esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Experiments
### Simulation Experiment Conclusion  
The simulation results are shown in Figure 3.  
Figure 3: _Cont_.  
Figure 2: An \(8\times 8\) target map.  
The results show that under the same sparse transform basis, measurement matrix, and reconstruction algorithm, increasing the sampling rate reduces the phase RMSE and improves the reconstruction effect. When the TV algorithm has the same sampling rate and a different measurement matrix, the phase RMSE after reconstruction is not much different, indicating that the two measurement matrices have a considerable impact on the reconstruction effect. The reconstruction effect of the TV algorithm is better than the effect of the BP or OMP algorithm. The reconstruction effect of the BP algorithm is better than that of the OMP algorithm. The reconstruction effect using FFT sparse transform is clearly better than when using DCT sparse transform. When the sampling rate is the same and the measurement matrix is different, the Hadamard matrix is slightly better than the Bernoulli matrix.  
A comparison of the three reconstruction algorithms is shown in Figure 4 using the Hadamard matrix and FFT sparse transform.  
Figure 4 shows that the BP algorithm can reconstruct the target map more accurately at a higher sampling rate (0.8 or 0.6), but its phase RMSE is larger at a low sampling rate. The OMP algorithm cannot reconstruct the target image accurately. The phase RMSE of the TV algorithm is maintained at approximately 0.03, and the reconstruction effect is good.  
Figure 4: Comparison of the results of three reconstruction algorithms.  
Figure 3: Reconstruction results of three reconstruction algorithms, based on different measurement matrices and sparse transforms. (**a**) Basis pursuit (BP) algorithm; (**b**) orthogonal matching pursuit (OMP) algorithm; (**c**) total variation (TV) algorithm.  
The simulation results show that the reconstruction effect of the FFT sparse transform is obviously better than that of the DCT sparse transform; the reconstruction effect of the Hadamard measurement matrix is slightly better than that of the Bernoulli measurement matrix; and the TV reconstruction algorithm is slightly better than BP, with OMP producing the worst results.  
Figure 5 shows the 3D image recovered by the TV reconstruction algorithms and Hadamard matrix. When the sampling rate is 0.8 or 0.6, the 3D imaging effect is good.  
In order to further verify that this scheme is also feasible for a complex target, we increased the resolution of imaging and the complexity of the imaging target in the simulation experiments. Figure 6 shows the target with a resolution of \(16\times 16\). Figure 7 shows the reconstructed 3D image using TV reconstruction algorithms. This illustrates that the complex target can also be successfully reconstructed at a sampling rate of 0.8.  
Figure 5: Three-dimensional image reconstructed by TV reconstruction algorithms when the sampling rate is 0.8, 0.6, 0.4, and 0.2.  
Figure 6: A \(16\times 16\) target map.
# Study on Path Planning Method for Imitating the Lane-Changing Operation of Excellent Drivers
## 3 Fitting of the Test Path
By substituting the state constraint of the initial point, final point and intermediate point into Equation (4), Equation (6) can be obtained as follows:  
\[\left\{ \begin{array}{l}y_{0}=a_{n}x_{0}^{n}+\cdots+a_{5}x_{0}^{5}+a_{4}x_{0} ^{4}+a_{3}x_{0}^{3}+a_{2}x_{0}^{2}+a_{1}x_{0}\\ y_{f}=a_{n}x_{f}^{n}+\cdots+a_{5}x_{f}^{5}+a_{4}x_{f}^{4}+a_{3}x_{f}^{5}+a_{2} x_{f}^{2}+a_{1}x_{f}\\ \dot{y}(x_{0})=na_{n}x_{0}^{n-1}+\cdots+5a_{5}x_{0}^{4}+4a_{4}x_{0}^{3}+3a_{3} x_{0}^{2}+2a_{2}x_{0}+a_{1}=0\\ \dot{y}\left(x_{f}\right)=na_{n}x_{f}^{n-1}+\cdots+5a_{5}x_{f}^{4}+4a_{4}x_{f}^ {3}+3a_{3}x_{f}^{2}+2a_{2}x_{f}+a_{1}=0\\ \dot{y}(x_{0})=n\cdot(n-1)a_{n}x_{0}^{n-2}+\cdots+20a_{5}x_{0}^{3}+12a_{4}x_{0} ^{2}+6a_{3}x_{0}+2a_{2}=0\\ \ddot{y}\left(x_{f}\right)=n\cdot(n-1)a_{n}x_{f}^{n-2}+\cdots+20a_{5}x_{f}^{3} +12a_{4}x_{f}^{2}+6a_{3}x_{f}+2a_{2}=0\\ y_{m}=a_{n}x_{m}^{n}+\cdots+a_{5}x_{m}^{5}+a_{4}x_{m}^{4}+a_{3}x_{m}^{3}+a_{2} x_{m}^{2}+a_{1}x_{m}\\ \end{array} \right. \tag{6}\]  
Substituting \(x_{0}=y_{0}=0\) and \(y_{f}=\) D into Equation (6), Equation (7) is as follows:  
\[\left\{ \begin{array}{l}D=a_{n}x_{f}^{n}+\cdots+a_{5}x_{f}^{5}+a_{4}x_{f}^{4} +a_{3}x_{f}^{3}\\ 0=na_{n}x_{f}^{n-1}+\cdots+5a_{5}x_{f}^{4}+4a_{4}x_{f}^{3}+3a_{3}x_{f}^{2}\\ 0=n\cdot(n-1)a_{n}x_{f}^{n-2}+\cdots+20a_{5}x_{f}^{3}+12a_{4}x_{f}^{2}+6a_{3}x_ {f}\\ y_{m}=a_{n}x_{m}^{n}+\cdots+a_{5}x_{m}^{5}+a_{4}x_{m}^{4}+a_{3}x_{m}^{3}\\ \end{array} \right. \tag{7}\]From Equation (7), it is known that the number of polynomial coefficients need to be determined is \(n-2\), and the number of constraint equation is only4. To determine the unique expression of the lane-changing path, the equation (8) needs to be satisfied. \[n-2=4 \tag{8}\]  
Thus, \(n=6\). The expression of the lane-changing trajectory is as follows:  
\[y=a_{6}x^{6}+a_{5}x^{5}+a_{4}x^{4}+a_{3}x^{3} \tag{9}\]  
The expression of the optimal lane-changing path under different conditions can be obtained with the middle point coordinates and the final distance of lane-changing determined.
## 2 State of the Art
The analysis of reference target returns is carried out in focused SAR imagery of point-like targets [1; 16], such as radar reflectors or specifically selected distributed targets, such as water surfaces [17]. Notwithstanding the success and proven accuracy of established calibration techniques, the approach presented in the following sections may offer certain advantages in the context of multi-channel SAR systems. Firstly, conventional external calibration techniques rely heavily on focused SAR imagery. In addition to the arguably undesirable reliance on SAR processing software, the use of focused imagery may not even be an option for multi-channel systems that require analog or digital beam-forming techniques, in range or in azimuth, carried out on board or on ground, for image formation. For such instruments, image formation supposes a well-calibrated antenna array but calibration requires image formation. The present approach circumvents this circular problem, as it does not require SAR image formation and can be applied to data that are under-sampled in azimuth. Similarly, it can be applied without modification in arbitrary imaging geometries, such as circular SAR [18], or to data that is irregularly sampled in azimuth [19]. Secondly, existing approaches are geared towards verifying an existing antenna model. They do not, generally, include mechanisms to systematically work backwards from an observed mismatch between expectation and observation to a set of causes for the mismatch and the corresponding corrections. The proposed approach introduces explicit error models and derives corrections through a rigorous optimization process. In particular, it explicitly addresses uncertainty in the three dimensional antenna phase center positions to ensure the consistent inter-channel phase and geometry that are of central importance in most multi-channel SAR applications. Furthermore, the optimization approach naturally lends itself to a more detailed modeling of error sources. For instance, the proposed antenna mispointing estimate can derive three dimensional roll/pitch/yaw corrections, rather than the conventional elevation/azimuth estimates. It can also provide separate corrections for the transmit and receive channels when these involve different antennas.
# An Optimization Design of Bi-log Hybrid Antenna with Taguchi's Method for EMI Measurements
###### Abstract  
This paper presents a comprehensive design approach for a biconical log-periodic dipole array (Bi-log) hybrid antenna optimized specifically for electromagnetic interference (EMI) measurements. The antenna's elements, scaling function, feed structure, balanced-to-unbalanced (Balun) geometry, and key parameter selection are carefully considered to achieve enhanced broadband testing capabilities. The proposed compact Bi-log hybrid antenna is optimized using [PERSON]'s method within the frequency range of 30 MHz to 6 GHz. The optimization focuses on the discontinuity of the antenna factor (AF), incorporating miniaturized elements. The dimensions of the proposed antenna are minimized, with a length of 95 cm, width of 148.5 cm, height of 60 cm, and weight of 2.5 kg. Simulation results and experimental validations demonstrate its efficacy through comparison. Optimization results indicate that the voltage standing wave ratio VSWR \(<\) 2 (with 5 dB attenuator) and symmetry \(<\pm\)0.5 dB also meet the regulatory standards according to ANSI C 63.4. This makes the proposed antenna suitable for use in various types of semi-anechoic chambers.  
antenna factor; Bi-log hybrid antenna; electromagnetic interference; optimization design; Taguchi's method +
Footnote †: journal: Applied Sciences  
1
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 8 Lesson Learned  
For future implementation of BPLC to the NS-3 simulation framework, these recommendations should be used:  
* Maximum distance without repeaters.
* Simulation could be the first quick and effective tool for measurement of throughput etc.
* G.hn provides best variant of a standardized solution for BPLC technology.
* Results must be in correlation with physical parameters of cable (age of cables, ambient humidity, temperature, parameters of junctions).
## 2 Analytical Models
### Direct Models  
Before providing details on how researchers developed their equations, [PERSON]'s formula is given. Traditionally, this model was the first ever proposed. At the beginning of the previous century, [PERSON] stated that each seal passage could be treated as a single sharp-edged orifice. His theory is based on three assumptions: (a) the process is isothermal, i.e., the inlet total temperature is the same for each teeth, (b) the velocities are everywhere subsonic and (c) all the kinetic energy is converted into heat inside the seal cavity and hence, no carry-over factor is included. Under these hypotheses, the leakage mass flow rate can be calculated as:  
\[\dot{m}=K\cdot A\cdot\frac{p_{0,in}}{\sqrt{RT_{0,in}}}\cdot\sqrt{\frac{1-( \frac{p_{s,N}}{P_{0,in}})^{2}}{N-\ln\frac{p_{s,N}}{P_{0,in}}}} \tag{1}\]  
where \(A\) is the geometrical tip clearance area, \(p_{0,in}\) is the total pressure upstream the first tooth, \(T_{0,in}\) is the total temperature upstream the first tooth, \(p_{s,N}\) is the static pressure downstream the last tooth and \(N\) is the number of labyrinth teeth. In the end, \(K\) is the flow coefficient of an annular orifice which can be experimentally derived as a function of the Reynolds number and the ratio between the tip width of the tooth and the clearance size (\(w/c\)). The need of a \(K\) value has been first explained by [PERSON] [10] and is based on the idea that the actual area value \(A_{j}\) that should be used is lower than the geometrical area \(A\) and higher than the vena contracta one \(A_{m}\) (see Figure 2). The exact value corresponds to a section somewhere near the minimum one where the pressure in the air jet is equal to the cavity pressure. Since the position of this section is unknown, an empirical flow coefficient is employed to overcome this issue and fix the geometrical area value. The radicand term is often referred to as \(\beta\) and so it is described in the following for this treatment.
## 7 SensorNC Applications
### Further Applications  
There are further applications of SensorNC of which we briefly mention some here without going into further details:  
An early application and customization of SensorNC was done in the context of ZigBee cluster tree networks [11; 32]. Achieving (statistical) worst-case delay bounds despite uncertainty about the topology of a WSN has been addressed in [9; 33]. Application of the SensorNC in the case of multiple sinks in the sensor field has been demonstrated in [34]. In [35], it was investigated how traffic splitting algorithms perform in terms of worst-case performance in mesh-based WSNs. A MAC protocol has been designed to exactly match the assumptions of SensorNC in order to enable an efficient and effective real-time communication behavior in WSNs [36]. An investigation of the power management in video sensor networks has been performed using SensorNC in [37]. The issue of topology control in  
Figure 17: Flow in a general sink tree.  
combination with sink placement has been dealt with in [38] in the framework of SensorNC. Several efforts to compute energy-efficient trajectories for mobile sinks when delay guarantees also have to be met have been performed using SensorNC [39; 40; 41]. The effect of network coding on the QoS in a WSN has been investigated using SensorNC in [42]. Multimode WSNs have been analyzed using SensorNC in [43]. Industrial wireless mesh networks have been dimensioned using SensorNC in [44].
## 1 Introduction  
Although intrusion detection is an important issue to wireless sensor networks (WSNs), it is still in its infancy and there are currently only a few of studies in this area. Due to the intrinsic features of WSNs, it is difficult to perform efficient intrusion detection in such a resource-restricted environment [1]. Manyintelligent or statistical approaches are too complex for resource-constrained WSNs. In contrast, it is much easier to elude or bypass malicious nodes rather than detect them. One possible solution to such kinds of attacks is to exploit the routing functionality of WSNs. If the locations of the malicious nodes (also called compromised nodes) are known _a priori_, then sensed information can be delivered over paths that circumvent (bypass) malicious nodes, whenever possible. As the existing intrusion detection methods for WSN are still immature, in practice it is difficult to acquire such location information precisely. Therefore, the above idea of delivering information is often implemented in a probabilistic manner. Multipath routing allows the establishment of multiple paths between a single source and single destination node. It is typically proposed in order to increase the reliability of data transmission (_i.e._, fault tolerance) or to provide load balancing [2]. If the location information of compromised nodes is not known _a priori_, the source node can deliver sensed information by multiple paths to decrease the chance of the information being intercepted.  
However, there are still problems with the multipath routing approach. If adversary can selectively compromise sensor nodes, sensed information is intercepted in each fixed routing path even if it can be distributed over different routes. One possible solution to this problem is delivering information randomly through different paths rather than a fixed set of routes [3]. Although an adversary can still intercept part of the information, we can reduce the probability of interception to an acceptable extent using specific methods.  
In this paper, we propose a novel approach of secure data collection for WSNs. We explore secret sharing and multipath routing to achieve secure data collection in a WSN with compromised nodes. The remaining of the paper is organized as follows: Section 2 gives an overview of the related works. In Section 3, we present an adaptive multi-path data collection algorithm for WSNs. In Section 4, we propose a feedback-based secure path algorithm for secure data collection in WSNs. We evaluate the approach with a simulation experiment in Section 5 and discuss the simulation results in detail. Section 6 concludes the paper with an outlook to future research directions.
###### Abstract  
The topic of environmental pollution by petroleum products is highly relevant due to rapid urbanisation, including industrial development, road infrastructure and fuel distribution. Potential threat areas include refineries, fuel stations, pipelines, warehouses and transshipment bases, as well as sites affected by accidents or fuel spills. This study aimed to determine whether organic and mineral materials could mitigate the effects of diesel oil pollution on the soil's trace element content. The used materials were compost, bentonite and calcium oxide. Diesel oil pollution had the most pronounced effect on the levels of Cd, Ni, Fe and Co. The levels of the first three elements increased, while the level of Co decreased by 53%. Lower doses of diesel oil (2.5 and 5 cm\({}^{3}\) per kg of soil) induced an increase in the levels of the other trace elements, while higher doses caused a reduction, especially in Cr. All materials applied to the soil (compost, bentonite and calcium oxide) reduced the content of Ni, Cr and Fe. Compost and calcium oxide also increased Co accumulation in the soil. Bentonite had the strongest reducing effect on the Ni and Cr contents of the soil, reducing them by 42% and 53%, respectively. Meanwhile, calcium oxide had the strongest reducing effect on Fe and Co accumulation, reducing it by 12% and 31%, respectively. Inverse relationships were recorded for Cd (mainly bentonite), Pb (especially compost), Cu (mainly compost), Mn (mainly bentonite) and Zn (only compost) content in the soil. At the most contaminated site, the application of bentonite reduced the accumulation of Pb, Zn and Mn in the soil, while the application of compost reduced the accumulation of Cd. Applying various materials, particularly bentonite and compost, limits the content of certain trace elements in the soil. This has a positive impact on reducing the effect of minor diesel oil pollution on soil properties and can promote the proper growth of plant biomass.  
diesel oil; soil; materials; Cd; Pb; Cr; Ni; Zn; Cu; Fe; Mn; Co
###### Abstract  
Accurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits. Existing settlement prediction methods mainly focus on the prediction of the maximum ground settlements based on static influence factors, such as soil properties and the geometry of foundation pits. However, these methods are unable to be applied to the prediction of daily ground settlements in a direct way because daily ground settlements can be affected by many time-dependent influence factors, and an accurate prediction of daily ground settlements should take into consideration such factors. To address this problem, this paper proposes an artificial neural network-based daily ground settlement prediction method, where both static and time-dependent influence factors, as well as previous settlement monitoring data, are considered in the optimum artificial neural network. The proposed method is validated using data collected from a real cut-and-cover highway tunnel project in western Hangzhou, China. The results demonstrate that time-dependent influence factors and previous settlement monitoring data play vital roles in establishing an optimum artificial neural network for the accurate prediction of daily ground settlement.  
artificial neural network; daily ground settlement; dynamic prediction; foundation pit; settlement monitoring
# An On-Demand Fault-Tolerant Routing Strategy for Secure Key Distribution Network
## 4 Proposed Solution  
Due to the criticality of the basic services provided by the SKDN, in addition to improving its scalability, it is also necessary to consider how to handle the failure of a SKD node. Based on the traffic consistency principle [33], if the aforementioned fault occurs, it will cause inconsistency between the inflow and outflow of nodes. To this end, nodes count the acknowledgment of the communication key, determine the traffic consistency, and detect and locate the abnormal link. However, the detection can also be caused by benign faults. To prevent the direct isolation of the faulty link solely based on the acknowledgment rate, which could lead to a significant reduction in system availability, we apply DS evidence theory to identify the cause of fault through multi-information aggregation and make corresponding decisions, and propose an on-demand fault-tolerant routing strategy for SKDN.
## Abbreviations  
\begin{tabular}{l l} Abbreviation & Meaning \\
2C-ICE & Cloudsat and CALIPSO Ice Cloud Property Product \\ AATSR & Advanced Along-Track Scanning Radiometer \\ ABI & Advanced Baseline Imager \\ AHI & Advanced Himawari Imager \\ AIRS & AtmoSphericAtmospheric InfraRed Sounder \\ AMSR-E & Advanced Microwave Scanning Radiometer for EOS \\ AMSU & Advanced Microwave Sounding Unit \\ AO & AIRS-Only \\ ARM & (US Department of Energy) Atmospheric Radiation Measurement (user facility) \\ ARTS & Atmospheric Radiative Transfer Simulator \\ ATMS & Advanced Technology Microwave Sounder \\ ATOVS & Advanced TOVS \\ AVHRR & Advanced Very High Resolution Radiometer \\ AVHRR-PM & AVHRR-Post Meridiem \\ BT & Brightness Temperature \\ BTD & Brightness Temperature Differences \\ CALIOP & Cloud-Aerosol Lidar with Orthogonal Polarization \\ \end{tabular}  
+++ ==ERROR: No output for this page==
CALTPSO  
++  
IASI  
IASI-NG  
IASI-Next Generation  
ICAS  
Integrated Cloud Analysis System  
ICON-NWP  
ICOsahedral Nonhydrostatic NWP  
IPCC AR6  
Intergovernmental Panel on Climate Change--Sixth Assessment Report  
IR  
ICRCP  
International Satellite Cloud Climatology Project  
IWV  
Integrated Water Vapour  
JAIVEx  
Joint Airborne IASI Validation EXperiment  
JAXA  
Japan Aerospace Exploration Agency  
LMD  
Laboratoire de Meteorologie Dynamique (CNRS, Paris)  
LUT  
Look Up Table  
MAE  
Mean Absolute Error  
MAS  
MODIS Airbone Simulator  
MHS  
Microwave Humidity Sounder  
MIRS  
Microwave Integrated Retrieval System  
MISR  
Multi-angle Imaging SpectroRadiometer  
ML  
Machine Learning  
MLP  
MultiLayer Perceptron  
MMW  
MilliMeter-Wave scanning radiometers  
MODIS  
MODerate resolution Imaging Spectroradiometer  
MR  
Minimum Residual  
MSG  
Meteosat Second Generation  
MSPPS  
Microwave Surface and Precipitation Products System  
MVI  
Microwave Visible and Infrared  
MW  
MicroWave  
MWI  
MicroWave Imager  
NASA  
National Aeronautics and Space Administration  
NASA AST  
NASA AIRS Science Team  
NASA GPROF  
NASA Goddard Profiling Algorithm  
NCEI  
National Centers for Environmental Information  
NN  
Neural Network  
NOAA  
National Oceanic and Atmospheric Administration  
NOAA National Environmental Satellite, Data, and Information Service  
(centre) for SaTellite Applications and Research  
NUCAPS  
NOAA Unique Combined Atmospheric Processing System  
Satellite Application Facility on support to NoWCasting and Very Short  
Range Forecasting  
NWP  
Numerical Weather Prediction  
OD  
Optical Depth  
OEM  
Optimal Estimation Method  
OLR  
Outgoing Longwave Radiation  
OSSE  
Observing-System Simulation Experiments  
PATMOS-x  
Pathfinder ATMOSpheres extended  
PCRTM  
Principal Component-based Radiative Transfer Model  
POES  
Polar Operational Environmental Satellite  
PPS  
Polar Platform System  
R  
Correlation coefficient  
RF  
Random Forest  
RMS  
Root Mean Square  
RMSD  
Root Mean Square Differences  
RMSE  
Root Mean Square Error  
RTTOV  
Radiative Transfer for TOVS