esa-sceva/satcom-chunk-collection huggingface dataset:
# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 2 Related Works
### Omnidirectional Depth Estimation  
Recently, many deep-learning-based approaches for omnidirectional depth estimation have been proposed. CoordNet [5] uses additional coordinate features to learn contextual information in the equirectangular projection (ERP) space [23]. BiFuse [24] estimates the omnidirectional depth from a monocular image using both equirectangular and cubemap projections, in order to overcome the distortion of panoramas. UniFuse [4] unidirectionally feeds cubemap features to equirectangular features only at the decoding stage, which is more efficient than [24]. OmniFusion [3] converts panoramic images into low-distortion perspective patches, makes CNN-based patch-wise predictions, and merges them to obtain the final output, handling spherical distortions that are difficult for CNNs. 360 SD-net [6] performs spherical disparity estimation on panoramic images from top-down viewpoints, enabling 3D reconstruction of the entire perceived scene. CSDNet [25] presents an approach for panoramic depth estimation using spherical convolutions and epipolar constraints. MODE [7] uses Cassini [26] projection for spherical stereo matching.  
Some methods take fisheye images as input and directly output omnidirectional depth maps. SweepNet [9] uses a four fisheye omnidirectional stereo acquisition system with a spherical sweeping method to project images onto concentric spheres centered at the device origin for matching. OmniMVS [8] is the first end-to-end deep learning method for directly estimating omnidirectional depth maps from multi-view fisheye images, accomplishing cost computation, aggregation, and optimization within its 3D convolutional network in an end-to-end manner without requiring separate pipelines. Crown360 [27] proposes an icosahedron-based omnidirectional stereo matching algorithm, using spherical sweeping and icosahedral convolutional networks to estimate panoramic depth maps from multi-view fisheye images. UnOmni [28] proposes an unsupervised omnidirectional MVS framework based on multiple fisheye images. However, the methods mentioned above typically have a high resource utilization and slow inference speed, due to redundant dense sampling of hypothetical spheres. OmniVidar [29] combines depth maps from multiple traditional stereo pairs but requires additional binocular calibration, which may cause discontinuity issues.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
###### Abstract  
The main goal of this study is to enhance the prediction of ground surface settlements induced by Earth pressure balance (EPB) shield tunneling. In the setting of Changzhou, China, a comprehensive database of long-term ground-displacement findings from Metro Lines No. 1 and No. 2 was analyzed with the goal of assessing the parameters characterizing the settlement, i.e., volume loss, trough width parameter. For the metro lines in the water-rich soft strata of Changzhou, the ground loss \(V_{1}\) is usually in the range of 0.1-0.75%, and the trough width parameter \(K\) is usually in the range from 0.3 to 0.7. A superposition analytical method is proposed to estimate the short-term ground settlements induced by shield tunneling, with attention given to ground loss as well as shield working loads. The suggested analytical approach was found to be in good agreement with the field measurements in the case of EPB shield tunneling. This study can provide a reliable assessment of the long-term as well as short-term ground surface settlements for tunnel design.  
shield tunnel; metro; ground surface settlement; prediction model; settlement trough +
Footnote †: journal: Medical Physics Letters
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
## 2 Background
### M-Sequence  
The M-sequence method that has a maximum length sequence (MLS) is a method of generating a sequence with the length of \(2^{N}-1\), which is the maximum length that can be generated, through linear feedback using a shift register of length \(N\)[22; 23]. The PRN code of the M-sequence method has the property of having the highest correlation value at zero lag in the case of autocorrelation and almost no correlation value in the case of cross correlation. Signals that use the M-sequence code include GPS L2C [11], GPS L5 I [12], GPS L5 Q [12], GLONASS L1 OF [13], and GLONASS L2 OF signals [13].  
As shown in Table 1, the code generator for GPS L2C signals is a representative example of M-sequence code generators [11]. The codes of GPS L2C signals include the L2 CM code and the L2 CL code, and both codes use the same code generator structure. Figure 1 shows the architecture of the code generator for the GPS L2C signals converted into the Fibonacci LFSR. Please note that the code generator of GPS L2C signals has the structure of Galois LFSR in Interface Control Documents (ICD), but the structure can be converted into Fibonacci LFSR when it is used [24]. The code generator shown in Figure 1 consists of one 27-bit shift register, Feedback Tap, and Initial ROM. The feedback polynomial of the shift register is \(f(x)=x^{27}+x^{24}+x^{21}+x^{19}+x^{16}+x^{13}+x^{11}+x^{9}+x^{6}+x^{5}+x^{4}+ x^{3}+1\). The initial value designated for each satellite number is stored in Initial ROM. When the satellite number is input into the Initial ROM, the stored initial value is input into the shift register and operates. After the initialized LFSR value is moved linearly, the value of the shift register is updated through the feedback circuit and operates. At this time, the last register output becomes the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GNSS** & **Frequency Band** & **Signal** & **Code Structure** & **Code Type** \\ \hline \multirow{4}{*}{GLONASS} & \multirow{2}{*}{L1} & L1 OF & LFSR (9b) & M-sequence \\ \cline{3-5}  & & L1 OC (L1 OCd/L1 OCp) & LFSR (10b, 10b/12b, 6b) & Gold code/Kasami code \\ \cline{2-5}  & & L2 & L2 OF & LFSR (9b) & M-sequence \\ \cline{2-5}  & & L2 OC (L2 OCp) & LFSR (14b, 7b) & Kasami code \\ \cline{2-5}  & & L3 & L3 OC (L3 OCd/L3 OCp) & LFSR (14b, 7b) & Kasami code \\ \hline \multirow{4}{*}{Galileo} & \multirow{2}{*}{E1} & E1 & Memory code & Memory code \\ \cline{2-5}  & & E5a (E5a-I/E5a-Q) & LFSR (14b, 14b) & Gold code \\ \cline{1-1} \cline{3-5}  & & E5b (E5b-I/E5b-Q) & LFSR (14b, 14b) & Gold code \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Conventional code generator architecture of GPS L2C signal [11].  
PRN code of the GPS L2C signal. The code generator shown in Figure 1 can support both the L2 CM code and L2 CL code. The L2 CM code generates PRN codes that are 10,230 chips long, and L2 CL code generates PRN codes that are 767,250 chips long.
## 3 Materials and Methods  
### Dataset  
In this research, we use the dataset from the Kaggle competition (\"Understanding Clouds from Satellite Images\" [14]) which was held in November 2019. Competitors were required to identify regions in satellite cloud images with different cloud formations labeled as Fish, Flower, Gravel, and Sugar. Each image contains at least one formation and up to all four.  
Through the crowd-sourcing platform Zooniverse, 10,000 satellite images were screened. Each image was labeled by at least three scientists from a team of 68 scientists. They identified cloud patterns in each image. About 50,000 medium-scale cloud organizations were identified. Lots of noises have been included in the labels in the dataset, and many regions do not even contain clouds. In addition, different types of cloud masks also overlap [37].  
In this dataset, each image is labeled separately for the four categories: Fish, Flower, Gravel, and Sugar. Figure 1 shows example images of the four categories. One label is called a sample, and an image has four samples. If a cloud category does not exist in the image, the corresponding sample is empty. The dataset consists of 5546 images, with 4436 images in the training set and 1110 images in the test set. For the training set, there are 17,744 samples (4 \(\times\) 4436) in total, and 8267 samples are empty. With empty samples being removed, 9477 samples remain. Similarly, for the test set, there are 4440 samples (4 \(\times\) 1110) in total, and 2081 samples are empty, so there are 2359 available samples.  
Figure 1: Samples of the four cloud categories: Sugar, Flower, Fish, and Gravel. (**a**) Sugar; (**b**) Flower; (**c**) Fish; (**d**) Gravel.  
Table 1 shows the basics of the dataset, including the training set and the test set.
# Review of Recent Type-2 Fuzzy Controller Applications
## 2 T2 Fls
### General T2 Fls  
A general T2 FLS (GT2 FLS) accounts for the MF uncertainties, but it weights all of the uncertainties nonuniformly. In an IT2 FLS, however, all of the uncertainty weights are considered to be uniform. The MF of a GT2 FLS is represented in 3D space, where the z-axis resembles the MF value provided by \(\mu_{\overline{A}}\ (x,u)\). Both IT2s and GT2 FLSs are parametric models, but GT2 FLSs have more parameters than IT2 FLSs. A GT2 FLS can be represented in four different ways, such as points, wavy slices, horizontal slices and vertical slices [9]. In Figure 1, the structure of a GT2 FLS is shown, which consists of several blocks. The fuzzifier maps a crisp input vector with multiple inputs into other input FSs. These FSs are also known as input T2 FLSs. The inference engine combines rules and gives a mapping from input T2 FFs to output T2 FSs. The output of the inference engine is a T2 set, which can give a T1 FS using extended defuzzification methods. This is the TR since it transforms output T2 FSs to T1 FSs, and it obtains a TR set. To get the crisp output from T2 FLS, the TR set is defuzzified by finding the centroid of it or through other methods, such as choosing the highest membership point in the TR set [10].  
In Figure 1, the structure of a GT2 FLS is shown which consists of several blocks. The fuzzifier maps a crisp input vector with multiple inputs into other input FSs. These FSs are also known as input T2 FSs. The inference engine combines rules and gives a mapping from input T2 FSs to output T2 FSs. The output of inference engine is a T2 set which can give a T1 FSs using extended defuzzification methods. This is the TR since it transforms output T2 FSs to T1 FSs and it obtains a TR set. To get the crisp output from T2 FLS, the TR set is defuzzified by finding the centroid of it, or through other methods such as choosing the highest membership point in the TR set [10].  
The rule structure of a general T2 FLS is given by [9]:  
If \(x_{1}\) is \(F_{1}^{i}\) and \(x_{n}\) is \(F_{n}^{i}\), then y is \(Y^{i}\) where \(i=1,\ldots,M\).  
In the above rule base, \(x_{i}\) is the j-th input to the system (e.g., sensor input), \(F_{j}^{i}\) is the j-th input Type-2 fuzzy membership and \(Y^{i}\) is the output Type-2 fuzzy membership function.
# On the Seismic Design of Structures with Tilting Located within a Seismic Region
## 6 Ductility Transformation Factors between Simplified and MDOF Systems
### Ductility Transformation Factors (DTF)  
Figure 12 shows the DTF estimated for the five reinforced concrete buildings of interest (see Table 3) and their corresponding equivalent SS, corresponding to different values of asymmetric yielding (\(\alpha\)). It can be observed that the DTF estimated for the five buildings are very close to unity. In addition, it is shown that the DTF do not present any trend regarding the annual rate of exceedance, the level of asymmetric yielding, nor the number of stories of the buildings.  
Figure 12: Ductility transformation factors between simplified systems (SS) and multi-degree of freedom (MDOF) systems for different values of \(\alpha\). (**a**) Symmetric; (**b**) \(\alpha=0.01\); (**c**) \(\alpha=0.02\); (**d**) \(\alpha=0.03\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Building**} & \multicolumn{2}{c}{**T\({}_{1}\)**} & \multicolumn{2}{c}{**V\({}_{\text{b}}\)**} & \multicolumn{2}{c}{**W**} & \multirow{2}{*}{**c\({}_{\text{d}_{\text{y}}}\)**} & \multirow{2}{*}{**d\({}_{\text{u}}\)**} \\ \cline{2-2} \cline{5-8}  & **(s)** & **(Ton)** & **(Ton)** & & & **(m)** & **(m)** \\ \hline
8-story & 1.2 & 1332.45 & 3807 & 0.35 & 0.168 & 0.63 \\
9-story & 1.22 & 1498.2 & 4540 & 0.33 & 0.181 & 0.65 \\
10-story & 1.33 & 1563.02 & 5042 & 0.31 & 0.195 & 0.69 \\
11-story & 1.4 & 1721.1 & 5737 & 0.3 & 0.223 & 0.74 \\
12-story & 1.48 & 1772.68 & 6331 & 0.28 & 0.246 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamic characteristics of the buildings analyzed.  
The results were fitted to a linear function, and it is observed that the expected ductility demand of the SS is, on an average basis, only 4% higher than that of MDOF systems, for any value of the annual rate of exceedance. These results indicate that the use of simplified structural systems for the calculation of strength amplification factors for MDOF structures with asymmetric yielding is slightly conservative. Thus, the expressions proposed in Equations (6) and (7) may be considered as appropriated.
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 5 Validation of Developed Application for Common Emergency Services
### Analysis of Data Packet in Case 1  
In case 1, we conducted tests to validate the developed SDN application. Firstly, an emergency message was sent from Car1 to CarEmer, and the data packet was captured at the CarEmer and RailEmer nodes using Wireshark. Additionally, to measure latency, 500 UDP data packets were sent from Car1 to CarEmer on port 135. The average latency observed was 30.9 ms, as shown in Figure 8.  
Figure 8: Latency test between Car1 and CarEmer.  
After receiving the emergency data packet, CarEmer forwarded it to Car2 and Car3, resulting in an average latency of 7.5 ms, as shown in Figure 9. In this case, the end-to-end latency was (30.9+7.5) 38.4 ms. Similarly, 500 UDP data packets were sent from Train1 to RailEmer, and RailEmer to Train2 and Train3. The average end-to-end latency for this test was 36.9 ms.  
In addition to these tests, non-emergency data packets were also sent between cars and trains and their respective servers. The results of these tests are presented in Table 1. From Table 1, it can be concluded that during non-emergency data transmission, cars are only able to communicate with other cars and their assigned servers, while trains can only communicate with other trains and their assigned servers. This distinction ensures the appropriate differentiation of data traffic in scenarios where both road and railway transportation coexist. However, during an emergency situation, the emergency data packets were shared with every node in the network.
## 2 Materials and Methods  
### Study Area and Field Observation  
Chengdu Basin, southwestern China, is a main rice production region in China, where rice is cultivated once per year. The growing season is typically from May to August. Under the influence of subtropical monsoon climate and basin terrain characteristics, the sky is constantly cloudy and rainy for most of the growing period. The study area, Meishan City, is located in the western part of Chengdu Basin. Field observations were conducted about every 12 days from 2 May to 8 August in 2016 at 30 sample sites (Figure 1). Each site was at least 50m wide and 50m long. A hand-held GPS unit was used to locate the sites and 11 ground control points (GCPs), which were used to geo-reference the radar data and sites. Plant height, stem diameter, length and width of leaves, and tiller and ear number were measured to identify rice phenology.  
Figure 1: The study area imaged by the French SPOT-6 optical sensor on 15 July 2016. Thirty sample sites are located and numbered.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 2 System Description and Implementation Aspects
### Implementation Aspects
#### 2.2.2 Software Architecture  
The received EPC identifiers are stored on a _MySQL_ database. By using the above described fields \(St\), \(Rd\), and \(Km\), each EPC is unequivocally associated to a pair of georeferenced coordinates, which is employed as input of the dynamically-generated map and the web interface designed by using _PHP_, _Javascript_, and _JQuery_. The map view has been implemented by using the framework _Openlayers_, which enable access to the _OpenStreetMap_ dataset. Additionally, the complement _Chosen_ of _JQuery_ has been used to design the selectors and menus.
# Modeling Epithelial Homeostasis and Perturbation in Three-Dimensional Human Esophageal Organoids
## 3 Results
### IL-13 Induces BCH-like Changes in Normal Esophageal Organoids Formed in HOME0  
We next explored the utility of HOME0-grown normal human esophageal 3D organoids as an experimental platform to model pathologic epithelial conditions. We have previously modeled EoE-related reactive epithelial changes and BCH where human esophageal 3D organoids were grown in KSFMC and stimulated with EoE-relevant cytokines including interleukin (IL)-13 [22; 32]. In normal esophageal PDOs, EN1 and EPC2 grown in HOME0 stimulation with recombinant IL-13 resulted in the formation of less differentiated structures compatible with BCH as evidenced by an expansion of basaloid cells expressing basal cell-specific transcription factor SOX2 by immunohistochemistry (IHC) and IVL, an early-stage squamous-cell differentiation marker by immunofluorescence (IF) (Figure 5A). BCH was corroborated by significant upregulation of SOX2 and downregulation of IVL within the basaloid cell compartment at the outermost cell layer of EN1 organoids, as documented by AI-assisted scoring (Figures S3 and 5B-D). However, these molecular changes were quantitatively not as robust in EPC2 organoids (Figure 5E-G), suggesting differential cytokine sensitivity between EN1 and EPC2. Moreover, Ki67, a proliferation marker, was not significantly elevated in either EN1 or EPC2 with BCH-like changes (Figure 5B,E), suggesting that IL-13 delayed post-mitotic terminal differentiation but did not necessarily stimulate basal cell proliferation in a persistent manner.
# An All-Digital Optical Phase-Locked Loop Suitable for Satellite Downlinks +
## 2 Coherent Optical Receiver
### All-Digital OPLL Description
Previously, an all-digital OPLL was demonstrated using a lookup table to implement the fourth power [20]. However, the projected control loop changes for a system subject to a fading signal since the gain \(K_{D}\) is related to the input signal level \(P_{s}\). Consequently, the control loop behavior will change, making the OPLL unstable. Additionally, the phase error signal uses the approximation of \(\sin(\theta)\approx\theta\), for small \(\theta\). Therefore, to use the phase error value without approximation, in this study, we propose a modification of a technique used in DSP systems. Figure 5a shows a block diagram for phase estimation used in the DSP technique. This method is based on the Viterbi-Viterbi algorithm, which is commonly used in DSPs for coherent optical systems [21; 22]. Therefore, the input data \(X(k)\) are complex multiplied by the estimated phase, where the resulting corrected signal will be \(Y(k)=X(k)\cdot\exp[-i\hat{\Theta}(k)]\), where \(\hat{\Theta}\) is the estimated phase. Thus, to obtain the estimated phase error value without approximation, a moving averaging filter (MAF) followed by the four-quadrant arc-tangent function is applied to the power moment of the sampled signals. Then, the estimated phase error can be expressed as:  
\[\hat{\Theta}\left(k\right)=\frac{1}{M}\arg\left\{\sum_{n=k+1}^{k+L}[X(n)]^{M}\right\} \tag{11}\]  
In DSP, the described procedure is realized for blocks of data. However, this study is interested in carrier tracking, where the block diagram is modified and shown in Figure 5b. Thus, the estimated phase directly drives the loop filter input instead of having a multiplication for \(\exp[-i\hat{\Theta}(k)]\). In this way, the phase is corrected sample by sample according to the sample rate of the digital PLL. Regarding the MAF, the drawback of the power moment is the presence of harmonics at high frequencies in its output, which increases the OPLL phase error variance. For this reason, the MAF is employed to filter out the harmonics. Note that the MAF is a particular case of the finite impulse response (FIR) filter with all coefficients equal to 1/L, where L is the average number of samples [23].
On the other hand, spatio-temporal gap-filling methods combine both temporal and spatial methods in a multi-step approach to incrementally remove gaps in the time series of remote sensing data [12; 13; 14; 21]. Machine learning (ML) algorithms have recently emerged as an innovative approach to impute missing values in remotely sensed data for learning complex spatial and temporal relationships between input and target variables [22; 23]. Although optical satellite imagery shows a lower spatial resolution compared with UAV imagery, existing machine learning methods, such as the random forest (RF) algorithm, can capture the relationship between coarse-scale (optical satellite imagery) and fine-scale (UAV imagery) remotely sensed data acquired for the same location and time to impute the missing parts in UAV orthomosaics with high spatial detail required for PA applications. In land-use classification applications, the RF algorithm has been used to combine and optimize the spectral, spatial, and temporal resolutions of satellite and UAV data [24; 25]. However, we are unaware of studies attempting to address the problem of missing parts in UAV orthomosaics using the RF algorithm and ancillary data derived from satellite images. Additionally, the launch of the Sentinel-2 missions by the European Space Agency (ESA) in 2015 [26] and the new generation of orbital platforms, such as Planet CubeSats [27], have greatly enhanced the possibilities for ancillary data with improved spectral and spatial resolutions (10 m and 3 m, respectively) still unexplored in imputation approaches of missing parts in UAV orthomosaics using ML algorithms. So, this study proposes a methodological framework to impute the missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the robust RF algorithm. The main novelty of our proposed framework is using a data inter-calibration approach and the RF's ability to learn complex relationships between remotely sensed images with different spatial and spectral resolutions to impute missing parts of UAV orthomosaics. Our goals in this study were to develop a data-driven spatial imputation methodology that (1) balances the need for high spatial resolution in the UAV orthomosaics with the spatial resolution necessary for feasible PA application in specific crop growth stages, (2) uses ancillary data from satellite imagery within the same or very close acquisition dates to impute the missing parts in UAV orthomosaics, and (3) provides a generalizable and flexible approach that applies to a wide range of remote sensing datasets. Our underlying hypothesis was that spatial and spectral autocorrelation inherent within spectral bands from UAV and satellite images from the same target of interest can be leveraged to impute the missing parts in UAV orthomosaics.