esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The primary concern of synthetic aperture radar (SAR) images is speckle noise, an inherent property. The creation of speckle noise is in a granular form and its nature is multiplicative. To reduce such noise from the radar images, the researchers' primary motive is to suppress granular pattern while preserving the quality of the obtained images, thereby facilitating easier feature extraction and classification. Existing speckle-noise reduction methods often fail to preserve fine details such as edges and textures. This study proposes a fusion-based method that integrates non-linear transform-based thresholding with advanced noise reduction techniques. The proposed method is implemented on two simulated SAR images at noise variance levels of \(\sigma\) = from 5 to 40. The fundamental and most significant step is to analyze the effect of granular patterns in radar images before despeckling. Different performance metrics, classified into with-reference and without-reference indexes, are considered to investigate the effectiveness of the proposed despeckle method. The Signal-to-Noise Ratio (SNR) for SAR-1 at \(\sigma\) = 20 was observed at 16.22 dB, outperforming the next best result of 12.89 dB from the Log Compression filter. The Universal Image Quality Index (UIQI) reached 0.6987, indicating high visual quality retention across various noise levels. The proposed despeckling method demonstrated superior performance in comparison to different filters, achieving a Peak Signal-to-Noise Ratio (PSNR) improvement of up to 29.37 dB on SAR-2 at a noise variance of \(\sigma\) = 5, significantly higher than the best filter method's 26.70 dB. Additionally, the method achieved a Structural Similarity Index Measure (SSIM) of 0.6538, indicating superior image quality preservation.  
speckle noise; non-linear diffusion; method-noise thresholding; despeckling methods +
Footnote †: journal: Applied Sciences
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 6 Conclusions  
In this paper, we presented a multi-loop vehicle counting method under gray mode and RGB mode. For vehicle detection, the detection operated under gray mode and RGB mode and the final detection was obtained by integrating four detection results and by optimizing using a morphological closing operation. Under this kind of algorithm, the integrity of the detected vehicles greatly improved. As for counting, the setting with multiple of loops could count vehicles by lane and could better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc.  
The experimental results showed that the proposed method performs well in videos shot in different weather at different road scenes. The proposed method successfully counted the vehicles with a high performance, e.g., the total _Recall_, _Precision_, _F-measure_, and _Accuracy_ of proposed method achieved 99.39%, 98.59%, 98.99%, and 99.19%, all of which are more than 98.5%.  
There is still much to study in future work. As analyzed in Section 5.3, when the color of the vehicle is very similar to the color of the road background, the effect of vehicle detection is not ideal. Therefore, it is necessary to find other methods to further improve the detection algorithm, such as the applications in proposed regions with objects to further improve the integrity of the vehicle, etc.  
Conceptualization, Y.C. and J.L.; methodology, Y.C.; software, Y.C.; validation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and J.L. Both authors have read and agree to the published version of the manuscript.  
Figure 23: Example of redundant counting. (**a**) Example frame under RGB mode. (**b**) Detection result set with primary loops. (**c**) Detection result set with secondary loops.  
Figure 22: Example of missed counting. (**a**) Example frame under RGB mode. (**b**) Detection result set with primary loops. (**c**) Detection result set with secondary loops.  
**Funding:** This research was funded by the Jiangsu Provincial Key Research and Development Program (Social Development) Project (BE2018754).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 4 Experiments, Results, and Ablation Study
### Experimental Settings
#### 4.1.3 Evaluation Metrics  
Based on previous works [21; 22; 23; 24], we evaluated the performance of RGB-D point cloud registration using three metrics: rotation error, translation error, and chamfer error [47]. The rotation error is reported in degrees, while the translation error is given in centimeters. Additionally, we report the chamfer distance between the predicted and ground truth alignments of the scene. For each metric, we present the mean error, median error, and accuracy at different thresholds.  
\begin{table}
\begin{tabular}{l c} \hline \hline Batch size & 2 \\ Image size & \(128\times 128\) \\ Feature dimension & 32 \\ Number of correspondence \(k\) & 200 \\ Training epochs & 4 \\ Optimizer & Adam \\ Learning rate & \(1\times 10^{-4}\) \\ Momentum & 0.9 \\ Weight decay & \(1\times 10^{-6}\) \\ \(K_{v2g},K_{g2v}\) for training & \(K_{v2g}=16,K_{g2v}=1\) \\ \(K_{v2g},K_{g2v}\) for test & \(K_{v2g}=32,K_{g2v}=1\) \\ The number of heads in self- and cross-attention & 8 \\ The number of layers in self- and cross-attention & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Implementation details of FFMT.
# A Systematic Literature Review on Penetration Testing in Networks: Future Research Directions
## 4 Results and Discussion
### Wireless Local Area Network Penetration testing  
According to the studies analyzed, the vulnerabilities in the WLAN were vulnerabilities in the IEEE 802.11 encryption protocol, namely WEP2 and WEP3. The possible attacks were the KRACK attack and Downgrade attack.
# Status of the SXFEL Facility
## 3 The SXFEL User Facility
### Energy Upgrade  
The main linac of the SXFEL-UF will accelerate the electron beam from an energy of 130 MeV at the exit of the injector to 1.5 GeV at the end of the linac. In this process, the electron bunch length will be compressed from 10 ps to about 0.7 ps. As shown in Figure 12, an additional set of S-band 50 MW RF power source in section L1, four C-band accelerating units with eight RF accelerating structures, and a second bunch compressor section (BC2) are added to SXFEL-TF in its reserved space between L2 and L3 to constitute the SXFEL-UF main linac. In this upgrade, an X-band transverse deflecting cavity is placed at the end of this linac to obtain high resolution bunch length measurement at higher energy. The designed working parameters are shown in Table 5 and the simulated beam distributions are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c} \hline
**Linac** & **Values** \\ \hline Electron energy & 1500 MeV \\ Energy spread (rms) & \(\leq\)0.1\% \\ Normalized emittance (rms) & \(\leq\)1.5 mm\(\cdot\)mrad \\ Bunch charge & 0.5 nC \\ Peak current at undulator & \(\geq\)700 A \\ Pulse repetition rate & 50 Hz \\ \hline Undulator \\ \hline Line 1 \\ \hline FEL operation mode & SASE \\ FEL output wavelength & \(\sim\)2 nm \\ FEL output pulse peak power & \(\geq\)100 MW \\ \hline Line 2 \\ \hline FEL operation mode & External seeding \\ FEL output wavelength & \(\sim\)3 nm \\ FEL output pulse peak power & \(\geq\)100 MW \\ \hline \end{tabular}
\end{table}
Table 4: Main parameters of the SXFEL-UF.  
Figure 12: Layout of the main linac of the SXFEL-UF. (Reprint from reference [26]).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Linac** & \multirow{2}{*}{**Eout (MeV)**} & \(\sigma\)**z-Out** & \multirow{2}{*}{**\(\sigma\)\(\delta\)-Out (\%)**} & \multirow{2}{*}{**E (MV/m)**} & \(\Phi\)**rf\(\backslash\)**obend** & **R56 (mm)** \\
**Components** & & & & & **(Deg)** & \\ \hline L0 & 130 & 0.86 & 0.14 & & - & - \\ L1 & 273 & 0.86 & 1.44 & 27 & \(-\)29.2 & - \\ X & 256 & 0.86 & 1.51 & 19 & 180 & - \\ BC1 & - & 0.13 & - & & 3.968 & \(-\)48 \\ L2 & 640 & 0.13 & 0.42 & 38 & 4 & - \\ BC2 & - & 0.07 & - & & 2.217 & \(-\)15 \\ L3 & 1500 & 0.07 & 0.028 & 38 & 6 & - \\ \hline \end{tabular}
\end{table}
Table 5: Designed working parameters of the main linac for SXFEL-UF.
# Recent Progress in Multiplexed Single-Photon Sources
## 5 Theoretical Results and Experiments
### Theoretical Results
The three subfigures show the maximal single-photon probability \(P_{1,N}\) (a), the corresponding values of the normalized second-order autocorrelation function \(g_{N}^{(2)}\) (b), and the optimal input mean photon number \(\lambda_{\text{opt},N}\) (c) against the number of multiplexed units \(N\). The loss parameters of the multiplexed systems for which the data are shown in the figure are the realistic ones used in Table 1. Note that \(P_{1,N}\) is maximal, and \(\lambda_{\text{opt},N}\) is optimal only for the chosen value of \(N\). Figure 5a shows that increasing the number of multiplexed units \(N\) (but still keeping it below the optimal value), where the maximal single-photon probability \(P_{1,N}\) increases in general. We point out, however, that in the case of spatially multiplexed SPSs based on OMAXVs and for temporally multiplexed SPSss based on binary-divided delays, there can be breaking points [40; 53]. It is also worth noting that single-photon probabilities higher than 0.7 can be obtained by applying four of these multiplexers in an SPS with only \(N=11\) multiplexed units, while for temporally multiplexed SPSs based on binary-divided delays, this value is higher than 0.65. This observation shows that it is possible to achieve high single-photon probabilities close to the maximal value with a considerably lower number of multiplexed units than the optimal value. According to the figure, the highest single-photon probabilities \(P_{1,N}\) can be obtained for SPSs based on stepwise-optimized binary tree multiplexers for all values of the number of multiplexed units. Figure 5b shows that an increasing number of multiplexed units \(N\) yields decreasing values of the normalized second-order autocorrelation function \(g_{N}^{(2)}\) for all multiplexer types. The lowest, that is, best values of \(g_{N}^{(2)}\) can be obtained for SPSs based on OMAXV multiplexers. Finally, Figure 5c shows that, similarly to the \(g^{(2)}\) function, the optimal input mean photon number \(\lambda_{\text{opt},N}\) decreases with increasing numbers of multiplexed units \(N\). Figure 5b,c, show that there is a correlation between the normalized second-order autocorrelation function and the input mean photon number, as we have discussed above. We note that for suboptimal system sizes, the application of different input mean photon numbers in the multiplexed units can relevantly improve the performance of the underlying SPS [52; 56]. Figure 5: _Cont._
## 1 Introduction
Thus, the characteristics of the scattering properties depend on several conditions for determining the roughness of the ocean. The paper describes a wave propagator that can be used to simulate GNSS reflected signals from ocean surfaces. The wave propagator simulates the characteristics of a bi-static scattering system, where the transmitted wave from a GNSS satellite is detected by a GNSS receiver after the wave has been reflected by the ocean surface. The theory of propagation of microwaves in the atmosphere is well established, and methods for the propagation range from ray tracing to numerical solutions to the wave equation. In addition to ray tracing, there are propagation methods that use mode  
Figure 1: The transmitted wave from the GNSS satellite (Tx) in blue is reflected at the ocean surface and received by the GNSS receiver (Rx). The red line represents the direct GNSS signal path. theory [13] and a finite difference solution to the parabolic equation [14; 15]. The presented propagator in this paper is based on the solution of the parabolic equation. The parabolic equation is solved using the split-step sine transformation. The Earth's ocean surface is modeled using an impedance model. The concept gives an accurate lower boundary condition in the determination of the electromagnetic field and makes it possible to simulate reflections and the effects of transitions between different media [16]. This paper is divided into six separate sections: Section 1 introduces the GNSS-R measurement technique and the experimental setup for the observations; Section 2 explains the observations and the GNSS-R instrument used for obtaining the data; Section 3 describes the retrieval of ocean wave heights and sea surface roughness parameters from the measurements; Section 4 gives a description of the theory needed to perform the simulations of the bistatic scattering system and the retrieved estimates of wave heights and surface roughness, followed by a discussion in Section 5; Section 6 forms the conclusions.
## 2 Materials and Methods
### Data Analysis  
All experiments were performed in triplicate and the corresponding results are presented as the mean \(\pm\) standard deviation (SD). Statistical analysis of VOC differences among the groups was conducted using one-way analysis of variance (ANOVA) with SPSS software 26 (IBM, Armonk, NY, USA), and \(p\)-values \(<\) 0.05 were considered statistically significant. Multivariate statistical analysis of the experimental data was performed using MetaboAnalyst 6.0 ([[https://www.metaboanalyst.ca/](https://www.metaboanalyst.ca/)]([https://www.metaboanalyst.ca/](https://www.metaboanalyst.ca/)), accessed on 7 September 2024) and SIMCA 14.1 (Umetrics, Umea, Sweden), focusing on Principal Component Analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to analyze the similarities and differences in VOCs across different samples. The variable importance in projection (VIP) method, derived from PLS-DA models, was utilized to identify the key aroma compounds responsible for distinguishing between different tea cultivars and the effects of drought treatments. The alterations in VOCs following drought treatment were illustrated using a volcano plot. Significant changes were defined by a \(p\)-value \(<\) 0.05 (\(t\)-test) and a fold change (FC) of \(\geq\)2 or \(\leq\)0.5. The volcano plot was generated with MetaboAnalyst 6.0. Graphical representations of the data were created using OriginPro 2021 (OriginLab, Northampton, MA, USA), MetaboAnalyst 6.0, Reporter and Gallery Plot plug (G.A.S., Dortmund, Germany), and the Biodeep cloud platform from Panomix (Suzhou, China).
## 2 Materials and Method
### Testing Scheme  
In the actual project, the burial depth of sandstone in Luohe Formation is approximately 200 m, so 2 MPa was selected as the confining pressure in this test. As shown in Figure 1, the loading test was carried out using a rock thermo-hydro-mechanical-chemical multifield coupling triaxial compression test system developed jointly by the Wuhan Institute of Geomechanics, Chinese Academy of Sciences, and Xi'an University of Science and Technology. Twelve samples in each of groups B, C, and D were selected, and the 36 samples were subjected to a single freeze-thaw cycle test. The specific scheme is shown in Table 2. As shown in Figure 1, the samples were first saturated and then placed in a freeze-thaw testing chamber. The freezing temperatures were set to \(-10\), \(-20\) and \(-30\)\({}^{\circ}\)C, respectively. After 48 h of freezing, the specimens were melted at 20 \({}^{\circ}\)C. Based on the temperature variation measured by [PERSON] et al. [7], the melting time was set to 48 h. Finally, all samples of group A and groups B, C, and D after freezing-thawing were subjected to triaxial loading.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Dry** & **Saturated** & **Longitudinal** & \multirow{2}{*}{**Porosity/\%**} & **Uniaxial** & \multirow{2}{*}{**Poisson Ratio**} & **Elastic** \\  & **Density/g cm** -3 & **Density/g cm** -3 & **Velocity/km s** -3 & & & **Modulus/GPa** \\ \hline Luohe Formation sandstone & 2.04 & 2.25 & 1.91 & 20.48 & 11.19 & 0.32 & 1.63 \\ Intact quasi-sandstone & 1.70 & 2.05 & 2.06 & 27.64 & 10.51 & 0.36 & 1.91 \\ Similar ratio & 0.83 & 0.91 & 1.07 & 1.35 & 0.94 & 1.12 & 1.17 \\ \(\theta=0\) quasi-sandstone & 1.69 & 2.02 & 2.1 & 28.52 & 9.06 & 0.31 & 1.82 \\ \(\theta=45\) quasi-sandstone & 1.6765 & 2.03 & 2.17 & 29.71 & 7.79 & 0.37 & 1.64 \\ \(\theta=75\) quasi-sandstone & 1.78 & 2.09 & 2.27 & 26.99 & 10.08 & 0.30 & 1.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameter of quasi-sandstone.
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 1 Introduction
He proposed the following formula for RC buildings based on a regression analysis of the measured data. \[T=0.018H=H/55 \tag{4}\]  
[PERSON]'s proposal was adopted in Eurocode 1 [9] with minor modifications, as follows. \[T=0.022H=H/46 \tag{5}\]  
Equation (5) can be applied to steel buildings and buildings with other structural systems, as well as RC buildings. The Australia and New Zealand standard (AS/NZS 1170) [10] and Hong Kong design code [11] adopted the empirical formula given in Equation (5) for estimation of the natural period. Research on the measured data of 137 steel buildings, 43 RC buildings, and 25 steel-reinforced concrete (SRC) buildings in Japan was conducted more recently, and the following regression formula was proposed for the wind design of RC buildings [12]:  
\[T=0.015H=H/67 \tag{6}\]Notably, Equation (5) was obtained from the data of buildings shorter than 120 m, and Equation (6) was obtained from the data of buildings shorter than 129.8 m. In Korea, [PERSON] and [PERSON] [5] measured 22 rectangular residential buildings with RC shear wall systems and heights less than 66 m and proposed the following empirical formula:  
\[T=0.0193H=H/52 \tag{7}\]  
All of these empirical formulae are summarized in Table 1. It can be seen from the table that in all formulae, the natural period is linearly proportional to the height of the building, except in KBC 2009 and ASCE 7-10.
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
## Results
### System Configuration  
Power consumption is a key feature of the proposed system, as it is designed to be used mostly on commercial farms, where animal tracking systems need to provide location data for a long time.  
Regarding the BLE tags, there is a trade-off between power consumption and network performance. When the BLE tags broadcast advertisement messages with high power, the coverage is wider, but the  
Figure 3: Hardware components of the solution: (**a**) GPS device detail; (**b**) BLE device detail; (**c**) GPS collar placed on the neck of a cow; and (**d**) BLE tag placed on the ear of a calf.  
Figure 2: Communication protocol of the proposed solution.  
operational life is shorter. The same happens with the interval between messages and with the number of repetitions per message. The power consumption of BLE tags in an idle state is 2.0 \(\upmu\)A, being 7.5 mA during transmission state, which have been configured to maximize coverage. Considering the battery size that can be used in the ear tag and the need for a long operational life, the BLE tags have been configured with a message interval of one second. This configuration provides an average operational life of 280 days (unpublished data).  
As stated, the GPS collars have two operating modes, corresponding to an idle state and an active state. During the idle state, power consumption is less than 20 \(\upmu\)A. The power consumption during the active state is due to different components. The GPS unit uses a hot-start mechanism to optimize the consumption, which exceeds 30 mA during satellite search. On the other hand, in order to send Sigfox messages to the server, the system needs 30 mA. Finally, for receiving advertisement messages from the BLE tags, the BLE module have a consumption of 15 mA. Considering all these elements, the collars have been configured to have a temporal resolution of 30 min. The battery life with this configuration exceeds 365 days (unpublished data).
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Temporal Logics  
Temporal logics are extensions of classical logics that are used to represent, and reason about, statements qualified in terms of _time_. Both propositional and predicate logic can be extended to include temporal modalities. In this dissertation, we are only interested in _Propositional Temporal Logics_ (PTLs), i.e., temporal logics obtained augmenting propositional logic with _temporal operators_. This is because they are the logics typically used to express the requirements for bit-level sequential circuits. Hereinafter, when referring to those logics, we will omit the prefix \"propositional\" for conciseness.  
Different temporal logics, with different expressiveness, can be defined based on the temporal operators they use. The two temporal logics used the most in practice are _Linear Temporal Logic_ (LTL) and _Computation Tree Logic_ (CTL). LTL adopts a _linear-time_ perspective, in which every moment in time is followed by a single successor moment. A possible behavior in LTL can, therefore, be seen as an infinite, ordered sequence of moments. CTL, instead, adopts a _branching-time_ perspective, in which every moment in time may be followed by many alternative moments representing different courses of execution. A possible behavior in CTL can, therefore, be seen as an infinite, directed tree of moments. Neither of these logics is a subset of the other, as many temporal properties can be expressed only using one of either LTL or CTL.  
The model-theoretic semantics usually given to temporal logics is defined in terms of paths and states of transition systems.
###### Abstract  
During the process of plant growth, such as during the flowering stages and fruit development, the plants need to be provided with the various minerals and nutrients to grow. Nutrient deficiency is the cause of serious diseases in plant growth, affecting crop yield. In this article, we employed artificial neural network models to recognize, classify, and predict the nutritional deficiencies occurring in tomato plants (Solanum lycopeneum L.). To classify and predict the different macronutrient deficiencies in the cropping process, this paper handles the captured images of the macronutrient deficiency. This deficiency during the fruiting and leafing phases of tomato plant are based on a deep convolutional neural network (CNN). A total of 571 images were captured with tomato leaves and fruits containing the crop species at the growth stage. Among all images, 80% (461 captured images) were used for the training dataset and 20% (110 captured images) were applied for the validation dataset. In this study, we provide an analysis of two different model architectures based on convolutional neural network for classifying and predicting the nutrient deficiency symptoms. For instance, Inception-ResNet v2 and Autoencoder with the captured images of tomato plant growth under the greenhouse conditions. Moreover, a major type of statistical structure, namely Ensemble Averaging, was applied with two aforementioned predictive models to increase the accuracy of predictive validation. Three mineral nutrients, i.e., Calcium/Ca2+, Potassium/K+, and Nitrogen/N, are considered for use in evaluating the nutrient status in the development of tomato plant with these models. The aim of this study is to predict the nutrient deficiency accurately in order to increase crop production and prevent the emergence of tomato pathology caused by lack of nutrients. The predictive performance of the three models in this paper are validated, with the accuracy rates of 87.273% and 79.091% for Inception-ResNet v2 and Autoencoder, respectively, and with 91% validity using Ensemble Averaging.  
nutrient deficiency; tomato plant; prediction; classification; deep learning; Inception-ResNet v2; Autoencoder; ensemble averaging