esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Equations
### The Tank Model  
The time-dependent variables of the tank model are (see Figure 2, where the tank is referred to as a Receiver Vessel):  
* Gas pressure, \(p_{r}\);
* Gas temperature, \(T_{r}\);
* Mass of gas, \(m_{r}\);
* Cylinder wall temperature, \(T_{w}\).  
Some hypotheses have been made to simplify the equations characterizing this element:  
* The potential energy linked to any difference in altitude between the storage terminal and the tank is neglected;
* The kinetic energy of the gas inside the tank is neglected;
* The pressure and temperature of the gas inside the tank does not depend on the spatial coordinates (perfect mixing hypothesis);
* The mass flow rate is calculated, in the case of free loading/unloading, as an isenthalpic expansion through an orifice while, if the compressors is used, it is taken from the machine characteristic curve, once the compression ratio is known;
* The heat exchange from the control volume of the tank to the wall is described by a convective heat flow toward the wall, which is considered a concentrated mass: this implies that the cylinder wall temperature does not depend on the spatial coordinates but assumes a uniform average value. The equations needed to calculate the values of the tank model variables are:
* Conservation of mass: \[\frac{dm_{\rm r}}{\rm dt}=\ \dot{\rm m}_{\rm i}\] (1)
* Energy balance: \[\frac{\rm d(m_{\rm r}\cdot u_{\rm r})}{\rm dt}=\dot{\rm m}_{\rm i}\left(h_{i}+ \frac{{\rm v_{i}}^{2}}{2}\right)_{\rm i}+\frac{\rm dQ_{\rm r}}{\rm dt}\] (2)  
Figure 2: Scheme representing the variables of the receiving tank.  
* Mass flow rate: (a) Free loading cases: (b) Forced loading cases: centrifugal compressor curves (see Section 4.4)
* Cylinder wall temperature: (c) (d) Forced loading cases: centrifugal compressor curves (see Section 4.4)
* Cylinder wall temperature: (d) (e) (f)  
where assuming that natural convection is dominant, the internal heat flux of the vessel is:  
(6)  
and the external heat flux between the vessel surface and the atmosphere is:  
(7)  
The internal convective heat exchange coefficient, \(\alpha_{\rm i}\), is obtained by the value calculated for the Nusselt number, while for the external one, it is assumed \(\alpha_{\rm e}=4.5\) W/m\({}^{2}\)/K [17].  
The equations have been rearranged in order to calculate at each time step the mass of CNG and the temperature in the tank. Then, after the calculation of the gas density as the ratio between the mass and the tank internal volume, the pressure and all the other thermodynamic properties of interest are calculated by means of Coolprop.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 3 Methodology
### Digital Twin-Based Generation of Integrated Dataset for Pavement Distress
#### 3.1.1 On-Site Data Collection Based on UAV  
The quadrotor UAV (Phantom4 RTK) manufactured by the DJI company is used to capture the asphalt pavement-distress images, and a controllable (pitch angle: \(-90^{\circ}\) to \(+30^{\circ}\)) three-axis head is installed on the bottom of the UAV to hold a high- precision camera for image collection (shown in Figure 3). The selected section of road is a two-way four-lane asphalt road. The optimal flight altitude of the UAV is tested before deployment to ensure the flight stability and area clearance requirements while providing complete coverage of a two-way four-lane road area. In addition, the accuracy of the construction of the digital orthophoto image (pavement background model) is ensured by setting a reasonable flight speed to control the overlap rate of two consecutive images: that is, the minimum overlap rate should be above 80% for frontal overlap and above 75% for lateral overlap, respectively. The parameter setting of the UAV are shown in Table 1.
## 3 Empirical Research
### Data Sources and Processing  
#### 3.2.1 Data Sources  
Since the latest data is currently for 2022, some variables have been lagged. Therefore, this paper takes 30 Chinese provinces (Hong Kong, Macao, Taiwan, and Tibet are not included in this study due to data reasons) as the research object in 2016-2019 to analyze the innovation efficiency and change trend of the high-tech manufacturing industry in 30 provinces in China. The original data are from the _China Science and Technology Statistical Yearbook_ (2017-2023) and the _China High-Tech Industry Statistical Yearbook_ (2017 and 2019-2023).  
In 2018, officials did not release the Statistical Yearbook of China's High-Tech Manufacturing Industry; for the missing data related to the high-tech manufacturing industry of individual provinces in 2017, this paper collects and organizes them through the official websites of the regions. For the uncollected data, this paper adopts the linear interpolation method to supplement the missing values (the \"annual average number of employees\" data of each province in 2017).
# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
###### Abstract  
Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromechanical industries. In particular, the 3D television (3 DTV) and free-viewpoint television (FTV) enhance viewers' television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available views and their depth maps using depth-image-based rendering (DIBR) techniques. The quality of a DIBR-synthesized image may be compromised for several reasons, e.g., inaccurate depth estimation. Since depth is important in this application, inaccuracies in depth maps lead to different textural and structural distortions that degrade the quality of the generated image and result in a poor quality of experience (QoE). Therefore, quality assessment DIBR-generated images are essential to guarantee an appreciative QoE. This paper aims at estimating the quality of DIBR-synthesized images and proposes a novel 3D objective image quality metric. The proposed algorithm aims to measure both textural and structural distortions in the DIBR image by exploiting the contrast sensitivity and the Hausdorff distance, respectively. The two measures are combined to estimate an overall quality score. The experimental evaluations performed on the benchmark MCL-3D dataset show that the proposed metric is reliable and accurate, and performs better than existing 2D and 3D quality assessment metrics.  
3D image quality assessment; objective quality metric; view synthesis; 3D television +
Footnote †: journal: Applied  
Article Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 5 Conclusions  
Theoretical analyses of the carrier phase recovery in long-haul high-speed \(n\)-PSK (\(n\)-level phase shift keying) coherent optical fiber communication systems, using the one-tap normalized least-mean-square (LMS), block-wise average, and Viterbi-Viterbi algorithms, have been investigated and described in detail, considering both the laser phase noise and the equalization enhanced phase noise. The expressions for the estimated carrier phase in these three algorithms have been presented, and the bit-error-rate (BER) performance such as the BER floors, has been predicted analytically. Comparative studies of the one-tap normalized LMS, block-wise average, and Viterbi-Viterbi algorithms have also been carried out. It has been found that the Viterbi-Viterbi carrier phase recovery  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**One-Tap Normalized LMS** & **Block-Wise Average** & **Viterbi-Viterbi** \\ \hline
5 & \(n\) & \(n\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complexity of carrier phase recovery approaches (complex multiplications per symbol).  
algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms in the carrier phase recovery for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS carrier phase recovery algorithm is more sensitive to the level of modulation formats than the other two algorithms.  
The BER floors in this paper were discussed and analyzed based on the influence from laser phase noise and equalization enhanced phase noise in the long-haul \(n\)-PSK transmission systems, and this represents the system limits from laser phase noise and equalization enhanced phase noise. In addition, signal degradation from fiber nonlinearities is also a significant effect in such communication systems. Therefore, the actual BER floors will be determined by involving the impacts from amplified spontaneous emission (ASE) noise, laser phase noise, equalization enhanced phase noise and fiber nonlinearities, which will be investigated in our future work.  
This work is supported in part by UK Engineering and Physical Sciences Research Council (project UNLOC EP/J017582/1), in part by European Commission Research Council FP7-PEOPLE-2012-IAPP (project GRIFRON, No. 324391), in part by European Commission Research Council FP7-PEOPLE-2013-ITN (project ICONE, No. 608099), and in part by Swedish Research Council Vetenskapsradet (No. 0379801).  
[PERSON] presented the basic idea and carried out the analytical calculations and discussions. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to developing the research ideas and were involved in the discussion of results. [PERSON] wrote the main manuscript and prepared the figures. All authors reviewed the manuscript and gave the final approval for publication.  
The authors declare no conflict of interest.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 2 Related Works
### Cryptography-Based Techniques  
The authors of [26] proposed an architecture based on blockchain technology, where multiple UAVs collaborate to detect GNSS spoofing attacks. Each UAV performs its designated functions, including sharing its location data on the blockchain. These data are later compared with the UAV's position obtained via cooperative positioning to identify any suspected spoofing attempts. However, this approach requires UAVs to be equipped with a set of radio direction finding (RDF) transmitters and receivers. In addition, the computational overhead introduced by the blockchain infrastructure remains a significant challenge. Ref. [27] proposed a semi-decentralized UAV architecture to prevent GPS spoofing. A leader UAV is elected by the UAV network community through a consensus process for a predefined time period. The leader obtains its GPS position, encrypts it, and broad casts it to the entire UAV network. The remaining UAVs then decrypt the leader's position and compute their own positions relative to it.
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
###### Abstract  
Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method.  
GNSS-based passive bistatic radar; extensive cancellation algorithm (ECA); long-time integration Fourier transform (LIFT) Article
###### Abstract  
To address the challenges of low accuracy in coal gangue image recognition and poor segmentation performance under the influence of dust in underground coal mines, a scaled simulation platform was constructed to replicate the longwall top coal caving face. This platform utilized real coal gangue particles as the raw material and employed dust simulation to mimic the dust conditions typically found in coal mines. Images of coal gangue without dust and under varying dust concentrations were then collected for analysis. In parallel, an improved DeeplabV3+ coal gangue image segmentation model is proposed, where ResNeSt is employed as the backbone network of DeeplabV3+, thereby enhancing the model's capability to extract features of both coal and gangue. Furthermore, two channel attention modules (ECAs) are incorporated to augment the model's ability to recognize edge features in coal gangue images. A class-label smoothing training strategy was adopted for model training. The experimental results indicate that, compared to the original DeepLabV3+ model, the optimized model achieves improvements of 3.14%, 4.70%, and 3.83% in average accuracy, mean intersection over union (mIoU), and mean pixel accuracy, respectively. Furthermore, the number of parameters was reduced from 44.18 M to 43.86 M, the floating-point operations decreased by 8.33%, and the frames per second (FPS) increased by 45.03%. When compared to other models such as UNet, PSANet, and SegFormer, the proposed model demonstrates superior performance in coal gangue segmentation, accuracy, and parameter efficiency. A method combining dark channel prior and Gaussian weighting was employed for defogging coal gangue images under varying dust concentration conditions. The recognition performance of the coal gangue images before and after defogging was assessed across different dust concentrations. The model's segmentation accuracy and practical applicability were validated through defogging and segmentation of both indoor and underground dust images. The recognition accuracy of coal and gangue, before and after defogging, improved by 6.8-71.8% and 5.8-45.8%, respectively, as the dust concentration increased, thereby demonstrating the model's effectiveness in coal gangue image defogging segmentation in underground dust environments.  
coal gangue recognition; image segmentation; image defogging; DeepLabV3+, fully mechanized top coal caving face
## 5 Numerical Simulation of Pressure Loads on Assembled Road Surfaces Underground  
### Modular pavement Mechanics Model  
Assembled road surfaces are typically composed of structural layers including precast block layers, sand cushion layers, base layers, and subgrades (see Figure 10). This study investigates this type of pavement structure using field measurements and laboratory test results obtained from the 431,304 working face of the Liangshuijing coal mine, located in Shenmu City, Shaanxi Province. The mechanical parameters of the tunnel floor are summarized in Table 21.  
Article 4.3.8 of the National Standard of the People's Republic of China for Design of Factory and Mine Roads (GB) 22-87) [27] indicates that the method for calculating the deflection of concrete in China is not yet mature, and the calculation of deflection is carried out by designing an equivalent uniform body. Using the WC55E model--the heaviest-duty bucket-type transport vehicle at the Liangshuijing coal mine--as an example, the vehicle has a self-weight of 35,000 kg. It has a maximum load capacity of 50,000 kg, and its dimensions are 10,050 mm (length) \(\times\) 3550 mm (width) \(\times\) 1950 mm (height). Each of its four tires has a ground contact width of 50 cm. The load-bearing area is approximated as a square with a side length of 50 cm. When fully loaded, the WC55E vehicle exerts a maximum load of 850 kN on the road surface. Under the most unfavorable conditions, the corresponding ground pressure is calculated to be 0.85 MPa. Given the specific conditions at the Liangshuijing coal mine, the pavement design in this study adopts road deflection as the primary design criterion. The design deflection value is calculated using the following formula:  
\[l_{d}=6N_{e}^{-0.2}A_{c}A_{s}A_{b}\]  
In the formula, \(l_{d}\)--Design deflection value, calculated as 0.83 mm; \(N_{e}\)--Cumulative equivalent standard axle load application frequency within the design service life, 50,000 times; \(A_{c}\)--Highway grade coefficient, with underground traffic volume being lower than highway surface traffic volume, set to 1.2; \(A_{s}\)--Surface layer type coefficient, with the surface layer set to 1.0; \(A_{b}\)--Base layer type coefficient, with the subgrade considered as a semi-rigid base layer and set to 1.0.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
###### Abstract  
In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.  
dynamic clustering; cluster head selection; IoT for agriculture; UAVs for agriculture +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Oil-Water Separation Performance of MFA@PDMS Sponge  
#### 3.3.1 Separation Performance for Immiscible Oil/Water  
In order to verify the oil-water separation capability of the MFA@PDMS sponge, an experiment was designed (see Figure 7). The MFA@PDMS sponge was placed in a normal hexane aqueous solution containing Sudan red II dye. The hexane solution was then moved to an oily area using a magnet. It was observed that the red normal hexane was quickly absorbed by the sponge (the red circles correspond to the locations of the red hexane), with the oil-bearing water quickly becoming transparent, indicating that that material has excellent oil-water separation capability (the separation efficiency can reach more than 95%), can effectively and quickly remove n-hexane (oil phase) from water, and can be remotely controlled via an external magnetic field, thereby facilitating the separation, recovery and transportation of oils.  
According to the experimental process shown in Figure 8, the adsorption times of pure PDMS, FA@PDMS, Fe\({}_{3}\)O\({}_{4}\)@PDMS and MFA@PDMS sponges for n-hexane were tested; the results are shown in Figure 8. As indicated, the adsorption multiple of the MFA@PDMS sponge was the highest, reaching six times of its own weight, indicating that the addition of MFA significantly improved the oil-water separation effect of the PDMS.  
Figure 6: Hydrophobic Properties of MFA@PDMS sponge.  
Figure 7: Oil–water separation using the MFA @ PDMS sponge.
## 4 Results
### PPI Network Construction and Hub Genes Selection  
Further investigation was done to gain insight into our DEGs list by exploring them using a PPI network. For this purpose, a PPI network was built using the STRING database and Cytoscape application, with an input of 540 DEGs. The first-order network created an extensive network comprising 538 nodes and 691 edges. Because there were too many nodes, the first-order network was too large to be visualized and we needed to focus only on the important nodes. Therefore, a zero-order PPI network was built to get a clear network and obtain the most significant nodes. This network has 56 nodes with the highest connection (degree of 13). Cyclin-dependent kinase 1 (_CDKI_), Cyclin A2 (_CCNA2_), and Cyclin B1 (_CCNB1_) were the up-regulated genes with the highest node degrees (see Figures 4-6). These genes are essential for controlling the cell cycle [38; 39; 40]. Moreover, the maximal clique centrality (MCC), maximum neighborhood component (MCN), and degree methods were used to select the hub genes from the PPI network using the CytoHubba plugin with default parameters in Cytoscape. The top 10 genes with the highest MNC, MCC, and node degree scores were identified as hub genes, as shown in Figure 7a-c, respectively. We observed the intersection from the three methods and generated a Venn plot to present the overlapped hub genes (see Figure 8). This process revealed eight hub genes (_CDK1_, _KIF11_, _CCNA2_, _TOP2A_, _ASPM_, _AURKB_, _CCNB2_, and _CENPE_).  
Figure 3: The top 40 genes that were highly enriched in the KEGG pathways.  
Figure 4: Full PPI network of the differentially expressed breast cancer genes.  
Figure 5: The PPI indicating selected nodes with at least 13 edges. Dark red indicates high degree, light red denotes low degree.  
Figure 6: PPI network presentation with red nodes indicating the up regulated genes and green nodes denoting down regulated genes. The node size indicates node degree.
## 2 Theory
### Parking Prediction and AI  
With the continuous acceleration of urbanization, the parking problem is becoming increasingly serious. Effective management of parking resources has become an urgent issue, and one solution is to enhance management and planning through parking prediction. Recent studies on parking space availability leverage deep learning techniques. For example, the work in ref. [13] employs a short-term demand prediction algorithm based on a convolutional neural network (CNN) and long short-term memory (LSTM) neural network. Similarly, ref. [14] uses LSTM alone for the prediction task. In contrast,ref. [15] incorporates a multi-head attention mechanism into an LSTM network, finding an increase in accuracy compared with using LSTM alone. The study in ref. [16] utilizes a transformer-based scheme for spatial-temporal prediction. While these methods harness the power of deep learning to make predictions, they typically require large amounts of data to achieve good generalization. This requirement can hinder their performance when adapting to specific parking lots with limited data availability. In this work, we evaluate machine learning techniques that are lightweight and generally adapt better than deep learning techniques when there are few data. Additionally, they are easier to implement in embedded systems, as they do not require extra processing power, such as GPUs, to achieve good performance.