esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The protection of information against electromagnetic penetration is one of the most important aspects related to the protection of information against its non-invasive acquisition. Compared to the activities of cybercriminals, the use of electromagnetic emissions in the electromagnetic infiltration process does not leave any traces of activity, and the owner of the information is not aware of its loss. The most common activities of electromagnetic eavesdropping are related to the infiltration of emission sources, graphically revealing the processing of information using both analog and digital methods. This allows for the presentation of reconstructed data in the form of images. Correct display of the acquired information requires knowledge of raster parameters such as line length and the number of lines building the reconstructed image. Due to the lack of direct access to the intercepted device, knowledge in this field does not allow for the correct determination of the aforementioned parameters, and thus, for recreating an image that would contain legible and understandable data. Additionally, incorrect values of the parameters result in failure of further processing of the obtained image, e.g., by using a coherent summation of images. Therefore, it is necessary to propose a solution that will allow not so much to roughly define the raster parameters but to estimate them precisely. Moreover, it should enable the automation of the process after the implementation of an appropriate algorithm. The article proposes an algorithm for estimating the line length of the reconstructed image. The raster parameter estimated with the use of the algorithm allows for summarizing images several dozen times with a significant improvement in the image quality and readability of the data contained in it. The image summation algorithm is very often used as one of the main image processing methods in the electromagnetic infiltration process. Incorrect raster parameters often make coherent summation useless. The proposed algorithm for estimating the line length of the reconstructed image uses three methods of determining the line length of the image for a given accuracy. At the same time, criteria were indicated that must be met to determine the correct length of the image line for the assumed accuracy of estimation. Obtained results confirmed that the proposed methods and criteria are effective in the process of electromagnetic infiltration. These methods allow us to determine the line length of reconstructed images with accuracy up to \(10^{-5}\).  
electromagnetic leakage information; computer security; protection of information; TEMPEST; image processing; reveal emission; VGA; HDMI; image contrast +
Footnote †: journal: Applied Sciences
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 1 Introduction
### Channel Exclusion Criteria  
Signal quality can be jeopardized by a number of factors including instrument and environment noise, poor coupling of optodes to the head, artifacts introduced by subject motion, and optical interference by coarse or heavily pigmented hair [6]. These sources of noise and artifact can result in reduced signal-to-noise-ratio (SNR), sharp spikes in the data time series, and/or abrupt shifts in baseline intensity, ultimately leading to inaccurate recordings and misinterpretations [7; 8].  
To ensure adequate signal quality, it is crucial to define a criterion for excluding poor channels that would otherwise contaminate the data. One commonly used criterion is the visual inspection of the \(\sim\)830-850 nm wavelength optical density signal for presence of cardiac pulsation, in either the time or frequency domain. The rationale being that the presence of a cardiac oscillation, which is robust and consistent, indicates that changes in optical density are coupled with physiological hemodynamic changes. An example of an acceptable channel is shown in Figure 1.  
Figure 1: Channel quality. (**A**) A 10-s segment and (**B**) the frequency power spectrum in log and (**C**) normal scale are shown for a 830 nm raw time-course (**D**, red). The typical cardiac waveform in the time domain (**A**) and the typical peak near the 1 Hz cardiac frequency (**B**,**C**) can be observed. Additionally, both wavelengths are plotted (**D**). (**E–H**) show the same figures as (**A–D**) for a channel with low signal to noise ratio.  
There are, however, several drawbacks to the visual inspection method, i.e., (1) it does not ensure that the ~700 nm wavelength signal is of sufficient quality (this wavelength is weighted to deoxyhemoglobin, and therefore cardiac pulsation is less robust), (2) it is subjective (highly dependent on evaluator), and (3) it is time-consuming, making it inefficient for large datasets. For these reasons, automated methods for channel exclusion are favorable, as long as they perform on par with or better than visual inspection. Several automated methods have been proposed to assess and identify channels for exclusion. The two automated criteria we will investigate are the widely used coefficient of variation (CV) method and the method developed by [PERSON] and colleagues [PERSON] and colleagues (2010), named PHOEBE (Placing Headgear Optodes Efficiently before Experimentation), which determines adequate SNR by automatic detection of cardiac pulsation [PERSON] and colleagues (2010).
## 3 Results
### Sensitivities of Vegetation Changes to Climate Factors Across Global Drylands
#### 3.2.2 Differences in the Sensitivity of Vegetation Growth to SW Changes at Different Depths  
Here, the study further investigated the differences in the sensitivity of LAI changes to SW at different soil depths (i.e., 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm) in global drylands during 1982-2018, as shown in Figure 5. Results showed a spatial consistency in the sensitivities of LAI changes to various soil depths of SW, i.e., 0-7 cm (Figure 5a), 7-28 cm (Figure 5b), and 28-100 cm (Figure 5c) in global drylands during 1982-2018. The high positive sensitivities of LAI changes to SW at different soil depths were primarily found in northern Australia, the eastern Brazilian Plateau, and the central Sahel region. Nevertheless, the spatial pattern in the sensitivities of LAI changes to SW at a 100-289 cm soil depth were not quite consistent with the other three soil depths (Figure 5d). It is noteworthy that, in contrast to the other three soil depths, the positive sensitivities of LAI to SW at 0-7, 7-28, and 28-100 cm tended to turn negative at 100-289 cm, in other words, the regions with negative sensitivities of LAI to SW had significantly expanded in the 100-289 cm soil depth, primarily located in  
Figure 4: Spatial patterns in the sensitivities of LAI to (**a**) rising CO\({}_{2}\) concentration, (**c**) decreasing SW, and (**e**) increasing atmospheric VPD in global drylands since the early 1980s. Their latitude differences in the sensitivities are shown in (**b**), (**d**), and (**f**), respectively. Regions labelled by black dots represent those trends that are statistically significant at a 95% significance interval (MK test, \(p<\) 0.05).  
southern Africa, southern Australia, Central Asia, and northeastern China. Statistically, LAI changes were positively sensitive to the 0-7 cm soil depth of SW in approximately 74.88% of global drylands, 80.29% at the 7-28 cm depth, 80.39% at the 28-100 cm depth, and 68.44% at the 100-289 cm soil depth, as shown in Table 2. Overall, these findings revealed that LAI changes tended to be more positively sensitive to SW at the second (7-28 cm) and third (28-100 cm) soil depths across global drylands during the period of 1982-2018.
## 2 Methods
### Quantum Proxy Signature Scheme
#### 2.3.1 Initializing Phase  
In initializing phase, [PERSON] is required to prepare the quantum carriers of the original message and all the secret keys for encryption and decryption processes are produced.  
**Step 1** [PERSON] holds a classical binary string \(M\) with \(n1\) bits, which is the original message to be signed and can be expressed as  
\[M=\{M_{1},M_{2},\ldots,M_{i},\ldots,M_{n1}\}, \tag{12}\]  
where \(M_{i}\in\{0,1\}\), \(i=1,\ldots,n1\). Then he or she encodes \(M\) into the corresponding qubit sequence \(|\varphi\rangle_{M}\) with \(n1\) qubits in the form of  
\[|\varphi\rangle_{M}=\{|\varphi_{1}\rangle_{M},|\varphi_{2}\rangle_{M},\ldots, |\varphi_{i}\rangle_{M},\ldots,|\varphi_{n1}\rangle_{M}\}, \tag{13}\]  
where \(|\varphi_{i}\rangle_{M}=\frac{1}{\sqrt{2}}(|0\rangle+m|1\rangle)\), in which \(m=1\) or \(m=-1\) corresponds to \(M_{i}=0\) or \(M_{i}=1\).  
**Step 2** [PERSON] shares secret keys \(\{K_{AT}\), \(K_{AC}\}\) with [PERSON] and [PERSON], respectively. Similarly, [PERSON] shares secret keys \(\{K_{BT}\), \(K_{CT}\}\) with [PERSON] and [PERSON]. This procedure can be completed through QKD system [10; 45; 46].
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems  
Before the research on the LEO navigation algorithm, it is necessary to analyze and model the error of INS and GNSS systems, because it plays an important role in the reasonable setting of the parameters of algorithms and influences engineering implementation.
## 4 Numerical Simulation  
### Numerical Modeling  
The diameter of the intact rock samples is 50 mm, the height is 100 mm, the minimum particle size of the rock particles is 0.4 mm, and the ratio of the maximum particle size to the minimum particle size is 1.66, as shown in Figure 4a. These particles are constrained by the four walls to be placed in a box of the same size as the internal test specimen; then, a numerical model of the intact sample can be obtained after the test of the equilibrium of the model. Furthermore, a circular contour with a hole diameter of 15 mm can be imported into the center of the intact sample by the geometry import command; then, the particles in the circular contour can be deleted by the delete command, and finally, the model containing the hole defects can be obtained by rebalancing the process, as shown in Figure 4b. In addition, a region is set at the left tip of the circular hole by using the measure function in PFC (Particle Flow Code), which can monitor the average stress of the marble particles in the region, as shown in Figure 4c.  
The micro-mechanical parameters in PFC model materials mainly include the contact between particles, and contact models are mainly linear and include contact bond, parallel bond contact, flat bond, and smooth bond models. As the contact force in the parallel bond contact model exceeds the bond strength, the parallel bond will break, and the bond and the force and moment acting on it will be removed, which is more in line with the mechanical behavior of rock; the parallel bond contact model requires an easier debugging of the microscopic parameters, which is also more widely used [30; 31]. Based on this, the numerical models of the three rock samples developed in this paper all adopt the parallel bonded contact model.  
The cement-filled specimen model is shown in Figure 5a, where the particle size range of the cement paste is 44 um to 100 um, the spatial distribution of the particles is set in a uniform and random manner, and the cement paste particles are distributed in a circular contour based on the hole-containing defect model. The cementation between the cement paste and the rock specimens is critical to the strength of the specimens. Due to the rock-like nature of the cement paste material, the cement paste and rock particles in the filling test model are still modeled with parallel bonding, as shown in Figure 5b. The bond between the grout and the rock is a parallel bond contact force, which is consistent with the overlay type in the bonding pattern at the grout-rock interface described by [PERSON] et al. [32]. The upper and lower walls of the model are used as loading tools, and the relative movement  
Figure 4: Numerical modeling of various types of specimens: (**a**) intact specimen model; (**b**) hole-containing specimen model; (**c**) monitoring area.  
rate of the upper and lower loading plates is 0.001 mm/s, which simulates the loading rate of 0.002 mm/s used in the indoor tests.
###### Abstract  
The maturity of seeds at harvest determines their intrinsic quality characteristics such as longevity and vigor, and these characteristics are dominant factors for seed quality evaluation in the seed industry. However, little information is available on how to identify and further classify the maturation stage of seeds in a way that is nondestructive, precise, rapid, and inexpensive, while also exactly meeting the need for the uniform control of seed performance in the seed industry to improve crop yield. This study demonstrated a nondestructive method for detecting seed maturity by using the single-kernel near-infrared spectroscopy (SK-NIRS) technique. The results showed that five classes of cucumber seeds with different maturation levels can be distinguished successfully. A tree-structured hierarchical classification strategy consisting of one soft independent modeling of class analogy (SIMCA) model and three partial least squares discriminant analysis (PLS-DA) models were proposed ending up with 99.69% of the overall classification accuracy and 0.9961 of [PERSON]'s kappa in the test set, and its predictive performance was superior to both SIMCA and PLS-DA for direct multiclass classification. SK-NIRS in combination with a multiclass hierarchical classification strategy was proved to be both intuitive and efficient in classifying cucumber seeds according to maturation levels.  
FT-NIR; single kernel; maturation stage; hierarchical classification; SIMCA; PLS-DA; PCA; exploratory analysis; cucumber seed +
Footnote †: journal: Applied Sciences
# A 2D-View Depth Image- and CNN-Based 3D Model Identification Method
## 2 Materials and Methods
### Dataset of SHREC'15 Non-Rigid 3D Shape Retrieval  
Since the most common modification to 3D models is pose deformation, we tested our identification method on the dataset of SHREC'15 _Non-Rigid 3D Shape Retrieval_. The benchmark is focused on the comparison of non-rigid 3D shape retrieval methods which have many pose-deformed 3D models. The dataset consists of 1200 3D models derived from 50 original models. For each original model, they used a 3D modeling tool to build its skeleton and then articulate it to generate the 23 deformed versions. Hence, the dataset can be classified into 50 categories, each with 24 models. Figure 3a shows some examples of the 3D models in the dataset. In our identification experiment, for each category, we used 20 for training the CNN and used the remaining four models with the 200 extra 3D models (see Figure 3b), which were randomly collected from _Thingiverse_ (a website dedicated to the sharing of user-created digital design files--[[https://www.thingiverse.com/](https://www.thingiverse.com/)]([https://www.thingiverse.com/](https://www.thingiverse.com/))) to test our method. The 200 extra 3D models are available at [[http://cclabs.smu.ac.kr/research/datasets-for-2d-view-depth-image-and-cnn-based-3d-model-identification-method/](http://cclabs.smu.ac.kr/research/datasets-for-2d-view-depth-image-and-cnn-based-3d-model-identification-method/)]([http://cclabs.smu.ac.kr/research/datasets-for-2d-view-depth-image-and-cnn-based-3d-model-identification-method/](http://cclabs.smu.ac.kr/research/datasets-for-2d-view-depth-image-and-cnn-based-3d-model-identification-method/)).  
As an identification system, it also should have ability to correctly reject irrelevant input queries, and the added 200 extra 3D models play an important role in our identification experiment to verify the ability.  
Figure 3: Examples of 3D models used in our identification experiments. (**a**) Examples of 3D models in the SHREC’15 _Non-Rigid 3D Shape Retrieval_; (**b**) Examples of 200 extra 3D models collected from _Thingiverse_.
## 7 Open Issues and Challenges
### Distance from CHs to CHs and CHs to SNs  
The energy consumption of its members is determined by the position and positioning of CH in a zone. Clusters with a greater intra-cluster distance spend more energy than clusters with a smaller intra-cluster distance. This must be taken into account by a clustering method, and a cluster should be established so that intra-cluster distance is less than inter-cluster distance [73; 74].
# Calibrated Phase-Shifting Digital Holographic Microscope Using a Sampling Moire Technique
## 2 A Calibrated Phase-Shifting Digital Holographic Microscope System  
The sketch of the calibrated phase-shifting digital holographic microscope system for transparent objects is shown in Figure 1. The diameter of the beam used to illuminate the sample is small, such that a beam can be extracted by Beam Splitter (BS) 1 before the beam passes through an expander. The extracted beam passes through the object and arrives at Camera 1 via the microscopic system. The collimated beam is divided into two arms by BS 2. One arm works as the reference beam, and the other one works as the object beam for Camera 2. The beam reflected from the mirror mounted on a PZT is subdivided into two beams by BS 4. One is reflected by BS 5 to arrive at Camera 2 and interferes with the beam reflected from mirror (M) 2. On the other hand, the beam reflected from M 4 and BS 6 arrives at Camera 1 and interferes with the object wave. Hence, the Camera 1 records a hologram including the object information, while Camera 2 records an interferogram with a periodic repetitive fringe pattern generated by two plane waves. The hologram recorded by Camera 1 and the interferogram recorded by Camera 2 will synchronously change if the phase of the reference beam is shifted by the PZT. The sampling Moire technique is capable of accurately measuring minute displacement from a single repetitive fringe pattern and the accuracy of the technique can theoretically achieve 1/500 of an interference fringe pitch [16; 17; 18]. Therefore, we introduce the sampling Moire method to analyze the interferograms recorded by Camera 2 to evaluate the phase-shifting errors. Finally, the 3-D object images are reconstructed by the phase-shifting error compensation algorithm [15].  
Figure 1: The sketch of the proposed calibrated phase-shifting digital holographic microscope system.  
In addition, the shifting amount of the PZT is not equal to the theoretical values, because the surface of the mirror mounted on the PZT is not strictly perpendicular to the incoming beam in general. If \(\varphi_{s}\) is the set phase-shifting amount of the hologram used for phase-shifting calculation, and the corresponding shifting amount of the PZT is \(d_{t}\), then many phase-shifted interference fringe patterns are recorded by Camera 2, and the average phase difference \(\overline{\Delta\varphi_{m}}\) between two neighboring interference fringe patterns can be calculated using the sampling Moire technique. The real shifting amount of the PZT can be determined using  
\[d=d_{t}\frac{\varphi_{s}}{\overline{\Delta\varphi_{m}}}. \tag{1}\]
## 1 Introduction
The two recommended configurations achieve much higher end-to-end PDR, a lower end-to-end delay and a higher energy efficiency than the default ZigBee settings in our experiments. Numerically, the Recommended-Unrestricted settings yield up to a 33.6% relative PDR increase, up to a 66.6% delay decrease and up to a 48.7% energy efficiency improvement in comparison with the default ZigBee settings. These improvements are achieved without incurring any overhead to the network. In addition, we provide detailed insight into the factors within and across the analyzed ZigBee layers that have a significant influence on the performance. Note that the findings of this work are not limited to ZigBee networks and also provide useful guidelines for the design and performance improvement of other IEEE 802.15.4-based networks. The rest of the paper is structured as follows. Related work is presented in Section 2. An overview of ZigBee is given in Section 3.1. The default configuration of the ZigBee protocol stack and the investigated network parameters and mechanisms are introduced in Section 3.2. In Section 4, the evaluation environment is described. In Section 5, the results of the evaluations are presented. Based on the results gained, the two improved configurations of the ZigBee protocol stack are proposed and evaluated in Section 6. In Section 7, the recommended settings are evaluated in alternative network scenarios. Section 8 concludes the paper.
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 2 Numerical Method
### Turbulence Model  
The SST model is insensitive to initial values and can accurately predict the characteristics of the mixing layer and jet flow [31; 32]. The equations are as follows [33]:  
\[\frac{\partial(\overline{\rho}k)}{\partial t}+\frac{\partial(\overline{\rho}k \widetilde{u}_{j})}{\partial x_{j}}=P_{k}-\beta^{*}\overline{\rho}\omega k+ \frac{\partial}{\partial x_{j}}\left[(\mu_{l}+\sigma_{k}\mu_{t})\frac{\partial k }{\partial x_{j}}\right] \tag{21}\]\[\begin{array}{c}\frac{\partial(\overline{\rho}\omega)}{\partial t}+\frac{\partial( \overline{\rho}\omega i_{j})}{\partial x_{j}}=P_{\omega}-\beta\overline{\rho} \omega^{2}+\frac{\partial}{\partial x_{j}}\Big{[}(\mu_{l}+\sigma_{\omega}\mu_ {t})\frac{\partial\omega}{\partial x_{j}}\Big{]}\\ \qquad\qquad\qquad\qquad\qquad+2(1-F_{1})\overline{\rho}\sigma_{\omega}2 \frac{1}{\omega}\frac{\partial k}{\partial x_{j}}\frac{\partial\omega}{ \partial x_{j}}\\ P_{k}=\tau_{ij}\,r\frac{\partial u_{i}}{\partial x_{j}}=\mu_{t}\overline{S}_{ ij}\,^{2},P_{\omega}=\frac{\gamma\rho P_{k}}{\mu_{\rm T}}\end{array} \tag{22}\]  
The expression for the eddy viscosity coefficient is obtained as follows:  
\[\mu_{t}=\rho\frac{a_{1}k}{\max(a_{1}\omega,\Omega\Gamma_{2})} \tag{24}\]  
where the function switches \(F_{1}\) and \(F_{2}\) are defined as follows:  
\[F_{1}=\tanh\!\left(\eta_{1}^{4}\right),\eta_{1}=\min\!\left[\max\!\left(\frac {\sqrt{k}}{0.09\omega y^{\prime}},\frac{500v}{\omega y^{2}}\right),\frac{4 \overline{\rho}\sigma_{\omega 2}k}{CD_{k\omega}y^{2}}\right] \tag{25}\]  
\[F_{2}=\tanh\!\left(\eta_{2}^{2}\right),\eta_{2}=\max\!\left(\frac{2\sqrt{k}}{ 0.09\omega y^{\prime}},\frac{500v}{\omega y^{2}}\right) \tag{26}\]  
\[CD_{k\omega}=\max\!\left(2\overline{\rho}\sigma_{\omega 2}\frac{1}{\omega} \frac{\partial k}{\partial x_{j}}\frac{\partial\omega}{\partial x_{j}},10^{-1 0}\right) \tag{27}\]  
where \(y\) is the distance to the wall and \(\
u\) is the kinematic molecular viscosity, and value for the constant \(\sigma_{k}\), \(\sigma_{\omega}\), \(\sigma_{\omega 2}\), \(\beta^{*}\), \(\beta\), \(\gamma\) can be found in previous study [33]. [PERSON] [34], based on the SST model, improved the turbulence model for adverse pressure separation flow, which had good effects on the prediction of the separation region before the jet and the penetration depth of the jet. The modification in the structural parameter \(a_{1}\) is provided as follows:  
\[a_{1}=\begin{cases}0.31&\frac{P_{k}}{D_{k}}\leq 1.1\\ \sqrt{\frac{P_{k}}{D_{k}}\beta^{*}}&\frac{P_{k}}{D_{k}}>1.1\end{cases} \tag{28}\]