esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Optimal NDVI Reference Period
Figure 10: Plot of the three seasons under investigation and the average of the 10 previous seasons (starting from 2008/2009) with the monthly mean temperature of the meteorological station of Conselice (Ravenna, Italy). While the NDVI-based index for the 2017/2018 season exhibits a consistently linear growth pattern, the 2019/2020 season shows this linear trend only until March, and the 2021/2022 season maintains it until April. This behavior can be partially linked to the well-known NDVI saturation effect [81], which seems to occur around values of 0.75/0.8 depending on the field's vegetation cover. Conversely, the slight decrease observed in the index from April to May 2022 suggests that part of the non-linear behavior is also due to the natural decline in vegetation vigor during that period. In Figure 12, we show the MVC maps corresponding to the best-correlated month (i.e., February) for each year under investigation. Under the hypothesis that the MVC value for this month serves as a proxy for the expected yield, these maps provide spatial information on areas likely to be more productive, as well as on the ones that may require additional management practices. The maps are also consistent with the field yields recorded in the 2018 (57 q/ha), 2020 (79 q/ha), and 2022 (65 q/ha) seasons.
## 2 Numerical Model and Methodology
The normal and tangential contact damping force [39], \(F_{n}^{cd}\) and \(F_{t}^{cd}\), are expressed as  
\[F_{n}^{cd}=-c\beta_{c}\sqrt{2m^{*}K_{n}^{c}}V_{n}^{c} \tag{8}\]  
\[F_{t}^{cd}=-\beta_{c}\sqrt{2m^{*}K_{t}^{c}}V_{t}^{c} \tag{9}\]  
where the parameter \(c\) is a constant which is equal to \(\sqrt{5/6}\) when the normal contact force \(F_{n}^{c}\) is proportional to \(\delta_{n}\)\({}^{3/2}\). The contact damping coefficient \(\beta_{c}\) determines the collisional dissipation rate, \(\beta_{c}\ =\ \frac{-\ln c}{\sqrt{n^{2}+(\ln c)^{2}}}\), in which \(e\) is the coefficient of restitution. \(m^{*}\) is equivalent mass, \(m^{*}=\left(\frac{1}{m_{1}}+\frac{1}{m_{2}}\right)^{-1}\), \(m_{1}\) and \(m_{2}\) are the masses of two contacting particles. \(K_{n}^{c}\) is normal contact stiffness and \(K_{t}^{c}\) is tangential contact stiffness, given by \(K_{n}^{c}=\frac{\mathrm{d}F_{n}^{c}}{\mathrm{d}\sigma_{n}}\) and \(K_{t}^{c}=8\mathrm{G}^{*}\mathrm{a}\), respectively [40]. \(\mathbf{V}_{n}^{c}\) and \(\mathbf{V}_{t}^{c}\) are the normal and tangential components of relative velocity at the contact point.
## 4 Design of Roll Angle Detection Module
### Parameter Optimization of Tracking Loops
(**a**) is the tracking result at \(T_{roll}=13\) ms, (**b**) is at \(T_{roll}=25\) ms.  
smoothly and facilitate the parameter settings at different frequencies, a series of fixed parameter settings in different frequency bands are set up. We divide the rotational speeds from 3 r/s to 300 r/s into four groups with division points of 4 r/s, 10 r/s and 40 r/s respectively. The integration times of the first three groups are the maximum periods of the rotational speeds in each group. For they are longer than the other periods in their own group so that the residual errors can be minimized, while the loop is used to track the signals with frequencies of their own group. The integration time of the last group (40 \(\leq\)\(f_{r}\) < 300 r/s) is greater than the longest period of their group, for a fixed integration time of 50 ms is sufficient to meet the acquirements of accuracy and real-time performance at the same time. The optimal choices of other parameters in each group are obtained from extensive simulations with theoretical guidance in the last section. The simulation results in Figures 14-16 showed the most appropriate parameter settings that have been discovered, which can satisfy the reasonable settling time and overshoot simultaneously. The overall optimal parameter settings at different frequencies of FLL-assisted PLL are given in Table 1. The corresponding performance index of the tracking loops is shown in Table 2, where the pull-in ranges of the loops are calculated with respect to a four-quadrant arctangent discriminator. With the presented parameter settings, we can use FLL-assisted PLL to track the roll modulation signals in high precision with reasonable settling time and overshoot. The real-time performance of tracking loops and the flexibility of parameter adjustments can also be ensured at the same time.
## 2 Materials and Methods
### Data Analysis and Respective Results
#### 2.4.1 Vine Location  
The geolocation of individual vines within a vineyard holds significant importance in the field of viticulture. The spatial data concerning the distribution of individual plants offer valuable assistance during vineyard replanting procedures, enabling viticulturists to establish an accurate inventory of plant count within the vineyard. This information proves instrumental in assessing the current state of the vineyard's plant population. In this study, the exact location of individual vines was derived by analyzing the 3D point cloud recorded from the mission on 8 April 2023 (Figure 3). At this time of the year, the plants are in the phenological stage EL 1-3, where only leaf tips are visible and the rest of the plant is represented by woody plant parts [18].  
In Figure 4, a portion of the 3D point cloud used for vine location detection is shown.  
Figure 4: Portion of the 3D point cloud recorded in April 2023 showing the vine in the phenological stage EL 1-3. Bird exclusion netting is placed around the trunks of the vines all year around.  
Figure 3: Methodical workflow diagram showing the analysis steps that lead from the processed data sets ready for analysis to the derived vine-related parameters: LSA, LAI, vine height as indicated by the green boxes.  
Bird exclusion netting is folded around the trunks all year around. For the task of deriving single vine locations, oblique UAS missions were performed together with nadir missions, where the camera angle was set to 45\({}^{\circ}\) and 90\({}^{\circ}\), respectively. In order to keep the computing time of the devices within the limits, a smaller part of the point cloud was used for the derivation of the individual positions of the vines. This part consisted of a series of vines with different spacing. A planar mesh surface was adapted to the 3D point cloud, which should form the base surface for the further analysis. Subsequently, the distance of the point cloud to the automatically generated mesh surface was calculated, with the maximum values of this calculation reflecting the top of the vine stems. In the scalar field of the individual vine stems, all X, Y, and Z values of the individual points were stored. The maximum Z values of the grapevine trunk clusters were used, and the X and Y coordinates of the maximum Z-values were stored, respectively.  
The exported coordinates of the maximum distance between the surface and point cloud are georeferenced in the Austrian geodetic system Lambert ETRS 1989, and they show the locations of the individual vines at heights of about 20 cm (Figure 5). Up to this distance, the vine trunks are vertical and do not show any significant slope angles.
# GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
## 4 Results and Discussion
### Morphotectonic Indices
#### 4.1.3 Hyposometric Integral Index (Hi)  
We investigated Hi values and Hi curves. The Hi integral index values were between 0.30 in sub-catchment 57 and 0.51 in sub-catchments 56, 69, 94, 97. Our hypsometric analysis showed some concave curves associated with values of 0.48 and 0.49 (results of hypsometric curves are illustrated in Figure 7). Obtaining elevation values from digital elevation models are necessary for computation of this index. The hypsometric integral values do not relate directly to relative tectonic activity [6]. The value of this index is generally affected by rock strength as well as some other parameters. High values of the Hi index generally suggest a younger landscape that may developed by active tectonic events [7] (Figure 7). High Hi values may also result from very recent incisions into a young surface resulting from deposition processes. The authors of [6,7,9] assumed that convex lower part curves may indicate uplift signals along an active fault, or uplifting signals resulting from recent folding events. They also stated that Hi values greater than 0.5 result in convex curves. Values descending from 0.5 to 0.4 indicate convex-concave or semi straight curves, and values less than 0.4 are described by concave shapes (Figure 7). The hypsometric integral index values were analyzed based on the utilization of all catchments/sub-catchments of greater than the fourth drainage network order. Results for the hypsometric index are presented on Table S1 in the Supplementary data.
## 3 Results
### Real-World Application Results
#### 3.1.4 Statistical Evaluation  
Given that graphical evaluations alone may not be sufficient, a statistical evaluation was conducted, to determine whether there was a significant difference in the mean deviation of the differences between the prediction and the true values of two time series. This approach aimed to ascertain whether one of the time series provided a better approximation of the true time series by examining if the mean difference of one time series was significantly smaller than the other. To perform the statistical analysis, the procedure of [21] was used.  
Our process involved selecting two time series for comparison, where time series one was always the OML approach and time series two was one of the remaining methods. Next, the number of samples required to make an informed decision was calculated, based on the following assumptions:  
* \(\alpha=0.05\)--probability of a Type I error, known as a false positive;
* \(\beta=0.2\)--probability of a Type II error, known as a false negative; \(1-\beta\) is known as the power of the test;
* \(\sigma=\) standard deviation of the difference of the absolute differences between model 1 and model 2;
* \(\Delta=\) twice the mean value of the absolute deviation between the actual values and the output of the OML approach.  
Instead of considering each sample value individually, we considered the overall mean of the absolute deviation between the prediction and the true value from each time series. It was determined that between 518 and 527 samples were needed, depending on the time series. As the analyzed time series comprised around 30,000 samples, obtaining the required sample size was not problematic.  
In the third step, a one-sided \(t\)-test was conducted, using the difference of the absolute values between the two time series. We formulated the following hypothesis, where \(j\) denoted the index of either the batch, landmark, or shifting window approach:  
\[H_{0}:\mu_{\text{OML}}-\mu_{j}\geq\ 0\] \[H_{1}:\mu_{\text{OML}}-\mu_{j}<0.\]  
Figure 6: The MAE, computation time, and memory consumption of different approaches for each evaluation step. The MAE plot shows an overlap of the graphs of all BML methods. In the plots for computation time and memory consumption, the curves of landmark and shifting window, as well as the OML and the classical batch method overlap.  
Based on the analysis, it was concluded that the null hypothesis could be rejected in all cases, indicating that the mean deviation of the differences of the OML approach was significantly smaller than that of all the other approaches.
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 2 System Model
### System Description and RA Procedure
#### 2.1.2 Jamming Types  
In power jamming, the jammer is assumed to send arbitrary signals or just sine-waves with full transmitting power at each slot. It causes the same level of interference to every SN in the system. On the other hand, in code jamming, the jammer is assumed to send a preamble using a randomly or specifically selected access code with the full power. It targets influencing the SNs using the same access code, but also works as interference for the other SNs. Since the code jammer works like a legitimate SN, it can hear AI from HS if PT is successful, and then, it sends a fake MSG using the same code, which further distorts the utilization of radio resources in the WSN.
# Motion Compensation for Long Integration Times and DoA Processing in Passive Radars
## 1 Introduction
The rest of the paper is structured as follows: in Section 2, a brief description of the PR operating principle is carried out; in Section 3, the main parameters of a bistatic radar scenario are detailed; in Section 4, the problem of used long integration intervals is addressed; in Section 5, the IDEPAR demonstrator is described; in Section 6, the proposed solution is explained; in Section 7, the experimental results for validation purposes are presented; and, finally, in Section 8, the conclusions are summarized.
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Impulse Noise  
#### 3.6.1 Impact of IN in NOMA  
Numerous obstacles to the adoption of NOMA systems have been raised by the broader literature on NOMA, which primarily occurs with respect to next-generation networks like the Internet of Things (IoTs) and smart grids. Analysis has also been done on the impact of IN on the NOMA downlink [89; 90] and uplink [91] systems. The NOMA uplink systems' outage performance in the occupancy of IN is given in the research work [91]. Analytical findings and comprehensive Monte-Carlo simulations were used to verify the NOMA system's sensitivity to IN. The effect of IN on the cumulative rate capacity of NOMA downlink systems was given by the authors in [90]. The real loss from IN was calculated using a particle IN scenario. The authors also examined how well NOMA performed across Rayleigh-fading channels with composite noise (impulse with AWGN). A union constraint on the bit error rate (BER) was developed using the pairwise loss of bits (LoB) formula. The research study quantified the variance in channel conditions that NOMA users encounter when there is composite noise. The operational reduction of NOMA-aided IoT networks caused by IN was examined, and a mitigation approach was suggested in [92]. For acquired OFDM symbols generated from the power domain multiple-NOMA (PDM-NOMA) strategy, a multistage nonlinear solution based on deep learning was presented.
###### Abstract  
The inertial sensor is the key measurement payload of the technology verification satellite of China's space gravitational wave detection mission-Taiji Project, which uses capacitive sensors to sense the acceleration disturbance of the test mass under the influence of non-conservative forces in the frequency range of 10 mHz-1 Hz. It is necessary to perform a ground performance evaluation and estimate the working state of the payload in orbit. However, due to the influence of the earth's gravity and seismic noise, it is impossible to directly evaluate the resolution level of the non-sensitive axis when testing with high-voltage levitation, which leads to incomplete evaluation of the performance of the inertial sensor. In order to implement this part of the test, the sensitive structure is designed and a torsion pendulum facility for performance testing is developed. The experimental results show that the measurement resolution of the non-sensitive axis of the inertial sensor can reach \(9.5\times 10^{-7}\) m/s\({}^{2}\)/Hz\({}^{1/2}\) under the existing ground environmental conditions and is mainly influenced by the seismic noise during the system measurement. If the inertial sensor enters orbit, the measurement resolution can achieve \(3.96\times 10^{-9}\) m/s\({}^{2}\)/Hz\({}^{1/2}\), which meets the requirements of the technology verification satellite for a non-sensitive axis. This proposed system also provides a reasonable method for the comprehensive evaluation of inertial sensors in the future.  
capacitive sensor; inertial sensor; measurement resolution; non-sensitive axis; sensitive structure; test mass; torsion pendulum
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 2 Related Work
### Pose Estimation of Primitive-Shaped Objects  
Recently, due to the development of the deep learning technique, 6 DoF pose estimation using Convolutional Neural Networks from a single RGB image have been well developed [1; 14]. However, as we aim to estimate the pose of visually simple (no color) objects, RGB information does not contribute to the estimation. The object pose can be also estimated from a single depth image. Moreover, the pose estimation methods based on learnable features [1; 14] or handcrafted features [4; 15] require rich textures or rich geometric keypoints. As we aim to estimate the pose of geometrically and visually simple objects, these approaches are unsuitable to employ.  
Numerous approaches of primitive shape pose estimation employed the RANSAC algorithm to estimate the pose of the primitives [16; 17; 18; 19; 20; 21; 22]. [PERSON] et al. [19] fit the parameters of plane, sphere, and cylinder to object point cloud by M-estimation SAmple and Consensus (MSAC) [23] and the final decision on the selection of the primitive shape is taken based on number of inliers during estimation. [PERSON] et al. [22] employ a local Hough Transform algorithm to estimate pre-defined geometrically primitive shapes. Most approaches define primitive shapes in each. For example, three parameters (width, height, and depth) are needed to represent a cuboid, two parameters (radius and height) are needed to represent a cylinder. As primitive-shaped objects are represented in all previous work, only limited types of primitive shapes can be handled. In contrast, our method can handle any primitive shapes as long as it can be represented by superquadrics.  
[PERSON] et al. [16] proposed a method to estimate the pose of a fixed-size cuboid for Spatial Augmented Reality. Although they handle a single type of primitive shape, they propose a method that estimates the pose of a cube sequentially, while the user interactively moves the target object. They estimate the pose parameter of cubes by efficient planar region detection using RGB-D superpixel segmentation. Their method has two main limitations: first, the size of the objects is known beforehand and, second, they only can estimate the pose and track the cuboid. Our work overcomes these two limitations.
###### Abstract  
In order to study the variation law of the flow field and pressure fluctuation in the hump section of the siphon outlet conduit, the flow field characteristics and frequency spectrum characteristics of the flow field were analyzed by combining a physical model test and numerical simulation under the conditions of the interaction between the axial flow pump and siphon outlet conduit, and the influence of the residual circulation at the outlet of the guide vane on the siphon outlet flow was investigated. Based on the influence of the flow field and hydraulic loss in the conduit, the equivalent surface method based on the \(Q\) criterion was used to analyze the vortex structure in the siphon outlet conduit and to analyze the internal vortex state. The results showed that with the increase of the flow rate, the intensity of the vortices in the cross-section of the hump section of the siphon outlet conduit decreased gradually, the average velocity circulation decreased gradually and the axial velocity distribution uniformity increased and tended to be stable; water flow stratification existed under three characteristic conditions with no circulation, and the hydraulic loss was greater with the circulation flow while it had a circulation under the small flow condition. Under the low flow rate conditions, the hydraulic loss was 6.6 times higher under the condition of circulation than without. Under a high flow condition, it was 1.3 times. Under the condition of a small flow rate, the vortex structure was distributed centrally at the inlet of the flow conduit, and under the other two characteristic conditions, the vortex structure mostly appeared as a strip; the pressure fluctuation in the hump section had obvious periodicity, and with the increase of the flow rate, the maximum pressure fluctuation amplitude in the hump section decreased gradually; with the decrease of the rotational speed, the pressure amplitude at the same measuring point in the hump section decreased gradually and at the optimum condition. Under the following conditions, the mean value of the pressure amplitude at the top of the hump section was reduced by 69.63%, and the mean value of the pressure amplitude at the bottom of the hump section was reduced by 63.5%. Under all the calculation conditions, the main frequency of pulsation at each measuring point of the hump section was twice the frequency of the rotation.  
pump device; siphon outlet conduit; internal flow field; pressure fluctuation; model test; numerical simulation +
Footnote †: journal: Applied Sciences
# Sparse Multiuser Receiver Design in Large Scale Array System
## 5 Simulation Results
### BPSK Receiver Performance  
Taking the BPSK modulation as an example, this subsection aims to discuss the BER performance versus SNR obtained by the random phase matrix method and the proposed method under the assumption that multiple users share fewer RF chains. Specifically, we set \(N\) as 4, \(M\) as 16, 32, and 64, and \(K\) as 6, 8, and 10 separately.  
In Figure 4a,b, it is observed that the two analog matrices can realize the simultaneous reception of BPSK signal transmitted by multiple users through the GAMP method at high SNR. For a fixed \(M\), the BER performance degrades as \(K\) increases. Similarly, for a fixed \(K\), with the increase in \(M\), the BER performance of the proposed method improved, while for the random phase matrix, the simulation results approximately remain unchanged. The reason is that the process based on a random phase matrix cannot afford coherent integration of user signals. In other words, when using the analog matrix to combine signals received by multiple antennas, noise accumulates, resulting in equivalently unchanged SNR characteristics. In contrast, our method manages the coherent integration of multiple users' signals. Conclusively, the proposed analog matrix can reap a performance advantage by approximately 10 dB compared with the method based on the random phase matrix.  
Figure 4: BER performance when the GAMP method is used for demodulation and multiple users are served by 4 RF chains: (**a**) for the proposed analog matrix, (**b**) for the random phase matrix.  
Figure 3: Array gain of massive array based on the proposed GAMP and the DFT-based GAMP: (**a**) on the condition that \(K=12\), (**b**) on the condition that \(K=24\).