esa-sceva/satcom-chunk-collection huggingface dataset:
# Loop-Type Field Probe to Measure Human Body Exposure to 5G Millimeter-Wave Base Stations
## 2 Probe Design
### Probe Operation  
The single-ended loop shown in Figure 1 enables simultaneous measurement of the E- and H-fields. The H-field perpendicular to the antenna substrate is coupled to the loop antenna, and this coupling generates a current flowing through the load resistor. As shown in Figure 1, the loop antenna has a gap between the loop end and the ground plane. The E-field parallel to the antenna substrate is coupled to this gap, generating a voltage across the load resistor. The current and voltage then generate power that is dissipated at the load resistor. The load resistor also prevents the LNA from oscillating by creating a leakage path that avoids the radiation leakage [17]. Parallel distribution of the two resistors makes the input power to LNA equal to the dissipating power. Such an operation can be expressed as  
\[P_{in,LNA}=C_{E}E_{x,RMS}\cdot C_{H}H_{y,RMS}\cos(\theta_{E}-\theta_{H}) \tag{1}\]  
In (1), all coefficients have a phasor form. \(P_{in,LNA}\) represents the input power to LNA, while \(E_{x,RMS}\) and \(H_{y,RMS}\) are the magnitude of the incident \(x\)-axis E- and \(y\)-axis H-fields, respectively, in the RMS unit. \(C_{E}\) is the coupling coefficient between the gap voltage end and the incident E-field, while \(C_{H}\) is that between the loop current and the incident H-field. The E- and H-fields can have different phases in the near field region. For increasing the difference between the phases, the power has a real component decreasing. In (1), \(\theta_{E}\) and \(\theta_{H}\) represent the phase of the E- and H-fields, respectively, and the cosine term, including the phase results in the real power component.  
In Figure 2, LNA amplifies the input power in (1) with a gain of \(G_{LNA}\). The amplified power is then applied to the RMS power detector. The detector converts the amplified power into a dc voltage that is proportional to the input power. This proportional relation is represented by another coefficient \(C_{PD}\). The output voltage in a linear scale is proportional to the input power in dB units [16]. As a result, the output voltage \(V_{PD}\) is expressed using (1) as  
\[V_{PD}=10\log[C_{PD}G_{LNA}C_{E}C_{H}\cdot E_{x,RMS}H_{y,RMS}\cdot\cos(\theta _{E}-\theta_{H})] \tag{2}\]  
In (2), \(E_{x,RMS}H_{y,RMS}\cdot\cos(\theta_{E}-\theta_{H})\) corresponds to PD of the incident field, so PD can be obtained by measuring \(V_{PD}\) and calculating PD from (2). The product of the coefficients in (2) can be obtained via measurement, which will be described later in this paper.  
Figure 3: Frequency response of voltage at the load resistor, \(R_{A}\).
## 2 Materials and Methods
### Specific Features of the Test Engine  
As mentioned above, the basis for the development of the test engine was a mass-produced four-cylinder turbocharged spark-ignition engine. This engine is equipped with a gasoline direct injection system. The engine adaptation for this research was mainly focused on the modification of the cylinder head and camshaft. A different turbocharger dedicated to the engine with a smaller displacement was used. The control of the boost pressure was done through the wastewater valve integrated in the turbine housing. For the test engine, the exhaust and intake manifolds were designed and manufactured. The test engine is fully autonomous, which means that the power to drive the high-pressure fuel pump, alternator, water, and oil pumps is derived from the engine rather than from external sources. Basic technical data of the engine are summarized in Table 1.  
An engine management system (EMS) was built based on the stand alone AEM controller cooperating with the wide-band oxygen sensor. This EMS allowed us to change the operating parameters of the engine in real time during operation. High-pressure fuel injectors supplied with increased voltage were controlled using the Denso injector driver, because the EMS only allowed us to control high-resistance injectors.  
Implementation of the engine concept presented in the scheme in Figure 2 required us to also develop modified camshafts, which provided valve timing as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bore & 82.5 mm \\ Stroke & 92.8 mm \\ Compression ratio, \(\epsilon_{\text{cp}}\) & 10.5 \\ No. of cylinders & 4 in-line, 2 fired and 2 add. expansion \\ Displacement of a fired cylinder, V\({}_{\text{frd}}\) & 2 cylinders, 496 cm\({}^{3}\) each \\ Displacement of both add. expansion cylinders, V\({}_{\text{add}}\) & 992 cm\({}^{3}\) \\ Overall expansion ratio \(\epsilon_{\text{dcp}}\) & 21 \\ No. of valves & 4 per cylinder \\ Turbocharger type & KP39 \\ Control system & AEM EMS 30-1010 \\ High-pressure injector driver & Denso 131000-1041 \\ Control method of high-pressure of fuel & pressure relief valve \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic technical data of the test engine.  
Figure 4: Valve timing of the test engine; Top Dead Center (TDC).
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Zinc-Based NPs
#### 4.4.3 Impact on Antioxidant Enzymes  
Growth of fresh and dry weight of HRs treated with 100 mg/L ZnO NPs showed a decrease compared to the control, whereas activities of CAT, GPX, and APX were considerably higher than in the control [67]. Significant increase of CAT activity was observed at application 60 and 100 ppm of ZnO NPs to _Momordia charantia_ L. plants grown in pots, but the activities of GPX and AXP were not affected [132].  
In cell suspension cultures of _Linum_ visitatsimum_ L. exposed to ZnO NPs, the highest PAL activity was observed at treatment with 30 mg/L for 48 h, whereas at application of 60 mg/L ZnO NPs, the highest activity of cinnamyl alcohol dehydrogenase was achieved. On the other hand, using both above-mentioned ZnO NP concentrations, the highest TPC and lignan amounts could be obtained [140]. In seedlings of flax (_Linum visitatsimum_) cultivated on MS medium and in stem-derived callus, the presence of ZnO NPs (1-1000 mg/L and 10-50 mg/L, respectively) caused the dose-dependent ROS generation. A differential induction of SOD (in seedlings) vs. POD (in vitro cultures) enzymes was observed. At concentrations > 500 mg/L (seedlings) and >25 mg/L ZnONPs (callus) oxidative damage was observed [138].  
Spherical green synthesized ZnO NPs with mean size of 41 nm had positive impact on root and shoot length of _Borago officinalis_ plants, but did not affect significantly soluble sugar and anthocyanin levels, and CAT activity in the plants. Due to enhanced ROS generation by ZnO NPs, the contents of proline and Chl decreased and oxidative stress in plants was accompanied with increased H\({}_{2}\)O\({}_{2}\) content, lipid peroxidation, phenol content, and APX, GPX, PAL activities [240].  
ZnO NPs capped with PEG and PVP enhanced total antioxidant capacity, total reducing power and DPPH free radical scavenging activity in shoots of _S. rebaudiana_ Bertoni cultured in vitro more than uncapped ZnO NPs, concentration 1 mg/L being highly effective [211]. Addition of 100 mg/L ZnO NPs resulted in effective increase in TPC, TFC, total antioxidant capacity and DPPH free radical scavenging activity in 6-week-old callus produced from leaf explants of _S. rebaudiana_ Bertoni grown in MS medium; the highest total reducing power was observed at application 50 mg/L ZnO NPs [117].
## Framework
### Local Density Peaks Clustering  
An image usually consists of different types of image patches. It is efficient to describe the underlying structure of each image patch by using specific sub-dictionary that describes different  
Figure 1: Proposed Image Fusion Framework.  
types of image patches. This paper uses the local density peaks clustering method to classify image patches into a specific group by the similarity of structure [25; 26]. Compared with other existing clustering methods, the local density peaks clustering method has two advantages. First, the method is insensitive to the start point (or initialization). Second, it does not need to know the number of clusters before clustering. Moreover, the basis of local density peaks clustering can be easily expressed by Euclidean distance between two different patches.  
In Figure 2a, the local density \(\rho_{i}\) of each image patch i is calculated by using Equation (1).  
\[\rho_{i}=\sum_{j}\chi(d_{ij}-d_{c})\ \, \tag{1}\]  
when \(x\leq 0\), \(\chi(x)=0\), otherwise \(\chi(x)=1\). \(d_{ij}\) is the Euclidean distance between image patch \(i\) and \(j\). \(d_{c}\) is a cutoff distance and usually set to the median value of \(d_{ij}\). Basically, \(\rho_{i}\) equals the number of patches that are closer than \(d_{c}\) to patch i. The clustering algorithm is only sensitive to the relative magnitude of \(\rho_{i}\) from a different image patch, and robust with respect to the choice of \(d_{c}\).  
A distance \(\delta_{i}\) of each image patch is measured to find the cluster centers. The calculation equation is shown in Equation (2). \(\delta_{i}\) is the minimum distance between the image patch \(i\) and any other patch \(j\) with higher density. For the point with highest density, \(\delta_{i}=max(d_{ij})\). A local density map can be constructed by using \(\rho_{i}\) (\(x\)-axis) and the normalized \(\delta_{i}\) (\(y\)-axis, \(0\leq\delta_{i}\leq 1\)), which is shown in Figure 2b. The cluster centers are recognized that are boxed by dotted squares in Figure 2b, when the value of \(\delta_{i}\) is anomalously large. When the cluster centers are identified, the remaining image patches are clustered into the relatively nearest identified center.  
\[\delta_{i}=\min_{j:\rho_{j}>\rho_{i}}\left(d_{ij}\right)\ \, \tag{2}\]
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### Constitutive Model Under THMC Coupling with Governing Equations  
3.1.1. Mechanical Behavior (M)  
A rock's mechanical behavior is governed by its stress-strain relationship. The constitutive equation for rock mechanics usually follows the general form of Hooke's law in the elastic region. Still, rock behavior under long-term loads involves creep, plasticity, and damage mechanics.  
The governing equation is as follows:  
\[\sigma_{ij}=D_{ijkl}\in_{kl} \tag{1}\]  
where \(\sigma_{ij}\) is the stress tensor, \(D_{ijkl}\) is the elastic modulus tensor, and \(\in_{kl}\) is the strain tensor.  
However, under coupled THMC conditions, mechanical behavior is also influenced by pore pressure \(p\), temperature \(T\), and chemical processes.  
For elastic-plastic behavior, the following equation is used:  
\[\sigma_{ij}=D_{ijkl}(\in_{kl}-\in^{p}_{kl}) \tag{2}\]  
where, \(\in^{p}_{kl}\) is the plastic strain component in Equation (2).  
The mechanical constitutive model also incorporates damage, which evolves over time:  
\[D=1-\frac{E_{d}}{E_{0}} \tag{3}\]  
where Equation (3) consists of \(D\) as the damage variable, \(E_{d}\) as the degraded modulus due to damage, and \(E_{0}\) as the initial elastic modulus.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 3 Preliminaries and Background
### Encryption for Secure Deduplication
#### 3.1.1 Convergent Encryption  
[PERSON] et al. proposed a convergent encryption (CE) technique for secure data deduplication in cloud storage [4]. CE is a technique that encrypts data with hash values. With CE, one does not need to share keys in advance because one has the same hash value for the same data. When plaintext data \(m\) is added to the hash function and the output string is \(h(m)\), the encryption key at CE becomes \(h(m)\). Encrypting plaintext \(m\) using encryption key \(h(m)\) creates ciphertext \(C\). Existing data \(m\) can be obtained by decrypting the ciphertext \(C\) using the encryption key \(h(m)\). The hash values of the same data match according to the nature of the hash function, which is a one-way function. In other words, even if the owner is different, if the data are the same, the same hash value can be generated, and if the same encryption key is used, the same ciphertext can be generated. Cloud storage clients can generate ciphertexts using hash values derived from data without the need for key sharing.  
* KeyGen\((h,m)\). Given a cryptographic hash function \(h\) and plaintext \(m\) as input, a convergent key \(K=h(m)\) is output.
* Encrypt\((K,m)\). Given convergent key \(K\) and plaintext \(m\) as input, it produces encrypted data \(C\).
* Decrypt\((K,C)\). Given convergent key \(K\) and ciphertext \(C\) as input, it produces decrypted data \(m\).
## 2 Materials and Methods  
### Method  
There are three steps to realize the standardization and traceability of grayscale. Firstly, the grayscale should be transmitted to an absolute value in theory, as described in Section 2.1. Secondly, a corresponding equipment should be established to realize the measurement and traceability of the theory value, as described from Section 2.2 to Section 2.4. Finally, the traceability uncertainty needs to be analyzed to accomplish the standardization, as described in Section 3.  
The grayscale of an image is a relative value. For traceability research, it should be transferred to absolute value. This step is related to the image type. Hardcopy images (usually films) are mostly described by optical density [24]. Optical density includes transmission density \(D_{T}\) and reflection density \(D_{V}\), and \(D_{T}\) is defined as shown in Equation (1), where \(\varPhi_{i}\) is incident diffuse light flux and \(\varPhi_{i}\) is transmission light flux [25]. Similarly, \(D_{V}\) is defined as shown in Equation (2), where \(\varPhi_{r}\) is reflection light flux [25]. We have developed a micron-scale printed density film (transmission film and reflection film) as the standard of value transmission [16]. The changes in optical density on a plane are achieved by adjusting the spatial frequency in the ways of amplitude modulation (AM) or frequency modulation (FM). Furthermore, spatial frequency is characteristic of periodic structure in space. As shown in Figure 1, different pixels in each display unit are assigned different values, and each unit is assigned the same pixel information, so it appears visually as a certain grayscale.  
\[D_{T}=\log_{10}(\phi_{\tau}/\phi_{i}), \tag{1}\]  
\[D_{V}=\log_{10}(\phi_{\tau}/\phi_{i}), \tag{2}\]For softcopy images, it is not optical density, but luminance, and the change of luminance is achieved by adjusting the time frequency. The same theory is applied in DLP technology. At the same time, image display resolution and detection precision are both improving, and real-time image processing technology is gradually maturing [26; 27; 28]. Consequently, digital grayscale generation equipment is established to reproduce high-resolution (bit depth) grayscale accurately, which can be regarded as grayscale-to-luminance traceability equipment, providing a measurable basis for grayscale image display standardization.
## 5 Numerical and Simulation Results
### Multiple SWIPT Relaying Systems
#### 5.2.3 Channel Capacity Comparison  
Figures 10 and 12 illustrate the channel capacity as a function of the direct link SNR for various values of \(R\), with \(\rho_{r}=\rho_{\text{opt}}\) under Ch. Model\(=[1,1,X]\) and Ch. Model\(=[1,X,1]\), respectively. In addition, Figures 11 and 13 provide a comparison of channel capacity for \(R\in\{1,2,3,4\}\), also under the respective channel models, while varying the SNR of the direct link. As observed in Figures 10-13, as the channel capacity tends to decrease, \(R\) increases. The analytical results labeled as 'Theory, AF-App', derived from (48) and (49), serve as lower bounds on the channel capacity, particularly in the certain SNR region. Forthe indirect link, 'Theory, AF-App' shows a mismatch with the simulation results, and this mismatch is inversely proportional to \(R\). On the other hand, 'Theory, AF-App' provides a well-aligned lower capacity bound for the combined link. Overall, it is confirmed that the approximately derived analytical results illustrate the channel capacity trend under various SNR and \(R\) conditions.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 2 Methods
### Line Segment Validation and Aggregation
Finally, we determine whether there is an overlap between the two line segments, as shown in Figure 7. The two segments \(l_{a}\) and \(l_{b}\) are projected onto the \(x\)-axis, and the overlap length \(\psi(l_{a},l_{b})\) between \(l_{a}\) and \(l_{b}\) can be obtained. Since the aggregation method is the last part of ST_LSD, it is necessary to prevent false positives while connecting disjoint line segments that may come from the same line segment. Therefore, we set a strict criterion for overlap judgment, where only \(\psi(l_{a},l_{b})=0\). In other words, the last condition for aggregation can be satisfied only when there is no overlap between the two line segments. We list five typical relationships between adjacent line segments, as shown in Figure 8. In Figure 8a, the angle difference between line segments \(l_{a1}\) and \(l_{a2}\) is too large to satisfy the first condition of aggregation. As shown in Figure 8b, although line segments \(l_{b1}\) and \(l_{b1}\) satisfy the constraint of an angle difference, the endpoint distance between the two line segments is greater than the endpoint distance threshold \(\tau_{d}\). This situation does not meet the aggregation condition. In Figure 8c, the angle difference between line segments \(l_{c1}\) and \(l_{c1}\) is small enough, and the endpoint distance also meets the requirements, but the two line segments overlap and do not meet the aggregation requirements. In Figure 8d, line segments \(l_{d1}\) and \(l_{d1}\) maintain a collinear disjoint relationship, which is the most ideal situation for aggregation. The two line segments are adjacent and close enough, and all three conditions meet the requirements, so the aggregation operation can be carried out on the two line segments. In Figure 8e, line segments \(l_{c1}\) and \(l_{c1}\) show a parallel disjoint situation, which is common in structural space targets composed of polyhedrons. In this case, the overlap of the two line segments also makes it impossible to meet the aggregation requirements. Figure 6.0 Schematic diagram of the overlap length between two line segments.
## 3 Results
### ERD Detection during MI  
ERD was detected in all 13 patients with the MSM and 12 of the 13 patients with the RP. An example of ERD detection is shown in Figure 6. ERDs were detected in both the MSM and RP, and the movement of the DiC arm, which was reconfirmed by moving images, was included in the analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **Sex** & **Age** & **Handedness** & **Height** & **Weight** &
\begin{tabular}{c} **Days after** \\ **Onset** \\ \end{tabular} & **Diagnosis** & **Side** & **Lesion** \\ \hline
1 & Male & 53 & Right & 171 & 78.6 & 29 & Hemorrhage & Left & Putamen \\
2 & Female & 47 & Right & 153 & 59.1 & 44 & Hemorrhage & Bilateral & Putamen \\
3 & Male & 78 & Right & 153 & 51.6 & 75 & Infarction & Right & Basal \\
4 & Male & 77 & Right & 171.5 & 58.5 & 107 & Hemorrhage & Right & Putamen \\
5 & Female & 77 & Right & 144 & 48.6 & 109 & Infarction & Right & Corona \\  & Female & 46 & Right & 154 & 64.7 & 84 & Hemorrhage & Right & Putamen \\
7 & Male & 79 & Right & 153 & 57 & 37 & Infarction & Light & Corona \\
8 & Male & 81 & Right & 157 & 59.9 & 77 & Infarction & Right & Corona \\  & & & & & & & & & \\
9 & Male & 75 & Right & 167 & 43.5 & 85 & Infarction & Left & Internal \\  & & & & & & & & & capsule \\
10 & Male & 55 & Right & 173 & 58.2 & 42 & Infarction & Left & ACA, \\
11 & Male & 62 & Right & 167 & 58 & 128 & Hemorrhage & Right & Putamen \\
12 & Male & 63 & Right & 174 & 60.3 & 194 & Hemorrhage & Left & Thalamus \\
13 & Male & 41 & Right & 175 & 83.9 & 110 & Hemorrhage & Right & Putamen \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinical characteristics of the patients.  
Figure 5: Flow chart regarding participant selection.  
In terms of RP, no joint motion of the DiC was observed in the movie captured during the experiment with patient 1. Therefore, we concluded that the ERD signal could not be detected in this patient. The data for patient 1 were excluded from the RP analysis. In addition, outliers were tested for each of the 12 patients, except for patient 1, and two patients with outliers were excluded from the analysis. Finally, the RP reaction time was analyzed in 10 patients.
# Modified Dual-Site Langmuir Adsorption Equilibrium Models from A GCMC Molecular Simulation
## 3 GCMC Molecular Simulation
### Force Field  
Since the main purpose of this study is to determine the characteristics of changes in the adsorption energy in the adsorption process, the interactions and energy distributions between adsorbates and adsorbents were explored. When selecting a force field, interactions between adsorbates can be ignored to simplify calculations. On this basis, the Lennard-Jones (LJ) potential energy model was selected to describe the van der Waals force in the adsorption process, and the Ewald summation method was used to describe the electrostatic force. The parameters of cross-interactions between different atoms were calculated using the Lorentz-Berthelot mixing rules. The potential parameter values for CO\({}_{2}\) molecules were taken from TraPPE, and the potential parameter values for the MIL-101 (Cr) framework were taken from the universal force field (UFF). According to the literature [25; 26], the values were adjusted in order to accurately describe the CO\({}_{2}\) adsorption process on MIL-101 (Cr). The force field parameters are shown in Table 3
## 2 Materials and Methods
### Outlier Detection and Replacement
#### 2.3.1 Box Plot Theory  
This article first uses box plots to identify outliers. The 3\(\sigma\) criterion assumes that the data obeys the normal distribution, but the actual data often does not strictly obey the normal distribution. The standard for judging outliers is based on calculating the mean and standard deviation of the data. The resistance of the mean and standard deviation is extremely small, and the outliers themselves will have a greater impact on them, so the number of outliers judged is very small. Box plot theory does not use the mean and variance as the basis to determine the abnormality of the data and does not need to assume that  
Figure 1: Output variable distribution status.  
the data obeys a specific distribution. It can intuitively describe the discrete distribution of the data. The box plot uses quartiles and interquartile ranges. It has strong resistance and is not easily disturbed by abnormal data, which provides a preliminary standard for identifying abnormal values (see Figure 2).  
In the boxplot, \(Q_{1}\) is the lower quartile (25% quartile), \(Q_{3}\) is the upper quartile (75% quartile), and the interquartile range (IQR) refers to the difference between the 25% and 75% quartiles.  
The formula for calculating \(IQR\) is as follows: \(IQR=Q_{1}-Q_{3}\), where minimum: \(Q_{3}-1.5\times IQR\), maximum \(Q_{1}+1.5 IQR\).  
The criteria for determining outliers are:  
\[\text{Outlier}=\text{value}<(Q_{3}-1.5 IQR)\text{ or value}>(Q_{1}+1.5 IQR)\]