esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction.  
P +
Footnote †: journal: applied sciences  
_Article_
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 1 Introduction
Moreover, another common method to generate a practical empirical algorithm is to fit a function relating the CP variables and the wind parameters. In this study, we utilize these two methods to construct the wind speed retrieval models for RV- and RH-polarization data. RADARSAT-2 is beyond its 7-year design life, as it was launched in 2007. Thus the launch of RCM is necessary, and application of CP SAR is a new application offering the advantages of full polarimetry SAR mode, with the possibility of better wide-swath coverage. Based on the simulated CP parameters, this study is a preparation for possible ocean wind retrievals from RCM which will be available in the next year. The remainder of this paper is organized as follows: Section 2 describes the database consisting of CP SAR NRCSs simulated by the RCM simulator using quad-polarized RADARSAT-2 SAR images, and collocated wind vectors observed by NCBC buoys. A new CMOD function for RV-polarization data is proposed based on a sensitivity analysis [13], followed by a performance evaluation, and we present a new model for RH polarization wind speed retrieval. Results and the validations of the two new models are shown in Section 3. Discussion and conclusions are given in Sections 4 and 5, respectively.
## 3 Results and Analysis
### Comparison and Analysis of Dynamic Characteristics  
To examine the matching, the MAC value of actual measurement and finite element calculation was solved.  
Figure 9 and Table 5 show that the first-order matching finite-element calculation of the measured modal in measurement area 1 is the 15 th order, and the first-order matching finite element calculation of the measured modal in measurement area 2 is the 20 th order. It can be considered that they correspond to each other.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement Area** & **MAC Value** & 8 & 15 & 20 & 28 \\ \hline Area 1 & 8 & 1 & 0.00059 & 0.06989 & 0 \\  & 15 & 0.00059 & 1 & 0.22992 & 0.00009 \\  & 20 & 0.06989 & 0.22992 & 1 & 0.1723 \\  & 28 & 0 & 0.00009 & 0.1723 & 1 \\  & 8 & 1 & 0.22701 & 0.00001 & 0.03524 \\ Area 2 & 15 & 0.22701 & 1 & 0.00732 & 0.00737 \\  & 20 & 0.00001 & 0.00732 & 1 & 0.01458 \\  & 28 & 0.03524 & 0.00737 & 0.01458 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Finite-element calculation of the MAC data of the first 4 main modes in the vertical direction.  
Figure 8: MAC of main vertical mode (a) Area 1; (b) Area 2.  
Figure 9: MAC histogram of measured mode and finite-element calculation mode. (a) Area 1; (b) Area 2.  
Table 6 shows that the maximal error between the theoretical and measured values of the mode shape was 7.47%, which was within a reasonable range. The measured model had higher stiffness, which may have been caused by an error between the load of the actual structure and the load of the calculation model, and there was a certain error between the measured and theoretical vertical frequencies. Weak rigid support at the outer edge of the cantilevered end of the structure had a certain impact on the dynamic characteristics of the structure (see Figure 10).
## 3 Results and Discussion
### Application of the Optimized Auxetic Sandwich Panel for Armored Fighting Vehicle Protection
#### 3.4.1 Numerical Model of an Armored Fighting Vehicle  
Figure 20 shows the FE model of an AFV subsystem subjected to an 8 kg TNT blast. This model is adapted from reference [52] based on NATO STANAG 4569 [68] and simulated to evaluate the blastworthiness of the additional protective structure on the AFV. The structural integrity of an AFV includes the floor or occupant side plate (OSP), struck side plate (SSP), lower hull, and vehicle mass, which are supported by four holders. In this case, the vehicle mass is set to 14,000 kg and modeled as a rigid part. The bottom side of the support and steel pot are fixed. The 8 kg cylindrical TNT with diameter of 270 mm and height of 90 mm is modeled using the SPH method, which was validated in Section 3.1.2. INITIAL_DETONATION is defined at the center of the charge with the SoD of 0.8 m to the floor. The materials chosen for the OSP and lower hull are high-strength martensitic steels of 1500 T, while hot-rolled (HR) steel 500 is used for the SSP. The material properties for both 1500 T and HR 500 are given in reference [52]. The thickness of the OSP and SSP are 10 mm and 6 mm, respectively.  
To assess the blastworthiness of the ASP as a protective structure, a comparative evaluation was conducted against other structures, which are an AFV without additional protective structures and an AFV equipped with the optimized AFSP. For the AFV without protective structures, simulations were performed with OSP thicknesses of 10 mm and 20 mm. The optimized AFSP configuration used in this evaluation is the optimum configuration from the optimization process conducted by [PERSON] et al. [52], while the configurations of the optimized ASP DAH are presented in Table 8.  
Figure 20: The FE model of the armored fighting vehicle (AFV) subsystem under 8 kg TNT: (**a**) side view, (**b**) front view, (**c**) detailed view of 8 kg cylindrical TNT placed on a rigid steel pot.
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
###### Abstract  
In this paper, we present the results of a comparative analysis of the sensitivity of interferograms to wavefront aberrations recorded with flat and cylindrical reference beams. Our results show that compared to classical linear interferograms based on flat wavefronts, cylindrical interferograms have at least 10% higher sensitivity for radially asymmetric types of aberrations and a 30% decrease in average absolute error for aberration recognition using a convolutional neural network. The use of cylindrical reference beams leads to an increase in the sensitivity of interferograms for detecting medium and strong aberrations.  
interferograms; structured reference beams; wavefront aberrations; convolutional neural networks; aberration recognition; optical interferometry; cylindrical wavefronts +
Footnote †: journal: Physics Letters A
## 2 Background
### Overview of APT Attacks on Mobile Sensors  
Mobile sensors are classified into three types [52]: inertial, positioning, and ambient. Figure 5 illustrates the classification of smartphone sensors. Inertial sensors on a smartphone are required to control the orientation of the user interface and detect events. Accelerometer and gyroscope sensors can be used to detect events such as device management, tilting, and dropping. Positioning sensors, such as global positioning systems (GPSs) and Wi-Fi, are essential for specifying the location of devices and transmitting information. Additionally, ambient sensors, such as microphones and cameras, must detect and analyze the user's environment, share documents, and interact with other Internet of Things (IoT) devices that utilize the same technology.  
SMS, MMS, and other telecommunication services such as calls, phone logs, and other services, including the calendar, create a constant data stream. As a result, sensor access is required for various services. Once an attacker obtains or reads these data, the user's protection may be threatened. These features depend on the user's attention [53], and the  
Figure 4: Mobile Vulnerabilities.  
Figure 5: Smartphone Sensor Classifications [12,14].  
perpetrator has the ability to execute a highly active APT attack on the mobile to exploit this dependency [12].  
Regarding to [PERSON] et al. [53], social engineering can facilitate file transmission and sharing. Among such sensors are Bluetooth connection and the Android beam. The malware also compromises APT target location, environmental sensors, and sensitive data resources. Thus, an APT assault on a mobile phone is a plausible scenario. The [PERSON] et al. [54] assault used an attachment in a year-phishing email to target a Tibetan activist.  
The GPS and Wi-Fi sensors can be compromised due to a flaw. Androrat [13] used application repackaging to target cellphones' GPS, Wi-Fi, camera, and microphone sensors in an assault that affected Turkey and the United States (US). Finally, an assault targeted Bahraini human rights advocates [14] using year-phishing emails that exploit GPS, Wi-Fi, Bluetooth, and microphone sensors [12].
## 3 Performance Evaluation
### System OP (SOP)  
Since the proposed schemes involve two sources, this paper evaluates the combined performance of both sources, referred to as system performance or SOP. Indeed, SOP is defined as the probability that one of two sources is outage. Therefore, we can express SOP of the scheme \(\text{X}\), \(\text{X}\in\{\text{Cov-Scm},\text{Mod-Scm}\}\), as follows:  
\[\text{SOP}^{\text{X}}=1-\Big{(}1-\text{OP}_{1}^{\text{X}}\Big{)}\Big{(}1- \text{OP}_{2}^{\text{X}}\Big{)}. \tag{27}\]  
In Equation (27), \(\Big{(}1-\text{OP}_{1}^{\text{X}}\Big{)}\Big{(}1-\text{OP}_{2}^{\text{X}} \Big{)}\) is the probability that both sources in the X scheme successfully recover their desired data.
## 5 Simulation  
In this section, we construct a simulation to evaluate the performance of the proposed approach. The major metric for performance evaluation is the packet interception probability (PIP) for a source node, defined as the ratio of the number of intercepted data packets to the total number of packets from the source node. To better understand the capability of the randomized multi-path routing algorithms in bypassing black holes, we also compare the performance of our approach with the original algorithms in [3]. The basic setting for the simulation is illustrated in Table 1. Here the parameter drop rate refers to the probability that a compromised node drops a data packet.
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 7 Throughput Analysis  
The throughput calculation is mainly based on two important parameters such as the information rate and system capacity. It should be assumed that \(T_{M}\) is the M-ary frame duration, \(L\) is the HPC sequence length, and each time slot has duration \(\tau\) which equals \(LT_{c}\). Therefore, for different applications \(\tau\) can be expressed as:  
\[\tau=T_{C}\;L=2T_{C}\times n(P_{1}+P_{2}) \tag{8}\]  
The throughput \(R_{HPC}\) for variable \(T_{C}\), according to the provided application by the network, can be defined as:  
\[R_{HPC}=\frac{Log(M)}{T_{M}}=\frac{Log(M)}{M\tau}=\frac{Log(M)}{2M\times n(P_{1 }+P_{2})T_{C}}=\frac{R_{C}\times\log(M)}{2M\times n(P_{1}+P_{2})}\psi_{HPC} \tag{9}\]  
where \(R_{C}=\frac{\psi_{HPC}}{T_{C}}\) is the chip rate and \(\psi_{HPC}\) is a factor that represents the probability of received bit correctly multiplied by the maximum throughput or the steady state value of the throughput. The user throughput product measures the total information rate transmitted by all users in a network. This product denoted by \(N\times R_{HPC}\), but in practice, the parameters \(M\), \(L\), and \(T_{c}\) are optimized in order to maximize the users-throughput product.
## 4 Experimental, Numerical and Empirical Comparison
### Transient-State Numerical and Experimental Comparison  
Figure 12 shows the numerical and experimental thermal flux results. The numerical FEM model had an amplitude of 5.025 W/m\({}^{2}\) while the experimental hot-box result had an amplitude of 6.225 W/m\({}^{2}\). The difference between the numerical and experimental thermal flux phases was 0.67 W/m\({}^{2}\). According to the objective function indicated in Equation (11), a difference of 0.54 W/m\({}^{2}\) was reached, representing an 8% difference with respect to the experimental thermal flux amplitude.  
Figure 13 shows the wall temperature on the indoor face (laboratory room). The average temperature obtained numerically was 23.02 \({}^{\circ}\)C and the experimental temperature was 25.1 \({}^{\circ}\)C. The temperature amplitudes of the numerical and experimental analyses were 0.201 \({}^{\circ}\)C and 0.753 \({}^{\circ}\)C, respectively. The difference between numerical and experimental temperatures was about 8%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **\(\mathbf{U}\) (W/m\({}^{2}\)K)** & **\(\mathbf{R_{tot}}\) (m\({}^{2}\)K/W)** & **\(\mathbf{I_{eq}}\) (W/mK)** \\ \hline Hot-box test & 0.690 & 1.449 & 0.256 \\ \hline Numerical FEM & 0.751 & 1.331 & 0.301 \\ \hline Empirical [2] & 0.743 & 1.345 & 0.289 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Steady-state thermal results.  
Figure 12: Comparison of thermal flux results obtained using experimental and numerical methods.  
Table 7 compares the dynamic thermal inertia parameters (time lag and decrement factor) obtained both experimentally and numerically. Empirical parameters were calculated following the ISO 13786 standard [3].  
Table 7 shows that the numerical FEM decrement factor was lower than that obtained experimentally or empirically. Numerical and experimental time lags were in good agreement, with a difference of less than 10%. However, the empirical result was lower than the results obtained using the numerical FEM and hot-box tests.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Centrality
#### 4.1.2 Closeness Centrality  
**Definition 5** (**Closeness Centrality [134]**).: _Given a graph \(G\), the closeness centrality of a vertex \(v\in V(G)\) is:_  
\[cc(v)=\frac{n-1}{\sum\limits_{u\in V(G)}dist(v,u)},\]  
_where \(dist(v,u)\) is the shortest distance between \(v\) and \(u\)._  
For the closeness centrality over static and unweighted graphs, it takes \(O(n(m+n))\) to compute the closeness centrality of each vertex by using the BFS. In weighted graphs, the time complexity is \(O(mn+n^{2}\log n)\) if [PERSON]'s algorithm with Fabonacci heap is used [135] or \(O(n^{3})\) if the Floyd-Warshall algorithm is used [136]. In dynamic graphs, [PERSON]. [44] borrows the idea of maintaining dynamic shortest paths from [137] and proposesfully dynamic algorithms for the closeness centrality computation. [PERSON]. [45] presents a framework named Streamer to parallelize the incremental closeness centrality computation on high-performance clusters. In [46], an algorithm named CENDY is proposed to update the closeness centrality when the edges are inserted/deleted but the vertices are fixed. Based on the observation that if the difference between the shortest distances from a vertex \(v\) to both end vertices of the edge to be inserted/deleted is greater than 1, then the shortest path from \(v\) to some vertices must change after an update, CENDY derives theoretical properties to quickly identify a set of vertices whose closeness centralities will change after an edge is inserted/deleted and updates the closeness centralities accordingly. Ref. [37] considers the batch update scenario to maintain the closeness centrality and shows a parallel approach to achieve the goal. Ref. [47] leverages the biconnected component and reserves key intermediate results to boost the closeness centrality maintenance.
## 4 Experimental Results
### Parallel Scenario Experiments
#### 4.3.2 Results Discussion
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **DB Index** & **Rand Index** & **Purity** & **Sensitivity** & **Precision** & **F1 Score** & **Accuracy** \\ \hline \multicolumn{8}{c}{Quantitative Clustering Algorithms} \\ K-means\({}_{1}\) & 1.75751 & 0.5522 & 66.231\% & 51.181\% & 80.745\% & 62.65\% & 66.231\% \\ K-means\({}_{2}\) & 1.14254 & 0.51785 & 59.586\% & 31.496\% & 87.432\% & 46.31\% & 59.586\% \\ K-means\({}_{\text{cosine (1)}}\) & 0.55905 & 0.58887 & 71.133\% & 72.441\% & 74.645\% & 73.526\% & 71.133\% \\ K-means\({}_{\text{cosine (2)}}\) & 0.57749 & 0.58254 & 70.37\% & 74.016\% & 72.868\% & 73.438\% & 70.37\% \\ Gmm\({}_{(1)}\) & 0.79985 & 0.63859 & 76.362\% & 83.858\% & 75.936\% & 79.701\% & 76.362\% \\ Gmm\({}_{(2)}\) & 0.98261 & 0.60623 & 73.094\% & 64.37\% & 83.206\% & 72.586\% & 73.094\% \\ \hline \multicolumn{8}{c}{Qualitative Clustering Algorithms} \\ Bmm\({}_{(1)}\) & 1.10022 & 0.69652 & 81.373\% & 84.646\% & 82.218\% & 83.414\% & 81.373\% \\ Bmm\({}_{(2)}\) & 0.63794 & 0.65883 & 78.214\% & 93.504\% & 73.988\% & 82.609\% & 78.214\% \\ Bmm\({}_{(3)}\) & 2.03007 & 0.52717 & 61.765\% & 85.827\% & 60.979\% & 71.3\% & 61.765\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluate the performance of all algorithms after the local step. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **DB Index** & **Rand Index** & **Purity** & **Sensitivity** & **Precision** & **F1 Score** & **Accuracy** \\ \hline K-means\({}_{1}\) & 1.0891 & 0.51956 & 60.022\% & 32.283\% & 87.701\% & 47.194\% & 60.022\% \\ K-means\({}_{2}\) & 1.08917 & 0.51956 & 60.022\% & 32.283\% & 87.701\% & 47.194\% & 60.022\% \\ K-means\({}_{\text{cosine (1)}}\) & 0.72586 & 0.58077 & 70.153\% & 69.685\% & 74.684\% & 72.098\% & 70.153\% \\ K-means\({}_{\text{cosine (2)}}\) & 0.72387 & 0.58077 & 70.153\% & 69.685\% & 74.684\% & 72.098\% & 70.153\% \\ Gmm\({}_{(1)}\) & 1.50585 & 0.51703 & 59.368\% & 30.315\% & 89.017\% & 45.228\% & 59.368\% \\ Gmm\({}_{(2)}\) & 1.50557 & 0.51827 & 59.695\% & 30.906\% & 89.205\% & 45.907\% & 59.695\% \\ Meta-classifier & - & - & - & 88.386\% & 85.199\% & 86.763\% & 85.076\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Evaluation of quantitative algorithms after collaboration between them. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **DB Index** & **Rand Index** & **Purity** & **Sensitivity** & **Precision** & **F1 Score** & **Accuracy** \\ \hline Bmm\({}_{(1)}\) & 1.0987 & 0.69652 & 81.373\% & 84.449\% & 82.342\% & 83.382\% & 81.373\% \\ Bmm\({}_{(2)}\) & 2.25264 & 0.69652 & 81.373\% & 84.449\% & 82.342\% & 83.382\% & 81.373\% \\ Bmm\({}_{(3)}\) & 2.84156 & 0.5522 & 66.231\% & 86.22\% & 64.602\% & 73.862\% & 66.231\% \\ Meta-classifier & - & - & - & 91.339\% & 81.547\% & 86.166\% & 83.769\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation of qualitative algorithms after collaboration between them.
# Study of Standing-Wave Thermoacoustic Electricity Generators for Low-Power Applications
## 4 Results and Discussion  
The target electric output power of the engines is \(W_{e}\) = 50 W for one-stage engines and \(W_{e}\) = 100 W for two-stage engines. All the devices are standing wave-type engines (characterized by a 90\({}^{\circ}\) out-of-phase relationship between pressure and velocity oscillations) operated with air at atmospheric pressure. This choice meets the goal of the present study of developing low-cost TAEGs. The use of near atmospheric air, in fact, avoids the use of costly pressure vessels, eliminates the problems associated to the availability and cost of noble (or other exotic) gases, and reduces the engines' size (and associated cost) due to the relatively low sound velocity.  
The devices that are supposed to comprise the standard components are: (1) properly shaped ducts (the acoustic network/resonator) with the task of selecting the working frequency and the proper pressure-velocity time-phase relationship; (2) porous solid media (stacks) where the desired heat-sound energy conversion takes place; (3) heat exchangers/HXs (a hot heat exchanger/HHX and an ambient heat exchanger/AHX) sandwiching the stack and enabling heat communication with external heat sources and sinks; and (4) standard LAs converting acoustic power to electric power.  
Specifically, the modeled stacks are commercially available ceramic substrates for automotive applications of the square pore-type with hydraulic radius equal to 0.2425 mm, porosity of 88.1%, and diameter of 13 cm. The HXs are of the fin-and-tube type with fin spacing, 2y0, equal to 1 mm and porosity 59% to avoid much too specific manufacturing requisites and relative high costs. The HHX temperature (\(T_{H}\)) is assumed at a maximum to be equal to 500 \({}^{\circ}\)C to not exceed the tolerances of typical materials used for its construction. The AHX temperature (\(T_{A}\)) is assumed to be maintained at \(T_{A}\) = 295 K and to be made of copper. A commercially available LS (the B&C 6 PS38 woofer manufactured by B&C speakers) is selected as an LA due to its relatively low mechanical resistance \(R_{m}\), low electrical resistance \(R_{e}\), high force factor \(Bl\), high \(\eta_{max}\) parameter (=72.5%) and relatively small dimension (7-inch nominal diameter). The parameters of the LS are listed in Table 1.