esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+
## 5 Conclusions  
In this paper, the improved DeeplabV3+ model is used to extract the crack area in the panoramic image, and then obtain the edge of the crack in the segmentation map. The SIFT algorithm is used to match the three feature points of the original left and right images, and the conversion relationship between the image coordinates and the spatial coordinates is calculated to obtain the crack width information. Experiments on concrete pavement show that the method can measure the crack width on a concrete plane accurately. The main conclusions of this study are as follows:  
1. The improved DeeplabV3+ model using the L-MobileNetV2 backbone network, IDAM module, ECA attention mechanism, and modified loss function can segment the crack area in the image more accurately than the current mainstream segmentation models. The MIoU, MPA, and PA of the model are 92.26%, 95.54%, and 99.45%, respectively.
2. Experimental results show that the method proposed in this paper has good measurement accuracy on the surface of the concrete structure. The error value of crack width measurement is less than 0.2 mm, and the error rate is less than 4%. Changing the angle between the camera optical axis and the concrete plane to measure the crack under 90 degrees to 10 degrees, it is found that the measured crack width RMSE increases with the decrease of the angle, but is not higher than 0.2 mm.
3. The proposed method is easy to deploy and improves crack detection efficiency. In the future, it can be integrated into a mobile automation platform to replace manual work and realize the regular detection of cracks on concrete pavements, bridges, and other surfaces. At the same time, without changing the resolution, the theoretical error of binocular vision measurement will increase rapidly with the increase in distance, and the current system cannot guarantee the accuracy of long-distance measurement.  
C.C.: Methodology, software, formal analysis, data curation, writing--original draft, investigation; PS.: Conceptualization, validation, supervision, writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
The research is funded by State Grid Henan Electric Power Company Technology Project (Grant No. 52170220000R00K1360000).  
Not applicable.  
Concrete crack segmentation data sets can be obtained from [[https://pan.quark.cn/s/432a37e1516e](https://pan.quark.cn/s/432a37e1516e)]([https://pan.quark.cn/s/432a37e1516e](https://pan.quark.cn/s/432a37e1516e))(accessed on 19 February 2023).  
The authors declare that this study received funding from State Grid Henan Electric Power Company. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.
# Design of a 3-Bit Circularly Polarized Reconfigurable Reflectarray
## 2 Materials and Methods
### Design of 3-Bit Circularly Polarized Reconfigurable Reflectarray
The DC control circuit in this study utilized an FPGA as the main control system. Since the reflection array consists of a total of 64 cells, with each containing 8 PIN diodes that need to be controlled, the control system needs to manage the DC bias of 512 PIN diodes. The general number of I/O port pins on the FPGA cannot meet this demand. In order to \begin{table}
\begin{tabular}{c c c c c} \hline \hline Calculated & \([-22.5,22.5]\) & \([22.5,67.5]\) & \([67.5,112.5]\) & \([112.5,157.5]\) \\ \hline Quantized & \(0\) & \(45\) & \(90\) & \(135\) \\ compensation phase & \([157.5,202.5]\) & \([202.5,247.5]\) & \([247.5,292.5]\) & \([292.5,337.5]\) \\ \hline Quantized & \(180\) & \(225\) & \(270\) & \(315\) \\ compensation phase & \(180\) & \(225\) & \(270\) & \(315\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Three-bit compensated phase quantization results. Figure 8: Structure of the 3-bit circularly polarized reconfigurable reflectarray: (**a**) model and (**b**) the distribution diagram of units. reduce the occupancy of the FPGA's I/O pins, this work employed the 8-bit shift register SN74 HCS594 to achieve serial-parallel data conversion. The DC control circuit system is shown in Figure 9, and the 8 circuits are connected to the back of the FPGA, where each circuit is equipped with 8 cascaded 8-bit shift registers. Each shift register can control the on/off state of 8 PIN diodes, thereby controlling a unit's operational state. Then, the system can control the operating state of 64 independent units by 64 8-bit shift registers. During operation, the computer controls the FPGA to output an 8-way data signal, shift register clock signal, and storage register clock signal, which are, respectively, connected to the corresponding pins of the first register of each way. Each data signal is fed into the first shift register of each column on the rising edge of the shift register clock signal. After 64 clock cycles, the first data output of each path from the FPGA reaches the highest output data bit of the last shift register in each column. At this time, the storage register clock appears at the rising edge, 64 shift registers output 8 control signals through the current limiting resistor, LED (light emitting diode), by which the state of each unit can be accurately controlled.
## 2 Method Analysis
### Distributed MVDR (DMVDR) Beamformer  
As the centralized beamformer requires an FC, there are two disadvantages of high energy consumption and poor scalability. The distributed beamformer is performed through the communications between a node and its adjacent nodes, which means that only local information and local processing are used to obtain the same optimal estimated result as Equation (14). Therefore, the distributed approach is more suitable for WASNs. In distributed beamformers, Equation (14) is rearranged as follows:  
\[Z=\mathbf{W}^{\text{H}}\mathbf{Y}=\left(\frac{\mathbf{R}_{in}^{-1}\mathbf{H}}{\mathbf{H}^{\text{ H}}\mathbf{R}_{in}^{-1}\mathbf{H}}\right)^{\text{H}}\mathbf{Y}=\frac{\sum_{i=1}^{N_{d}}H_{i}^{ H}\sigma_{i}^{-2}Y_{i}}{\sum_{i=1}^{N_{d}}H_{i}^{H}\sigma_{i}^{-2}H_{i}}=\frac{ \frac{1}{N_{d}}\sum_{i=1}^{N_{d}}H_{i}^{H}\sigma_{i}^{-2}Y_{i}}{\frac{1}{N_{d} }\sum_{i=1}^{N_{d}}H_{i}^{H}\sigma_{i}^{-2}H_{i}}, \tag{20}\]  
where \(\frac{1}{N_{d}}\sum_{i=1}^{N_{d}}H_{i}^{H}\sigma_{i}^{-2}Y_{i}\) and \(\frac{1}{N_{d}}\sum_{i=1}^{N_{d}}H_{i}^{H}\sigma_{i}^{-2}H_{i}\) can be computed by the average metropolis weight local average consensus algorithm, which means that an FC is not needed to collect the signals of all the nodes here.  
Two initial values of node \(i\) are defined, namely \(\widetilde{Y}_{i}(0)=H_{i}^{H}\sigma_{i}^{-2}Y_{i}\) and \(\widetilde{N}_{i}(0)=H_{i}^{H}\sigma_{i}^{-2}H_{i}\). Then, Equation (20) can be written as follows:  
\[Z=\frac{\frac{1}{N_{d}}\ \sum_{i=1}^{N_{d}}\widetilde{Y}_{i}(0)}{\frac{1}{N_{d}} \ \sum_{i=1}^{N_{d}}\widetilde{N}_{i}(0)}, \tag{21}\]  
Let \(\widetilde{Y}_{ave}=\frac{1}{N_{d}}\ \sum_{i=1}^{N_{d}}\widetilde{Y}_{i}(0)\) and \(\widetilde{N}_{ave}=\frac{1}{N_{d}}\ \sum_{i=1}^{N_{d}}\widetilde{N}_{i}(0)\), Equation (20) can be written as follows:  
\[Z=\frac{\widetilde{Y}_{ave}}{\widetilde{N}_{ave}}, \tag{22}\]  
Then, the final purpose of the distributed MVDR (DMVDR) beamformer is to achieve the average \(\widetilde{Y}_{ave}\) and \(\widetilde{N}_{ave}\). Here, we used the average metropolis weight local average consensus algorithm to fulfill the distributed evaluations of \(\widetilde{Y}_{ave}\) and \(\widetilde{N}_{ave}\). Set \(\widetilde{\mathbf{Y}}(t)=\left[\widetilde{Y}_{1}(t),\widetilde{Y}_{2}(t), \cdots,\widetilde{Y}_{N_{d}}(t)\right]^{\text{T}}\) and \(\widetilde{\mathbf{N}}(t)=\left[\widetilde{N}_{1}(t),\widetilde{N}_{2}(t), \cdots,\widetilde{N}_{N_{d}}(t)\right]^{\text{T}}\) as the vector \(\widetilde{\mathbf{Y}}\) and \(\widetilde{\mathbf{N}}\) at iteration \(t\), respectively. According to Equation (18), estimations of \(\widetilde{\mathbf{Y}}\) and \(\widetilde{\mathbf{N}}\) for each iteration \(t\) are given by the following equations:\[\widetilde{\mathbf{Y}}(t) = \mathbf{U}(t)\widetilde{\mathbf{Y}}(t-1), \tag{23}\] \[\widetilde{\mathbf{N}}(t) = \mathbf{U}(t)\widetilde{\mathbf{N}}(t-1), \tag{24}\]  
Let \(\overline{Z}_{i}(t)\) denote the distributed MVDR output at iteration \(t\), which can be calculated by using the following equation:  
\[\overline{Z}_{i}(t)=\widetilde{Y}_{i}(t)/\widetilde{N}_{i}(t), \tag{25}\]
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Ground Segment  
The ground segment is composed of two bands: UHF and S-Band, with S-Band being the main TTC link and UHF being the backup and the main one during LEOP. The main GCS shall be the one that UOA has been developing at Pasachna, Greece. However, a second GCS is considered to ensure a successful handling of the constellation. Since UOA does not own another GCS, GCS as a service is the go-to option. For this, KSAT's GCS at Nemea, Greece, is baselined.  
UOA's GCS is located at 38\({}^{\circ}\)34\({}^{\prime}\)10.39\({}^{\prime\prime}\) N, 23\({}^{\circ}\)38\({}^{\prime}\)54.55\({}^{\prime\prime}\) E (WGS'84) with an elevation of 42 m (above sea level), and KSAT's GCS is located at 37.8\({}^{\circ}\) N and 22.6\({}^{\circ}\) E, as illustrated in Figure 3 [8].  
\begin{table}
\begin{tabular}{c c c} \hline
**Objective Number** & **Mission Objective** & **Success Criteria** \\ \hline Objective 1 & \begin{tabular}{c} Spacecraft shall perform ODTS by \\ relying on MEO GNSS. \\ \end{tabular} & \begin{tabular}{c} At least one spacecraft shall perform ODTS \\ once by relying on MEO GNSS throughout the \\ mission duration. \\ \end{tabular} \\ \hline Objective 2 & \begin{tabular}{c} The mission shall demonstrate SLR. \\ \end{tabular} & \begin{tabular}{c} The altitude of a single spacecraft in orbit shall \\ be measured via SLR technique at least once. \\ \end{tabular} \\ \hline Objective 3 & \begin{tabular}{c} The system shall provide PNT \\ services to Germany. \\ \end{tabular} & \begin{tabular}{c} A user at ground level in Germany shall be able \\ to determine its 3D position via the system at \\ least once throughout the mission duration. \\ \end{tabular} \\ \hline Objective 4 & \
\begin{tabular}{c} Spacecraft shall remain operational \\ for the full 3-year mission duration. \\ \end{tabular} &
\begin{tabular}{c} At least one spacecraft shall be operational for \\ 3 years in orbit. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Mission Objectives—success criteria.  
Figure 3: GCS locations.
# Profile Design of the Grooved Die and Rolling Force Prediction in the Cold Pilger Rolling Process
## 2 Rolling Force Prediction in the Cold Pilger Rolling Process  
### Cold Pilger Rolling Process Overview  
The cold pilger rolling process incrementally reduces the outer diameter and wall thickness of a hollow metal pipe. This method was first designed by the [PERSON] brothers. It is mainly used for the manufacturing of pipes by cold working. In the pilger rolling process, forming of the pipe is performed by a pair of roll dies (top/bottom dies) with elliptical grooves in the circumferential direction. The pair of roll rotate forward  
Figure 2: Various mandrel surface profiles: (**a**) Linear profile; (**b**) Cosine profile; (**c**) Quadratic profile.  
and backward approximately half a turn each in opposite directions, with the workpiece intermittently moving forward and backward (Figure 3). At this time, the workpiece is subjected to the axial force in the opposite direction to the feed direction by the rotation of the roll and the compressive stress is generated inside the workpiece. In addition, once the rolling is progressed, the workpiece is formed by rotating at a constant angle. These incremental forming characteristics and compressive stress due to axial force contribute to increase the elongation of the workpiece. In a single process, a typical pipe can achieve up to 90% cross-section reduction without process annealing.  
Figure 4 shows a schematic diagram of rollers currently used by companies throughout the world. The equipment composition is the following: roll (_a_, _b_) installed by a pair of roll shafts (_c_, _d_) are assembled in the saddle (_f_) to move back and forth by the crank mechanism (_e_). The mandrel (_l_) in the center of the tube (_g_) is reinforced by the support (_m_), and its axial position is adjustable with screws (_n_). The tube has a clamping device (_k_) that drives the tube forward by the screw mechanism (_i_, _h_).  
The shape of the mandrel tip is conical and gradually tapers. The shape of the inner surface of the roll coincides with the diameter profile of the mandrel and the tube wall thickness. The dimensions of the working tube are determined by the mandrel shape, the position of the mandrel relative to the roll, and the shape of the roll groove. The conical part of the mandrel and each cross-sectional shape of the roll groove are almost circular.  
As shown in Figure 5, the diameter of the groove decreases from the inlet to the outlet, as in the cross-sections _A-A_, _B-B_, and _C-C_. The cross-section of the groove close to the inlet  
Figure 4: Schematic description of cold pilger rolling process.  
Figure 3: Side view of the pilger rolling process, showing dies rolling without sliding along the mandrel/tube system: (**a**) Forward rolling; (**b**) Backward rolling.  
(\(A\)) is slightly elliptical. The roll and material are not in contact at the left and right fillet portions of the roll horizontal axis. Meanwhile, the cross-section of the groove close to the outlet (C) is circular, and the inner dimension of the roll of the forming part is the same as the outer diameter of the worked tube.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.1 Uncertainty  
The entropy and the disorder level of the original and recovered signal are shown in Figure 2 through confidence ranges (95%). For the case of entropy, most of the values from the original signal were around 4.6 (Figure 2a), whereas for the encrypted signal they were around 7.76 (Figure 2b). Something similar happened with the level of disorder, where the original signal had DL values that concentrated around 0.13 (Figure 2c), while the DL values in the encrypted signal oscillated around 1.13 (Figure 2d). This means that the degree of uncertainty of the data in the encrypted signal is significantly greater than in the original signal, approaching its ideal value (i.e., 8 for entropy, and 2 for \(DL\)).  
Thus, the high entropy and \(DL\) values of the encrypted signal would not raise any suspicion when it is transmitted, and in turn the behavior of the signal moves away from deterministic behavior.
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
###### Abstract  
Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classical convolutional network is obviously insufficient in distinguishing ability. In this paper, a classification method of ground-based cloud images by improved combined convolutional network is proposed. To solve the problem of sub-network overfitting caused by redundancy of pixel information, overlap pooling kernel is used to enhance the elimination effect of information redundancy in the pooling layer. A new channel attention module, ECA-WS (Efficient Channel Attention-Weight Sharing), is introduced to improve the network's ability to express channel information. The decision fusion algorithm is employed to fuse the outputs of sub-networks with multi-scales. According to the number of cloud images in each category, different weights are applied to the fusion results, which solves the problem of network scale limitation and dataset imbalance. Experiments are carried out on the open MGCD dataset and the self-built NRELCD dataset. The results show that the proposed model has significantly improved the classification accuracy compared with the classical network and the latest algorithms.  
Keywords:convolutional neural network; classification of ground-based cloud images; combined convolutional network; overlap pooling; attention mechanism +
Footnote †: journal: Applied sciences  
Article Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 3 Results
### Quantitative Analysis of LULC Changes in Maseru  
Table 3 presents the areal extent for each LULC class in the study area of almost 29,825 ha between 1988 and 2019. Our results show that the landscape of Maseru in 1988 was dominated by two-thirds (63.1%) of natural land cover classes consisting of grassland vegetation (43.3%), woody vegetation (5.5%) water bodies (2%) and bare land (12.3%) and decreased to 10.5%, 3.3%, 1.3%, respectively by 2019, except for bare soil which rose to 35.3%. In 2019, only 15,013 ha remained as natural land cover, suggesting a conversion of 3794 ha to land use activities over the study period. The remaining 11,018 ha of land was occupied by land use activities (built-up area and agricultural land). As of 2019, we noted a tripling of the built-up area from 15.3% in 1988 to 48% in 2019. This is of concern as it is an extremely hard and irreversible type of change, completely obliterating the original natural landscape and unlikely to be reversed to its original natural state. The same period also saw an unexpected decrease in agricultural areas from 21.7% to 1.7% of the city.
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 7 Multi-User Substring Position Searchable Symmetric Encryption  
Our original system model shown in Figure 1 includes only three single entities. To make an important step towards widespread adoption of the searchable encryption techniques, there is a need to efficiently support hundreds, even thousands of users in the cloud. In this section, we consider a simple extension to our work, where a data owner has a document collection, and there is a group of data users that wants to query encrypted data in the cloud.  
[PERSON] et al.'s [6] solution extends the single-user searchable encryption framework with broadcast encryption [35], where the data owner sends the searchable index and encrypted document collection to the cloud, and a group of cloud users is allowed to invoke the search over encrypted cloud data. The framework describes the solution where the data owner distributes a single shared secret key among the group of cloud users. However, this solution may not work in the real-world cloud environment that involves a potentially large number of data users, since a single secret key is given to all participants. For instance, if the data owner decides to revoke the search access for one cloud user, he/she will have to generate a new key and distribute it to the remaining users. It is preferable that each cloud participant could keep its own secret key, thus making key management easier and more efficient.  
We propose a new multi-user substring position searchable symmetric encryption (MSSP-SSE) scheme that solves the problem of managing access privileges and searching a substring over encrypted cloud data. Our solution is based on the distributed broadcast encryption scheme [36]. First, we present the definitions of a multi-user substring position searchable symmetric encryption scheme. Later, we give an efficient construction that combines the ideas of a single-user SSP-SSE scheme with the distributed broadcast encryption scheme.
## 3 Influencing Factors and Data Preprocessing
### Correlations among Input Features and Output
Among them, mining height (PCC: 0.86) and mining method (PCC: 0.84) had a strong positive correlation with the height of WFFZ, followed by buried depth (PCC: 0.24). the ratio of the thickness of hard rock layers and soft rock layers in the bedrock (PCC: \(-0.6\)) and working face length (PCC: \(-0.46\)) have a strong negative correlation with the height of WFFZ. Under the mining conditions of middle-deep coal seams, more hard rock layers in the overlying rock structure have a greater impact on the development height of the WFFZ. The greater the thickness of the hard rock layer in the bedrock, the smaller the height of WFFZ.
# A Query Understanding Framework for Earth Data Discovery
## 3 Data  
Two types of data are collected to train models in the proposed query understanding framework. The first, geospatial metadata, provides information that describes geospatial data in details (e.g., long name, short name, description, processing level, sensor, platform, category, term, topic, spatial/temporal resolution, start/end date of the dataset records) (Figure 1). Data publishers (e.g., Distributed Active Archive Center, DAAC, data engineers) usually provide metadata to help users find data and applications efficiently and accurately.  
The second, data portal usage logs, records a user's interaction with the Web portal. A Web usage record in combined log format consists of client IP address, request date/time, page requested, HTTP code, bytes, referrer, and user-agent (Figure 2) [28]. Collectively, they identify users, track  
Figure 1: A sample PO.DAAC metadata.  
clicking behavior, and reflect search trends [29]. Logs can also be mined to find the implicit semantic relationship among geospatial vocabularies [30].  
This research uses the Physical Oceanography Distributed Active Archive Center (PO.DAAC) metadata and the Web usage logs. PO.DAAC Metadata are harvested from the PO.DAAC website through its Web service API ([[https://podaac.jpl.nasa.gov/ws](https://podaac.jpl.nasa.gov/ws)]([https://podaac.jpl.nasa.gov/ws](https://podaac.jpl.nasa.gov/ws))) and the user logs are provided by PO.DAAC. The PO.DAAC serves the Earth science community with NASA's ocean and climate data, including measurements focused on ocean surface topography, sea surface temperature, ocean winds, sea surface salinity, gravity, ocean circulation, and sea ice. All of the publicly available collection-level metadata are harvested from the PO.DAAC Web server. The PO.DAAC metadata are in Directory Interchange Format (DIF), a standard format in NASA Earth Science Data Systems [31]. In addition, the PO.DAAC Web portal usage logs are provided for vocabulary analysis.