esa-sceva/satcom-chunk-collection huggingface dataset:
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 3 System Model  
### Structural Diagram  
This paper proposes an OFDM-based PLC polar coding system, and the structure diagram is shown in Figure 2. A random binary bit sequence \(u\) is input into the polar encoding module, and the encoded sequence \(x\) is interleaved with bits and passed to the modulation mapper, where a high-order modulation scheme that combines phase  
Figure 1: CA-SCL Decoding Algorithm.  
and amplitude, such as 16 PSK, 16 QAM, and 16 APSK, is used to improve bandwidth transmission efficiency. The subcarrier modulated signal sequence \(S\) is generated according to the modulation scheme for OFDM system modulation and then converted into \(N\) parallel subcarrier modulation signals with a speed of \(1/T_{s}\) (\(T_{s}\) is the signal duration) through serial-to-parallel conversion. These signals are represented as \(S_{0}\sim S_{N-1}\). The parallel subcarrier modulation signals are then subjected to an inverse discrete Fourier transform (IFFT) to obtain \(N\) time-domain sampling signals, represented as \(s_{0}\sim s_{N-1}\). Here, the signal of the \(n\) OFDM modulation at the \(k\) IFFT output sampling point in the time domain is represented as \(s_{n,k}\), which can be expressed as:  
\[s_{n,k}=\frac{1}{\sqrt{N}}{\sum_{k=0}^{N-1}S_{N,k}}e^{\frac{|2m\mathrm{rk}}{N}} \tag{9}\]  
After windowing, adding a cyclic prefix (CP), and digital-to-analog conversion, the continuous OFDM modulation signal \(s^{\prime}\) is generated and transmitted to the PLC channel. The continuous OFDM modulation signal is generated and transmitted to the PLC channel, represented as \(s^{\prime}=F_{M}\widetilde{S}\). Here, \(\widetilde{S}\) is a \(M\times 1\) zero-padding signal vector, and \(F_{M}\) is the IFFT matrix, represented as \(\frac{1}{\sqrt{M}}e^{\frac{2m\mathrm{rk}}{M}}\), \(\{m=0,1,\ldots,M-1\}\). At the receiver, the received signal \(r\) is usually disrupted by additive Gaussian white noise \(a\) and impulse noise \(i\), which is represented as \(r=HF_{M}\widetilde{S}+a+i\), where \(H\) is the cyclic matrix of the multipath channel. Then, a similar OFDM demodulation is performed according to the transmitting end to realize the fast Fourier transform (FFT), and the symbol sequence is obtained:  
\[R_{k}=\frac{1}{\sqrt{N}}{\sum_{n=0}^{N-1}r_{n}e^{\frac{|2m\mathrm{rk}}{N}}} \tag{10}\]  
Finally, the output data is obtained after the inverse operations corresponding to the structure of the transmitter.  
Figure 2: PLC polarity encoding system based on OFDM.
## 4 Results
### Contribution of Spectral Responsivity Errors  
The errors on every junction spectral responsivity measurements affect only the SMR in which the junction is involved in; as an example, errors on the top junction do not affect \(\mathrm{SMR}_{23}\). Moreover, the two affected SMR are equal, because in this sensitivity analysis all the contributions are considered separately. For each measured spectrum, the function \(u_{C}(\Phi(\lambda))\) associated to the considered junction has been multiplied by a coefficient w (\(0.2\leq\mathrm{w}\leq 2\)), to study the sensitivity of the output errors varying the absolute values of the input errors. The case w = 1 describes the case in which the ESTI declared uncertainty are used in the simulation.  
This approach gives not only a value of the error of the output quantity given the actual input errors, but also how sensitive it is to them: The more the output quantity is sensitive to input variations, the more important it is that the input errors must be precisely estimated. The following figures show the results of the three junctions individually considered.  
The top junction is considered in Figure 4: it can be seen that even doubling the declared uncertainty on the top junction the standard deviation of \(\mathrm{SMR}_{12}\) and \(\mathrm{SMR}_{13}\) remain below 0.10%. The case w = 1 leads to a standard deviation of 0.05%.  
For the middle junction (Figure 5) the standard deviation is 0.015% in the special case w = 1, and \(\mathrm{SMR}_{12}\) and \(\mathrm{SMR}_{23}\) are less dependent upon the errors on the middle junction spectral responsivity (the slope is lower). Finally, the errors on the bottom junction result in a standard deviation of 0.04% for the case w = 1 (Figure 6).  
Comparing all the junction slopes in Figures 3-5, it may be seen that the top junction is the most sensitive, and therefore the uncertainty of its spectral response has to be most carefully estimated and lowered.  
Figure 4: Impact of the errors on top junction spectral responsivity on \(\mathrm{SMR}_{12}\) and \(\mathrm{SMR}_{13}\) (k = 1). The slope of the functions indicates how much the output error is sensitive to an accurate estimation of the input errors on the top junction spectral response.
## 2 Problem Formulation
### System Descriptions
\tag{1}\]  
The inertia matrix parameters are given by \(m_{11i}=0.25{b_{i}}^{-2}{r_{i}}^{2}(m_{i}{b_{i}}^{2}+I_{i})+I_{ui}\), \(m_{12i}=0.25{b_{i}}^{-2}{r_{i}}^{2}(m_{i}{b_{i}}^{2}-I_{i})\), \(m_{i}=m_{ci}+2m_{oi}\), \(I_{i}=m_{ci}a_{i}^{2}+2m_{oi}{b_{i}}^{2}+I_{ci}+2I_{mi}\) and \(c_{i}=0.5{b_{i}}^{-1}{r_{i}}^{2}m_{ci}a_{i}\), where \(m_{ci}\) and \(m_{oi}\) are the body and wheel (with motor) masses; \(a_{i}\) is the distance between the wheel axis center and robot mass center; and \(I_{ci}\), \(I_{oi}\), and \(I_{mi}\) are the moment of inertia about the vertical axis, the wheel about the wheel axis, and the wheel about the wheel diameter, respectively. Figure 1: The structure of the L-F model. From system (1), the dynamic equations can be defined by  
\[\left\{ \begin{array}{l}\dot{v}_{i}=f_{vi}(v_{i})+\overline{m}_{1i}\tau_{vi}\\ \dot{\omega}_{i}=f_{\omega i}(\omega_{i})+\overline{m}_{2i}\tau_{\omega i}\end{array} \right. \tag{2}\]  
where \(f_{vi}\) and \(f_{\omega i}\) are unknown nonlinear functions associated with \(v_{i}\) and \(\omega_{i}\), \(\overline{m}_{1i}=\frac{r_{i}}{2(m_{1i}+m_{1i2})}\), and \(\overline{m}_{2i}=\frac{r_{i}}{2b_{i}(m_{1i}-m_{1i2})}\). \(\overline{m}_{1i}\) and \(\overline{m}_{2i}\) are positive parameters. The dynamic of the leader NMR is given by [27]  
\[\left\{ \begin{array}{l}\dot{x}_{0}=v_{0}\cos(\psi_{0})\\ \dot{y}_{0}=v_{0}\sin(\psi_{0})\\ \dot{\psi}_{0}=\omega_{0}\end{array} \right.\]  
where \((x_{0},y_{0})\) denotes the position of leader NMR, and \(\psi_{0}\) is the heading angle. \(v_{0}\) and \(\omega_{0}\) represent linear velocity and angular velocity, respectively. **Assumption 1** ([21]). : _The acceleration \(\dot{v}_{0}\) is bounded such that \(\left|\dot{v}_{0}\right|\leq v_{0}^{*}\), with \(v_{0}^{*}\) being constant._  
_From (1), we have_  
\[\dot{x}_{i}\sin\psi_{i}-\dot{y}_{i}\cos\psi_{i}=0\]  
_which means the angle between the driving wheel's axis and the robot's speed direction is 90 degrees. In other words, the robot's axle-direction speed is always zero, thus it is also called nonholonomic constraint. The nonholonomic constraint ensures the robot's wheels roll without slipping._
# Improvement of Boeing Bump Method Considering Aircraft Vibration Superposition Effect
## 4 Testing Scenarios  
### Criteria for Four Vibration Index Values  
In this study, dynamic load coefficient (DLC) is defined as the ratio of the vertical dynamic load of an aircraft to its weight, it is also referred as dynamic load factor. Overload coefficient (or overload factor) is defined as the ratio of the excessive vertical dynamic load of an aircraft to its weight (i.e., overload coefficient equals to DLC minus one), it is also referred as overload factor. The effects of runway roughness on the aircraft, pilot(s), and passengers should be examined simultaneously. The runway roughness evaluation criteria are dependent on the threshold values of the dynamic load coefficient and the acceleration.  
United States Military Standard MIL-A-8866 analyzed the probability of aircraft dynamic load coefficient during landing. One landing consists of different phases such as touching down, turning, taxiing, and braking [20]. Table 5 shows the number of times per thousand runway landings that dynamic load coefficient (\(1\pm\Delta n_{y}\)) is experienced. It was observed that most of time during landing, the dynamic load coefficient is smaller than \(1\pm 0.15\).  
Therefore, the peak value of the aircraft dynamic response shown in Table 6 is taken as the basis for modifying the Boeing Bump standard.
## 4 Real Ship Experimental Verification
### Experimental System Layout and Parameter Setting
#### 4.2.2 Experimental Measurement  
Before initiating the test, the relevant parameters for the strain gauges and experimental equipment should be pre-set in the DHDAS Dynamic Signal Acquisition and Analysis System, as shown in Table 3, detailing the parameter settings for all measurement points and ensuring that each instrument is wirelessly connected to the laptop. During the ship's navigation experiment and data collection process, the ship's diesel engine continuously outputs at the rated power without the load of the shaft generator connected. The experimental collection revolves primarily around speeds near the rated speed, mainly because in actual ship navigation, the main engine mainly operates at the rated speed and power condition. Here, the focus is on analyzing the speed points near the rated speed. The DHDAS Dynamic Signal Acquisition and Analysis System records in detail the torque amplitude and stress curves at test speeds of 73 rpm, 75 rpm, 77 rpm, 79 rpm, and 80 rpm. When the shaft generator is connected to the load, the load is 3207 kW, and the propulsion shafting system's speed is based on the actual sea conditions. The DHDAS Dynamic Signal Acquisition and Analysis System records in detail the torque amplitude and stress curves at the test speed of 53 rpm.
## 1 Introduction
We introduced a new spatiotemporal multi-cast and dynamic CH role rotation technique, which is capable of adjusting the floated FNs due to water current movements. While the drifted FNs during the transmission phase can request the new CHs for conveying their data to the SS. 2. We proposed a novel redundancy control cluster-based approach to eliminate the data-similarity through some statistical tests from the application-specific UANs. The attractiveness of the proposed model is that the RH and CH control the data-similarity between the regions and clusters, respectively. This two-level data-redundancy ensures that only the original data flow toward the final sink to save the overall network resources. 3. In AUV-based schemes [9; 24; 25], the optimal routes for AUV are not defined and AUV stays a long time on DCPs for data gathering which introduces latency in data-gathering and operational costs. On the other hand, in the proposed model the defined routes for the MS are optimal and it stays at DCPs for a specific time to collect information which does not cause any transmission delay. The rest of the paper is organized as follows: Section 2 illustrates the related work and motivations. The preliminaries are defined in Section 3. We discussed the network architecture of our proposed model in detail in Section 4. Evaluation measures and simulation results for the lifetime of the network,the packet delivery rate, and the average end-to-end delay are discussed in Section 5. Finally, the conclusion is drawn in Section 6.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 1 Introduction
The specific contributions of this paper are as follows:  
* A novel grasp detection network, ODGNet, is proposed, which can output the object's corresponding grasp configuration by inputting the RGB-D image;
* The ODGNet firstly introduces ODConv to enhance the feature extraction ability of the grasping region; secondly, a GREF module is designed, which enhances the feature learning ability of the grasping region and suppresses the non-grasping region features; finally, the multi-scale features are used to improve the grasp detection accuracy;
* The ODGNet was trained and tested on the Cornell dataset and also tested on the multi-object dataset. The test results show that it delivers good detection results for both single and multiple objects;
* Robot grasping experiments were carried out in a simulation environment, and the results demonstrate the feasibility and effectiveness of the proposed method. The remainder of this article is organized as follows: Section 2 presents related work on robot grasp detection, omni-dimensional dynamic convolution, and attention mechanisms; Section 3 describes the grasp detection problem and the proposed method; Section 4 provides detailed experimental validation; and Section 5 summarizes the article and looks at future research directions.
## 1 Introduction
Whereas research on the general characteristics of the middle-latitudinal band structure has been investigated [3; 13], whether the middle-latitudinal band structure includes irregularities and the related characteristics of the irregularities are not clear. The topside ionospheric irregularities at low latitudes are thought to be the origination of the vertical extension from the bottom ionospheric irregularities. However, recent studies indicate that the topside ionospheric irregularities present particular variations [14; 15; 16; 17]. As a polar-orbiting satellite constellation, Swarm had gathered long-term in situ electron density data along the satellite tracks and the topside total electron content (TEC) data for more than 8 years up to now, which provides an opportunity to study the climatological features of global ionospheric irregularities and the corresponding ionospheric scintillation at different local times, latitudes, longitudes, and solar activity levels. In this work, we directly compare the occurrence of irregularities detected by in situ electron density with GNSS signal scintillation based on the platform of Swarm satellites. This study aims to verify whether the occurrence of phase scintillation in the topside ionosphere is caused by in situ irregularities and to further study the differences between the distribution of irregularities and scintillations in the topside ionosphere.
## 4 Overview of Our Approach  
_VMGuards_ is a PVM-based software protection system. Like traditional PVM-based protection schemes, the VM is used to virtualize a single application. Our goal is to protect the VM that is a key component of any PVM-based scheme from runtime code tampering so as to alter the behavior of the VM and the protected software. This is accomplished by introducing two new sets of virtual instructions, TPI and ADI, to prevent unauthorized internal modification of the VM and to detect any tampering performed in the host environment. VMData is the key secret of the scheduling mechanism. It determines the order in which these handlers run, but it is completely different each time. During the running process, _VMGuards_ encrypt and decrypt the VMData with the same key in order to reduce the costs of the system. In addition, every protected software version has a unique fixed key in advance.  
Figure 1 depicts the overall approach of _VMGuards_. In the figure, K_C is the key code segment (e.g., the key algorithm of the authorization information) to be protected. During static compilation, the segment will be filled up with some junk or useless code. A new section N_C will be created to store the generated virtual instructions for K_C. wnen K_C is executed, the control will be given to N_C and the virtual instructions will be dynamically translated into native code by a VM.  
This paper holds that all PE files can be protected in principle, so the legitimacy of the PE file inspection is not elaborated. The concrete construction of _VMGuards_ proceeds as follows:  
1. Locate the range of the critical code segment (start and end addresses) of the PE file to be protected and disassemble the binary code of the key code segment.
2. Design a set of intermediate instruction sets, virtual instruction sets (VIs), and transform these disassembled instructions into virtual instructions.
3. Construct atomic function sets(Hdls), and generate handlers by the corresponding virtual instructions.
4. Use the appropriate encryption algorithm to encrypt handlers generated by Step 3 and organize it into VMData.
5. The design of reasonable Dispatcher.
6. According to the order of execution, VMData, Dispatcher and Hdls make up the VM Interpreter, as shown in Figure 1, and it is represented in the form of a new section on the protected PE file.
7. The original key code segments fill with some junk or useless code and add the correct jump instruction, and then output the protected PE file.  
The specific execution flow within the VM Interpreter must first use Dispatcher to decrypt the first used Handler, while the latter execution is that the Dispatcher selects VMData and decrypts it to get the required Handler, or jumps directly from the previous Handler executed to the address of the next Handler to be executed based on the offset address. The following paper details the five parts of _VMGuards_.
## 4 Results
### Validation of SBAS-InSAR Monitoring Results  
To verify the monitoring results of surface subsidence obtained through the SBAS-InSAR technique, we conducted a field investigation in the Gaojaliang mining field, focusing on mining-induced surface subsidence. The field investigation revealed that the primary geological disasters in the Gaojaliang mining field are ground fractures, which are represented by bright purple lines in Figure 9a. Statistical analysis of these identified ground fractures (Table 5) shows that there are 29 ground fractures located in the three panels of the Gaojaliang mining field; specifically, five ground fractures are distributed in panel 301, 12 ground fractures are distributed in panel 401, and 12 ground fractures are distributed in panel 203. Furthermore, the orientation of the majority of the fractures aligns with the tangential orientation of the subsidence margins in which they occur.  
Figure 8: Cumulative subsidence time series curves of feature points in the Wanli mining area.  
Figure 9: Results of field validation. (**a**) Locations of ground fractures in the Gaojaliang mining field; (**b**) field observations of typical ground fractures; (**c-e**) are three-dimensional models of subsidence and ground fractures for panel 301, panel 203, and panel 401.  
Figure 9b shows a typical ground fracture, captured directly above by an unmanned aerial vehicle (UAV). To provide a more intuitive understanding of the distribution of these 29 ground fractures across the three panels, we created three-dimensional models of cumulative subsidence and fracture locations, illustrated in Figure 9c-e. The results indicate that the subsidence areas closely align with the boundaries of goats in the Gaojialiang mining field, where the ground fractures are concentrated.
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 4 Results
### MODIS Observations
Later in the month, it was possible to identify a few peaks in TET-1 and MODIS data, but the activity stagnated at _TADR_ of 0.4 m\({}^{3}\)/s. Such evolution is in line with the classical magma discharge models with rapid waxing and slowly waning flow [62, 63]. MODIS data are available for the whole eruption, thus we can estimate the total volume of the discharged lava. The mean output rate is 0.87 m\({}^{3}\)/s, which is more than the estimates for previous eruptions [5]. If we multiply this value by the duration of the eruption (6 August-13 November; 99 days), the total value of the erupted material equals 7.4 \(\times\) 10\({}^{6}\) m\({}^{3}\). Considering that it is highly likely that the MODIS data still contain some data affected by the clouds, this is a lower estimate for the total erupted volume. Figure 8: TET-1 data shows that _TADR_ is well correlated to _VRP_, allowing a linear fit of the radiant density also for MODIS.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 4 Performance Metrics
### Sensitivity to Channel Uncertainty at the Transmitter  
In practice, the CSI is imperfect due to noise, quantization, calibration errors, etc. We do not attempt to model these effects exactly. Instead, we adopt a standard approach based on MMSE estimation and provide the precoder with channel matrices \(\mathbf{\hat{H}}[\tau]\) that satisfy  
\[\mathbf{H}[\tau]=\sqrt{1-\varepsilon^{2}}\mathbf{\hat{H}}[\tau]+\varepsilon\mathbf{Z}[\tau] \tag{23}\]  
where \(0\leq\varepsilon\leq 1\) and \(\mathbf{Z}[\tau]\) is a \(K\times N\) matrix of independent, variance \(\sigma_{h}^{2}=1/L\), complex, circularly-symmetric Gaussian entries. Note that \(\varepsilon=0\) corresponds to perfect CSI and \(\varepsilon=1\) corresponds to no CSI. The precoder treats \(\mathbf{\hat{H}}[\tau]\) as the true channel realization for \(\tau=0,\ldots,L-1\).
## 4 Conclusions  
This work introduced a novel framework for EM-based size reduction of antenna structures. Our methodology enables global search with an initial knowledge-based pre-screening of the design space and approximating the geometry of the feasible region boundary using supplementary optimization processes, both executed at the low-fidelity (thus computationally cheaper) level of evaluating antenna responses. Furthermore, it allows for exploring the boundary region using dimensionality-reduced surrogate modeling methods. The reliability of the procedure is secured by executing the last stages (surrogate model identification and final parameter tuning) at the high-fidelity EM simulation level. The presented technique is comprehensively validated using four microstrip antenna structures and benchmarked against several algorithms, including both local (gradient-based) routines and a representative nature-inspired algorithm (PSO). The results corroborate the globalized search capability of the proposed procedure and its superior overall performance. On the one hand, it consistently ensures improved miniaturization rates over the benchmark. The obtained designs are up to fifty percent smaller with a twenty-five percent average. Further, it provides good repeatability of solutions, as indicated by low standard deviations of the antenna sizes, which are from six to as much as twenty-four times smaller than for the benchmark procedures, with an average of twelve. On the other hand, owing to the implemented mechanisms, especially variable-resolution models and dimensionality reduction, the proposed approach retains relatively low computational cost. The savings with respect to the PSO algorithm are about thirty percent, with the latter yielding designs of considerably larger footprints, even twice as larger. Our future work will be aimed at further development of this methodology, especially in terms of ensuring a more extensive exploration of the feasible region boundary, as well as applying it to the various classes of antenna and microwave components.  
Conceptualization, S.K. and A.P.-D.; methodology, S.K. and A.P.-D.; software, S.K., A.P.-D. and L.G.; validation, S.K. and A.P.-D.; formal analysis, S.K.; investigation, S.K. and A.P.-D.; resources, S.K.; data curation, S.K., A.P.-D. and L.G.; writing--original draft preparation, S.K. and A.P.-D.; writing--review and editing, S.K.; visualization, S.K., A.P.-D. and L.G.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Icelandic Centre for Research (RANNIS), Grant 239858, and by the National Science Centre of Poland, Grant 2020/37/B/ST7/01448.  
Not applicable.  
The authors thank Dassault Systemes, France, for making CST Microwave Studio available.  
The authors declare no conflict of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of this manuscript, or in the decision to publish the results.