esa-sceva/satcom-chunk-collection huggingface dataset:
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 4 CFS for Baseline Decorrelation Compensation
### Design Principle of CFS  
According to the principle of baseline decorrelation, a carrier frequency shift (CFS) can be introduced in between two transmitted signals, in order to shift back the non-overlapped part of the received spectrum as much as possible, and eventually compensate for the severe baseline decorrelation [18]. The CFS method in this paper is also named as cross-interferometry, first verified by the ERS-ENVISAT dataset, however, the dataset cannot be used to verify OST altimetry since the minimum interferometry time-lag is 28 min, during which the signals are no longer correlated due to time decorrelation.  
Figure 7: Schematic diagram of the principle of baseline decorrelation.  
The along-track baseline length of LB-IRA is 40 m at most in case of severe time decorrelation, however, the dual antennas both send pulses for interferometry, and therefore, the received signals may suffer from radio interference with each other. In order to prevent radio interference, the CFS and bandwidth should be compatible with each other. As shown in Figure 9, the CFS is set to be \(\Delta f\) based on Equation (12) to compensate for the baseline decorrelation. The bandwidth of the transmitted pulses is \(W_{b}\), the bandwidth should be smaller than the CFS, so that the transmitted spectrum windows will not overlap with each other, as shown in the upper left corner of Figure 9. The received signal spectrum of both antennas are exactly the same, after being demodulated with the carrier frequency of \(f_{0}\) and \(f_{0}+\Delta f\), respectively. Therefore, only the corresponding part of the spectrum can be demodulated correctly as a baseband signal. After bandpass filtering, the signal spectrum overlaps with each other very well owing to CFS, besides, the radio interference is avoided since the band-pass filter is designed according to the signal bandwidth.  
Cross-interferometry or CFS compensation is easy to implement since the carrier frequency and bandwidth are programmable by Field-Programmable Gate Array (FPGA) nowadays [30]. The critical part is how to properly design the CFS to achieve both centimetric accuracy and kilometer resolution. According to Equation (12), the CFS is determined by several key parameters of LB-IRA, most of which are correlated or mutually restricted with each other. Therefore, the optimal design principle of CFS  
Figure 8: Frequency shift versus radar look angle.  
Figure 9: Proper design of carrier frequency shift (CFS) and signal bandwidth to avoid radio interference. The \({}^{*}\) along with the circle with a multiplication sign in it, stands for complex multiplication, which is a standard operation for SAR signal processing.  
should be addressed for LB-IRA. The following focuses on the design principle of CFS, in terms of radar look angle, ocean surface slope, and the cross-track baseline length.
## 5 Conclusions  
The proposed methodology highlights the dynamic convergence of topographic and underwater photogrammetry, showing the interaction between two distinct environments. Employing a UAV equipped with a high-resolution camera and a snorkeler equipped using an underwater camera, a comprehensive georeferenced 3D model is generated, spanning both terrestrial and aquatic landscapes. The challenges encountered in this methodology include: (1) addressing underwater image distortion; (2) synchronizing time-based data between land and underwater cameras, and (3) ensuring the correct relative orientation of underwater images. The success of this methodology depends on the seamless integration of multiple components, including precise UAV flight path planning, innovative underwater data acquisition methods, and meticulous image processing techniques. Despite the complex nature of underwater environments in terms of measurements, the achievement of accurate and geo-referenced results clearly shows the robustness of the approach. Through the generation of orthorectified images and georeferenced models, this methodology demonstrates its accuracy and underlines its applicability within diverse interdisciplinary contexts. By providing a detailed account of the challenges and solutions encountered, this study contributes to the evolving field of geospatial analysis and modeling. Moreover, the development of streamlined tools and applications for computing the relative position and orientation of images, alongside data extraction, highlights the potential for future advancements in underwater photogrammetry. In this regard, this methodology expands the horizons of spatial analysis by bridging the gap between aerial and submerged perspectives. As technology evolves, this research paves the way for further innovation in geospatial methodologies, enabling comprehensive landscape assessment across both terrestrial and aquatic domains. The integration of aerial and underwater photogrammetry highlights the multidisciplinary potential of geospatial science and unlocks new avenues of exploration and discovery in both spatial domains.  
Conceptualization, A.A.K. and D.S.; methodology, A.A.K., I.F. and D.S.; data collection: A.A.K.; analysis and interpretation of results: A.A.K., I.F. and D.S.; draft manuscript preparation: A.A.K.; review and editing, A.A.K., B.F., I.F. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We would like to thank [PERSON] for performing the UAV flight and [PERSON] for the underwater photogrammetric survey.  
The authors declare no conflicts of interest.
## 2 Numerical Model and Parameters
### Turbulence Modeling and Algorithms  
Realizable \(k\)-\(\varepsilon\) is chosen for the turbulence model. The solar greenhouse, classified as a low building, resides within the near-surface layer of the atmospheric boundary layer--a region characterized by substantial turbulence and a high Reynolds number. This turbulent region is typically modeled using a two-equation approach. The standard \(k\)-\(\varepsilon\) model, however, tends to be inaccurate when simulating flow fields over curved surfaces such as solar greenhouses. In contrast, the realizable \(k\)-\(\varepsilon\) model better aligns with turbulence physics, particularly under conditions of large time-averaged strain rates. The second-orderwindward format is used for the discrete format of the convective terms, while the Couple's algorithm is used for the solution of the coupled pressure-velocity equations [24].  
In the near-wall region, the influence of molecular viscosity surpasses that of turbulent pulsation, necessitating the introduction of a wall function to adjust the realizable \(k\)-\(\varepsilon\) model. This function connects physical quantities such as velocity and pressure at the wall to those in the turbulent core region. To account for the pressure gradient effect, this study employs a Non-Equilibrium Wall Function, enhancing the simulation of fluid separation and reattachment phenomena in the ridge and edge regions of the solar greenhouse.
# Study on Share Rate of Support Structure for Super-Large Span Twin Tunnels with Small Interval
## 2 Methodology
### Daling Tunnel  
Daling Tunnel, located in the southeast of Jinan City, is a twin tunnel with a super-large span cross section, which was monitored in this research. This twin tunnel contains 8 lanes (4 lanes each) with a design speed of 80 km/h, which has great capacity in transportation. The height of the twin tunnel is 12.9 m and the span is 19.4 m. Additionally, the net distance between the twin tunnel is 12.6 m.  
Daling Tunnel was excavated by the drill-blast method and supported by the composite lining, which consisted of primary support and secondary support. Furthermore, the whole construction process of this tunnel adhered to the principles of NATM. In terms of the composite lining, the primary support included C25 shotcrete, welded-wire fabric reinforcement, steel arches, and hollow rock bolts (used for grouting and reinforcing). The secondary lining is made mainly of the cast-in-place reinforced concrete (C30). Furthermore, the advance support mainly consists of supporting sheds and small grouting pipes. The information of Daling Tunnel is shown in Figure 2.  
The cross section of Daling Tunnel we measured and monitored is Level-V surrounding rock [1], which is partly soft and cracked. The related physical parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Unit weight, \(\gamma\) (kN/m\({}^{3}\)) & 20 \\ Simplified internal friction angle, \(\varphi_{c}\) & 45\({}^{\circ}\) \\ Real internal friction angle, \(\varphi\) & 22\({}^{\circ}\) \\ Friction angle of the sliding plane, \(\theta\) & 13.2\({}^{\circ}\) (0.6 \(\times\)\(\varphi\)) \\ Span, \(\beta\) (m) & 19.4 \\ Height, \(H_{l}\) (m) & 12.9 \\ Net interval, \(D\) (m) & 12.6 \\ Buried depth, \(H\) (m) & 20 \\ \hline \end{tabular}
\end{table}
Table 1: The related parameters of Daling Tunnel.  
Figure 2: Information of Daling Tunnel. For optimal traffic capacity, these twin tunnels have super-large span and small interval. The main body of them are both composite lining and bolts.
## 5 Results and Discussion  
WIFFT is an effective approach to obtain harmonic frequencies. However, the hardest issue is to analyze the envelope parameters for an envelope signal. The simulation and experiment analysis show that the improved method based on WIFFT and DE algorithm can improve calculation precision of the envelope parameters greatly for an oblique envelope signal. Compared with the envelope parameters estimation method (based on Formula (7) and (8)), the relative error of envelope parameter \(a^{*}\) decreases from 30.2041% to 0.0176% and the relative error of \(b^{*}\) decreases from 16.9166% to 7.1951 \(\times\) 10\({}^{-4}\)%. Besides, the simulation results indicate that the improved method has good adaptability to noise.  
This paper mainly studies the parameter identification of the oblique envelope signal. It only required some simple modifications for the prediction formula of envelope parameters when this improved method is employed in the analysis of other types of envelope signal. Furthermore, an actual current signal not only contains the oblique envelope part, but also other kinds of envelopes, that is, this kind of signal is in fact the piecewise envelope signal. In order to analyze it precisely, the change point detection method of piecewise envelope signals should be investigated, which is an important follow-up work.
# Analysis of Measured Temperature Field of Unpaved Steel Box Girder
## 4 Temperature Gradient Curve
### Temperature Gradient in Various Specifications
#### 4.2.1 Positive Temperature Difference Curve  
The positive temperature difference curves of the temperature gradients in various specifications are mostly multi-segment broken lines, and the exponential curves in TB 10092-2017 also have prominent nonlinear characteristics in the vertical direction. Therefore, we chose the exponential curve form and the multi-segment broken line form in GB/T 51234-2017 to fit and analyze the measured data to determine the temperature gradient curve form suitable for this study. The most negative vertical temperature gradients in a year usually occur in the summer when the solar radiation is the strongest. The maximum measured temperature gradient data on 4 July, a typically sunny day, were selected for fitting and analysis with the above two curve forms. The temperature gradient curve should take the zero point at the bottom plate and the maximum temperature difference at the top plate. Below, we discuss the effect of fitting with different values of the parameter a.  
As shown in Figure 9, when a = 10, the curve basically satisfies the requirement that the temperature difference data at the bottom plate are the zero point of the temperature gradient curve, but the data of the four positions of the fitted curve web are very different from the measured data. The smaller the value of a, the more significant the offset of the backplane data to the zero point. When a = 6, the web position data fit well, but the zero offset is large. Therefore, using the exponential curve to fit the temperature gradient of the unpaved box girder is not suitable. The fitting effect of the multi-segment polyline temperature gradient model in GB/T 51234-2017, and the measured data of the box girder, are shown in Figure 10. The maximum difference between the measured value and the multi-segment polyline model is 1.05 \({}^{\circ}\)C. Therefore, the multi-segment polyline curve model has a better fitting effect on the measured data of the unpaved steel box girder.
## 2 Methodology
### Instrumentation  
The equipment used in this experiment consisted of portable Tongdy sensors (Figure 3) that simultaneously recorded temperature (T), relative humidity (RH) as well as concentrations of carbon dioxide (CO\({}_{2}\)) and total volatile organic compounds (TVOC). The CO\({}_{2}\) sensor ranged from 0 to 2000 ppm, with an accuracy of \(\pm\)40 ppm at 25 \({}^{\circ}\)C, while the TVOC sensor ranged from 1 to 30 ppm, with an accuracy of 1 ppm. In addition, the measuring ranges for T and RH were 0 to 50 \({}^{\circ}\)C and 0 to 95% (non-condensing). All parameters were recorded on a 24 h basis at 15 min intervals and at least 1 m from the ground. Quality assurance for the equipment was performed on several occasions during the experiment, and all of the instruments were calibrated according to the manufacturers' standards.  
It should also be mentioned that statistical differences between the groups of variables were tested for significance with the use of the Kruskal-Wallis-H test (for non-parametric data). The analysis showed that all \(p\)-values were \(<\)0.05, allowing us to reject the null hypothesis of no significant difference between the ranks of the grouping variables.  
Figure 2: Selected experimental points within the museum. From left to right: ground floor, 1 st floor, 2 nd floor.
## 1 Introduction
Another option could be to execute the service in the less-loaded node in order to reduce the processing latency [9; 10]. An alternative strategy could be to select the executor according to the service requirements [7; 8], e.g., compute-intensive services could be deployed in more powerful nodes. With the decisions taken in a distributed way by nodes with limited and time-varying capabilities, implementing even the simplest decision criterion is not trivial; thus, the impact on the final performance could be huge. In this paper, we explore the NDN fog computing paradigm in a smart environment scenario, with a special focus on the deployment of service orchestration routines. We consider an edge network domain deployed in a smart campus, where a set of NDN IoT devices are installed that produces environmental data, e.g., temperature, humidity, and luminance values from various sensors, and images and videos taken from cameras. NDN fog nodes can retrieve and process IoT data according to the requests they receive from consumers. In this context, the main original contributions of this work can be summarized as follows:  
* We extend the NDN architecture to support service provisioning in the campus network and propose a new forwarding strategy that supports the decision of whether the IoT data processing has to be performed into fog nodes or remotely in the cloud in a fully distributed manner. Hence, the strategy is aligned with the fog computing concept. * We conceive a service executor strategy, leveraging a bidding procedure that aims at minimizing the overall service provisioning time, accounting for the retrieval of input IoT data to be processed and for the execution of this processing. * We implement the devised strategy in ndnSIM [11] and compare its performance through valuable metrics against a set of benchmark orchestration mechanisms available in the literature. The evaluation shows that our proposal outperforms the alternative benchmarking schemes by better scaling as the number of service requests increases. The achieved improvements come at the expense of a slightly higher amount of traffic exchanged among fog nodes. The rest of the paper is organized as follows. Section 2 provides an overview of the NDN paradigm, notably its suitability for IoT content retrieval in smart environments. The potential of NDN as a key enabler of fog computing is discussed in Section 3, whereas the relevant literature is discussed in Section 4. The proposed framework is presented in Section 5 and evaluated in Section 6. Finally, conclusions are presented in Section 7.
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 6 Performance Evaluation  
In this section, we present simulation experiments to evaluate the proposed MADDPG-based algorithm's performance, which is employed to solve the SSASGEC problem. Considering the Wide-Area Situational Awareness technique, the edge agent gathers information on external environments, internal environments, and possible attacks of power terminals as situational elements. Specifically, this includes traffic on ports, alarm events in logs, vulnerable nodes, and known attacks. We have constructed an edge computing-based grid testbed that connects different categories of power terminals to each edge agent, typically smart meters, microgrid central controllers, relay protection devices, charging piles, etc. The port traffic information and log information of the power terminals are actively uploaded to the edge agent. The edge agent monitors the vulnerability information and the attack information of the power terminal nodes in real time. Then, the situational information is aggregated to the edge agent and quantified as the original training dataset of MADDPG. During the simulation, we assume a certain quantity of ports and alarm events, a topological network composed of power terminal nodes under intelligent edge agent, and a certain number of various types of known attacks. By changing the quantified values of the situational elements that the edge agent gathered to simulate various threats and attack behaviors. What's more, in an environmental state where a massive number of heterogeneous power terminals provide diverse situational elements, we trained the MADDPG based on the proposed algorithm during the training phase. The trained model is then used for performing tests in a new situational awareness environment to verify its performance.  
In the following, we first demonstrate the proposed algorithm's convergence performance and compare it with the single-agent deep deterministic policy gradient (DDPG) algorithm. Then, we verify the effect of detection error rate on the awareness results.
## 5 Results and Discussion
### Instantaneous Throughput
#### 5.1.3 NTRUEncrypt-Secured Network  
The malicious network is protected by NTRUEncrypt-secured routing in this case. The NTRUEncrypt-secured network checks the system's behavior with a secured algorithm in the presence of the malicious node. Figure 14 shows the increase in throughput even under the attacking node through the entire time frame. The network achieves 19,300 kbps at 200 s, which shows that our system remains secure under an attacking node. The slight decrease in value, compared to the healthy network, is due to the cryptographic overhead of the NTRUEncrypt algorithm.
## 1 Introduction
Its lateral vibration will generate a large dynamic load, which will aggravate the lateral vibration of the chain and seriously affect the dynamic balance of the rotary device. The drum system consists of multiple rotating shafts with the drum and housings on the shaft. Even if each drum is in equilibrium, there is often a large unbalanced state in the entire shaft when the equipment is fully installed in place. Regarding this unbalanced state, the Lagrange method is used to establish the vibration equation of the shaft system and the motion equation of the drum containing the torsional effect of the shaft system by [PERSON] et al., (2019). He indicated that the torsional stiffness of the main shaft had a greater effect on the torsional deformation of the shaft under stable conditions [22]. [PERSON] et al., (2018) found that coupling had a greater impact on dynamic bearing forces through substructure methods and generalized finite element methods [23]. Aiming at the unbalanced vibration problem of the spindle drum device during high-speed cutting, [PERSON] et al., (2017) proposed a method for controlling the optimal influence coefficient of multi-node unbalanced vibration of flexible electric spindle [24]. [PERSON] et al., (2019) proposed an innovative dynamic model for vibration analysis of flexible drum bearings [25]. At present, there are many researches on the balance method of multi-drum series shafting, especially methods such as mode balance method and influence coefficient method are optimized and developed relatively mature [26; 27]. For the multi-drums parallel type shafting system, the balancing method needs to be changed according to the change of the transmission mode. There are fewer balancing methods for the multi-drums parallel type shafting of the application chain transmission. However, multiple threshing drums on the combine harvester were assembled by chain transmission. In this paper, the double drum chain drive model of rice crawler combine harvester was selected as the research object. The response equation of the lateral vibration of the chain drive was established. The influence of random weighting on the unbalance of the driven drum and the unbalance effect of the driven drum on the active drum through the chain drive were studied. The equivalent unbalance of the chain drive and a multi-drum dynamic balance model were developed. For the multi-stage transmission characteristics of the combine harvester, a step-by-step balanced grading chain drive double drum dynamic balancing method was proposed and practiced.
# Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing
## 1 Introduction
The restricted isometry property (RIP) is analyzed, and the relationship between observed values and signals is derived and demonstrated. 2. The reconstruction process is divided into three stages, including the large step size stage, small step size stage, and support set update stage. Different step sizes are used in these stages. 3. A backtracking threshold operation is proposed, which adopts a composite strategy and uses dedicated parameters to control the different step sizes in the reconstruction process. 4. The proposed algorithm can achieve satisfactory reconstruction performance and overcome the sensitivity to step size.