{i}\) of the transmitter receiving the reflected wave, the length of the water diversion tunnel is calculated using Formula (5):  
\[H_{i}=\frac{1}{2}\sqrt{v_{i}^{2}t_{i}^{2}-d^{2}} \tag{5}\]  
In Formula (5), \(H_{i}\) represents the construction length of the water diversion tunnel; \(d\) represents the distance between the transmitter antenna and the receiving antenna.  
The detection technology is based on the premise that there is a significant difference between the detection object and other objects [14]. Therefore, the close relationship between the propagation speed \(v_{i}\) of the transmitter radar wave and the electromagnetic parameters is as follows:  
\[v_{i}\approx\frac{c}{\gamma_{x}/\phi_{x}} \tag{6}\]  
In Formula (6), \(c\) represents the velocity of the transmitter in vacuum, with a value of 3 \(\times\) 10\({}^{8}\) m/s; \(\gamma_{x}\) is the propagation medium coefficient; and \(\phi_{x}\) is the emissivity of the propagation medium.  
In practical work, antennas with different center frequencies should be selected based on the different depths of the water diversion tunnel that need to be detected, and more suitable observation points should be replaced.
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 4 Experiment
### Datasets  
We compared our SSCE superpixel segmentation algorithm with five representative algorithms, including SLIC [12], SNIC [34], LRW [13], USEAQ [23], and CAS [37], on the real-world image dataset BSD500 [24], the medical image dataset 3 Dircadb [25], and a self-built IR dataset. The self-built IR dataset was acquired using an uncooled infrared sensor with a size of 640 \(\times\) 512. The basic parameters of the IR camera are shown in Table 1. IR imaging systems are commonly used in the field of surveillance, therefore this dataset contains natural scenes and urban buildings that partially contain aerial targets. BSD500 has been widely used for segmentation quality evaluation and contains 500 artificially labeled color images with multiple nature scenes. In order to evaluate the segmentation algorithm designed for IR images, the images in BSD500 were converted to grayscale. The 3 Dircadb dataset contains 20 sets of CT images of biological tissues, and the corresponding regions of interest have been accepted by medical experts. Compared with BSD500, 3 Dircadb contains gray images with more noise, due to the difference in imaging modes.
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 2 Hardware Description
### Output Conditioning and Filtering Module for Microcontroller Interfacing  
A second output interface allows the designed probe to be used as an autonomous instrument managed by an Arduino Nano 33 IoT low-cost microcontroller (\(\upmu\)C). For this, an additional three-stage conditioning circuit was developed, following a similar philosophy to the oscilloscope interface described above. The block diagram is shown in Figure 8. The first stage performs a signal attenuation, limiting the signal values into the input voltage span of the 12-bit analog-to-digital converter (ADC) available on the \(\upmu\)C. A second stage adds a DC offset to the signal from the first stage, in order to centre its range to that of the ADC, from 0 to the 3.3 V \(\upmu\)C supply voltage. Before the signal is fed to the converter, a 50 kHz cut-off frequency Sallen-Key filter filters frequencies above the range of interest.  
The circuit diagrams corresponding to the attenuation and offset addition stages are shown in Figure 9. The filtering stage is the same as shown in Figure 7c.  
Figure 8: Conditioning stage block diagram for microcontroller connection.  
Figure 9: Circuit schemes of conditioning circuit stages: (**a**) attenuation stage and (**b**) offset addition stage.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 3 Measurement Monitoring Techniques for Multipath Mitigation
### Stochastic Model of Measurements and Weighting Approach  
Besides the constant and elevation-based weighting models, \(\mathrm{C/N_{0}}\)-based models have been widely investigated in the literature for the purpose of measurement weighting. As shown by References [20; 21; 22], a lower \(\mathrm{C/N_{0}}\) results in less precise range measurements. Therefore, according to the discussion conducted in Section 2.1, the measured \(\mathrm{C/N_{0}}\) can be used as a quality metric to model measurement weights in position solutions.  
* Carrier-to-Noise-density ratio (\(\mathrm{C/N_{0}}\)) metric for stochastic modeling  
Reference [17] presents a relationship between measurement precision and the corresponding \(\mathrm{C/N_{0}}\) which was then used to develop a stochastic model for the GNSS code and carrier phase measurements [19; 20; 21; 22]. The variance of a measurement from the \(i\)th satellite at the \(k\)th measurement epoch is then defined as  
\[\sigma_{i}^{2}(k)=a+b\times 10^{\frac{-C/N_{0}(k)}{10}}, \tag{11}\]  
where \(a\) (in \(\mathrm{m^{2}}\)) and \(b\) (in \(\mathrm{m^{2}Hz}\)) are model parameters and are set based on multipath environment and user equipment [20; 21; 22; 23]. \(C/N_{0}(k)\) is the measured ratio of the received carrier power to the noise density (either smoothed or not), in dB-Hz and at the \(k\)th measurement epoch. This model (sometimes called the _SIGMA-e_ model) is based on the assumption that measurements with \(\mathrm{C/N_{0}}\) value generally have higher precision which can be also effective in mitigating the stochastic errors caused by multipath. In addition to using the absolute value of the \(\mathrm{C/N_{0}}\) measurements, a _SIGMA-\(\Delta\)_ model was also developed by Reference [18] to use measured \(\mathrm{C/N_{0}}\) value differences and a corresponding pre-defined template to de-weight distorted measurements. Such a template can be obtained with either data recording with a specific antenna, use of an undisturbed reference receiver or simply by the use of a sliding moving average window to determine the nominal mean of \(\mathrm{C/N_{0}}\) measurements. Each method has its own limitations and challenges and, even if the nominal value can be estimated, relating the multipath-caused deviation to the precision of the measurements is still unknown and requires model tuning.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 4 Experiment Analysis and Results
### Influence of Flood on L4 for GPS Satellites
#### 4.1.4 Influence of Hardware Receiver on L4  
In order to analyze whether the increase of L4 residuals is caused by the hardware device, the residuals of L4 on the G10 satellite between DOY 199, 2021, and DOY 204, 2021 are compared. The reason for choosing these two days is because DOY 200, DOY 201, DOY 202, and DOY 203 are influenced by rainfall and local small-scale floods. The comparison results are demonstrated in Figure 10. The blue and red lines denote the residuals of L4 on DOY 199 and DOY 204, 2021, respectively.  
From Figure 10, it can be found that the residuals of L4 of the G10 satellite on DOY 199 and DOY 204 almost keep exactly the same as each other. This phenomenon is similar to the results of L4 on DOY 198 and DOY 200, which are presented in Figure 9. The RMS of residuals of L4 on DOY 199 and DOY 204 are 0.14 and 0.16 cm, respectively. These results indicate that the performance of the hardware device before and after the flood is stable and keep normal during these days. The data sets collected from this observation station are processed and uploaded automatically, and there are no maintenance and malfunction record reports during the flood. Considering that the probability of automatic recovery of the receiver damaged on the day of the flood is extremely low, thus, the increase of L4 residuals during the flood is not caused by the hardware device.  
Based on the above analysis, it can be found that the influence of rainfall and hardware device on L4 is very small and can be ignored. Thus, it can be concluded that the influence of flood on L4 combination observation is significant for GPS satellites, and the residuals of L4 perform obviously increasingly during the flood. This result is consistent with the theoretical analysis.  
Figure 10: Residuals of L4 on DOY 199 and DOY 204, 2021 for G10 satellite. The blue and red lines denote the residuals of L4 on DOY 199 and DOY 204, respectively.