esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Model Validation  
While the equations for contaminant adsorption to sand adopted in this paper (Equations (8) and (9)) have been reported and used in the literature [31, 37], the equation describing the adsorption mechanism of contaminant to retained colloidal particles presented in Equation (10) has not been reported. Therefore, the model presented in the previous sections was validated by comparing with laboratory experimental data as follows.  
Two cases of the experimental data presented in [9] were selected to validate the model documented in the previous sections and to obtain \(\omega_{2}\) and k\({}_{c}\), which describe the adsorption rate of contaminant to retained colloidal particles. A column experiment was performed to investigate the transport of Pb(II) under the presence of retained kaolinite colloidal particles. The column with 5.08 cm diameter and 4.61 cm height was designed and uniformly packed with 1%-5% kaolinite content. After equilibrating the system using the background solution without Pb(II), 5 mg L\({}^{-1}\) of Pb(II) solution was injected for 100 PVs. Conditions of the selected experiment cases for the model validation in this paper were 1% and 5% of kaolinite content at 6 mL min\({}^{-1}\) of flow rate and pH \(\sim\)6. Detailed information on the experimental procedure is documented in [9].  
The trust-region algorithm was used to obtain the best curve-fitted breakthrough curves and \(\omega_{2}\) and \(k_{c}\) at given observed breakthrough curves (the step and function tolerance for the stopping criteria was set as 10\({}^{-6}\)). As seen in Figure 1, the proposed model in this paper simulated the observed breakthrough curves accurately. Even though the back-calculated coefficients in Figure 1 may be able to exclusively describe the Pb(II) transport under the presence of kaolinite, the values of \(\omega_{2}\) = 0.016 s\({}^{-1}\) and \(k_{c}\) = 0.0152 s\({}^{-1}\) were used in further simulation in order to investigate the impact of \(\mu_{c}\) and \(\sigma_{c}\) of particle size distribution on contaminant transport at given \(\omega_{2}\) and \(k_{c}\).  
Figure 1: Observed and modeled breakthrough curves of Pb(II) under the presence of retained kaolinite colloidal particles at 1% and 5%. Back-calculated \(\omega_{2}\) and \(k_{c}\) are also presented with R\({}^{2}\) values. PV: pore volume.
## 4 Results and Analysis
### Identification of Signal App Traffic
#### 4.2.3 User B Typing Patterns  
In this section, we will analyze the traffic patterns of User B. Once User B started typing a message in his/her chat window, which would be sent to the target User A, it was always noticed that 729 bytes of packets with 663 bytes of payload were sent from the server to the client at the device of target User A. The client at target User A also sent 122 bytes of the packet in response with 56 bytes of payload. The target client device A also sent a response with packets of 105 bytes. In sequence, the server kept sending alerts to the client of 101, 97 bytes data packet with 35 and 31 payload sizes as shown in Figure 8. This pattern was observed and confirmed repeatedly through multiple iterations.  
Figure 8: User B message typing and sending.  
Figure 7: User A message typing and sending.
## 3 Results and Discussion
### Rate of Penetration Prediction Model  
In this study, SVM, BP, and RF methods are selected to establish an ROP model. The model optimization work was carried out by comparing the mean absolute percentage error (_MAPE_) of each model.  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{15}\]  
where \(\hat{y}\) is predicted ROP, and y is measured ROP.  
The modeling process of the three data-driven ROP prediction models is as follows:  
Data Partition: The data of five wells collected from the oil field have 18,143 remaining after outlier processing. Among them, 80% is used as the training set and 20% as the test set.  
Model Training: 14 features in Table 2 are used as inputs except for the ROP, and the ROP is the output. After training, the _MAPE_ of the training set was 5%. Hyperparameters of the random forest, SVM, and BP model were selected in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameter Type** & **Value** \\ \hline \multirow{6}{*}{RF} & number of decision trees & 185 \\  & maximum depth of decision tree & 130 \\  & minimum number of samples required to & 2 \\  & split decision tree nodes & \\  & finding the optimal number of feature & 9 \\  & variables to consider for a node & \\  & impurity evaluation function & MSE \\  & kernel function & rbf \\ SVM & penalty parameter & 100 \\  & gamma parameter & 0.01 \\  & number of hidden layers & 3 \\  & number of neurons & [64; 128; 32] \\ BP & optimizer & Adam \\  & learning rate & 0.001 \\  & loss function & MSE \\ \hline \hline \end{tabular}
\end{table}
Table 4: Superparameter of the ROP prediction data-driven models.  
Figure 9: The Pearson correlation coefficient between each parameter.  
Model Prediction: The test set was imported into the trained model, and the prediction results were output. The prediction results of the test set are shown in Figure 10.  
The random forest ROP model performs best in both test and training sets. Therefore, we choose to use the random forest ROP prediction model as one of the multiobjective optimization functions.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 2 Related Works
### ElGamal Public-Key Cryptography
#### 2.2.2 Encryption and Decryption  
Assume that the user \(U_{2}\) wants to encrypt a message \(m\) to the user \(U_{1}\). \(U_{2}\) chooses a random number \(r\) and computes \(b=g^{r}\bmod p\) and \(c=m\ \times\ y_{1}^{r}\bmod p\). \(U_{2}\) sends the ciphertext (\(b\), \(c\)) to \(U_{1}\). When receiving (\(b\), \(c\)), \(U_{1}\) can use \(U_{1}\)'s private key \(x_{1}\) to recover the original message by computing \(m=c\ \times\ (b^{x_{1}})^{-1}\bmod p\).
## 3 Study Data and Processing  
### Study Data  
Hefei is located in the central part of Anhui Province, China. It is the capital of Anhui Province, with a center latitude and longitude of 31\({}^{\circ}\)52\({}^{\prime}\) N, 117\({}^{\circ}\)17\({}^{\prime}\) E and a total area of 11,445 km\({}^{2}\). The climate belongs to the subtropical monsoon humid climate, and the landforms are dominated by hills. The geographical location and vegetation cover types are shown in Figure 4. The vegetation coverage data come from the MODIS land cover product ([[https://www.bu.edu/lsc/data-documentation/](https://www.bu.edu/lsc/data-documentation/)]([https://www.bu.edu/lsc/data-documentation/](https://www.bu.edu/lsc/data-documentation/)), (accessed on 19 August 2023)), and the coverage type is classified according to the International Geosphere-Biosphere Program (IGBP) classification scheme [33]. As a plain-based region, Hefei's main surface type is cropland. This research mainly focuses on surface reflection characteristics and surface albedo inversion in Hefei, Anhui, China.  
Figure 3: Algorithm flow of land surface albedo inversion.  
The FY-2G satellite scans the Earth once every hour and every half an hour during the flood period. The SVISSR detector carried by FY-2G has only one visible light channel, and its spectral range is from 0.550 to 0.750 microns. This study selects the multiangle observation data of the FY-2G geostationary meteorological satellite on the 24 th, 107 th, 228 th, and 302 nd days of 2019 to carry out inversion research on the surface albedo in Hefei. The data come from the FENGYUN satellite data service network ([[http://satellite.nsnsn.org.cn](http://satellite.nsnsn.org.cn)]([http://satellite.nsnsn.org.cn](http://satellite.nsnsn.org.cn)), (accessed on 19 August 2023)).  
Considering that the variation range of observation angles of geostationary satellites to a region is small, while the range of observation angles of polar-orbiting satellites is large, the MODIS reflectance product MOD09 GA/MYD09 GA in the red band was atmospherically corrected for the past five years from 2015 to 2019 and was selected in this research. The multi-angle observation data in the MODIS red band were used to construct typical BRDF archetypes in the Hefei area. At the same time, MODIS surface albedo product data MCD43A3 were selected to verify the inversion results. MODIS data were obtained from the EARTHDATA SEARCH website ([[https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)]([https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)), (accessed on 19 August 2023)).
# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 2 Methodology  
Two HS images \(\mathcal{I}_{1}\) and \(\mathcal{I}_{2}\), acquired at two different time steps for the same area, are given. Denoting with \(m\times n\times k\) the size of \(\mathcal{I}_{1}\) and \(\mathcal{I}_{2}\), each HS image is rearranged into a matrix \(I_{1}\) and \(I_{2}\), respectively, with size \(p\times k\), with \(p=mn\). The CD methodology in Algorithm 1 is proposed. The first step (line 2) consists in computing 6 error functions selected by previous and intensive experiments on several datasets; see [17; 26; 27]. More specifically, let the considered error function be denoted with \(f_{i}\), having \(i\) ranging between 1 and 6. To be self-contained, the description of the chosen error functions is reported in the following Section 2.1.
## 3 Results and Discussion
### MPPT Rating  
The MPPT rating is computed in this section using Equation (13). The variables for the ratings are: tuning parameter quantity (1-5), random numbers in algorithm (1-5), \(iter_{\max}\) for the GM location (1-5), tracking time (s), the efficiency (%age), the amount of hardware modification necessary for implementation, and the responsiveness of change in irradiance. The criteria for the selection of the ratings are shown in Figure 12. The criteria of the given values against every variable are shown in Equation (13).  
\[MPPT\_rating=\frac{Total\ achieved\ rating}{7} \tag{13}\]  
According to Table 3, the MRA does have the greatest MPPT rating of 1.285, indicating that it can follow the GM significantly more quickly. MPPT implementation for the MRA is straightforward because only one tuning parameter is needed. Because the MRA uses only  
Figure 11: Energy comparison Case 2.  
Figure 12: MPPT rating criteria in figure form.  
one random number, the oscillations are also reduced. A graphical representation of the MPPT rating achieved by the competing techniques is shown in Figure 13.  
The traditional gradient-based MPPT approaches are prone to power loss due to their inability to follow the GM under NUT. The metaheuristic approaches, i.e., the WOA and PSO, are a good way to deal with it, but the oscillations at the GM and extended tracking times lead to persistent oscillations that accumulate into a loss of power and efficiency. The MRA's effective behavior fixes the aforementioned issues and effectively tracks the GM up to 99.95% of the time. Additionally, the MRA tracks the GM up to 341.1 ms, which is 53.9% quicker than PSO. Because of this MRA capacity, the GM oscillations are extremely low and attain significant energy in comparison. Energy is extracted from the thermoelectric generator system 5.2% greater by the MRA. The current and voltage oscillations and variations are likewise less pronounced in the MRA. To fulfil the stopping requirements, the MRA uses a maximum of 18 iterations. The MRA obtains a superior MPPT rating because it uses fewer tuning parameters and random numbers.
## 3 Results and Discussion
### UV-Vis Spectrophotometry
In connection with these studies, the ratio of the integrated intensity of the absorption peak from the polyynes to that of the background (I\({}^{\text{A}}\)\({}_{\text{polyne}}\)/A\({}_{\text{background}}\)) is a measure of the efficiency of polyyne formation over the other products upon laser ablation of the bulk target in liquid [5; 27]. The ratio determined for the two polyynes versus the pulse energy is plotted in Figure 2a. The integrated intensity of the polyyne peaks, C-NMs band (I\({}^{\text{A}}\)\({}_{\text{polyne or C-NMs}}\)), and residual background absorption (A\({}_{\text{background}}\)), which are proportional to the relative concentration of each of those species in the solution, are also plotted in Figure 2b. The plots show that, for every pulse energy, the formation efficiency of the shorter chain polyyne C\({}_{6}\)H\({}_{2}\) is higher than that of the longer chain polyyne C\({}_{8}\)H\({}_{2}\). Additionally, in the measured pulse energy regime (up to 200 u), the polyyne formation efficiency increases with energy linearly and with the rate for the shorter polyyne being higher than that of the longer polyyne. Moreover, in the case of ns ablation, an increase in the polyyne concentration with fluence was obtained, which was determined by an increase in the pulse energy under a constant spot size [4]. UV-vis absorption spectra of solutions produced by a ps laser ablation also show that the shorter chain polyyne C\({}_{6}\)H\({}_{2}\) is produced with fluence at a higher rate than the longer chain polyyne C\({}_{8}\)H\({}_{2}\) (i.e., the intensity of the peak at 199 nm increases with fluence faster compared with the intensity of the peaks at 215.5 and 225.5 nm) [12]. According to the accepted model for polyyne formation (outlined in the Introduction of this paper), this in turn indicates that the chain growth termination becomes more effective as the fluence increases because of the increase in the amount of hydrogen atoms or radicals that are produced from the water vaporization or decomposition and that are mixed with the plasma plume species.
# Discussions on the Processing of the Multi-Component Seismic Vector Field
## 1 Introduction
It is difficult to effectively exhibit the holographability (containing abundant dynamics and kinematics information) of a vector field and the superiority of a vector processing method. In this paper, we introduce existing vector de-noising techniques for multi-component seismic data. The earliest vector de-noising technique for multi-component seismic data was polarization filtering. It was first applied to studies of natural earthquakes. For example, [PERSON] [23] proposed a method of three-component instantaneous polarization property analysis and applied this method to effectively identify the seismic phase and arrival time of the SV-wave, Love wave, and the Rayleigh wave in the recording of the San Fernando Earthquake in 1971. [PERSON] and [PERSON] [24] gave an overview of how to use the polarization vector of the fast and slow shear waves to detect anisotropy and crustal stress. [PERSON] and [PERSON] [25] proposed a method of time-frequency polarization analysis, which was used to separate and reconstruct various types of waves that interfere with each other in an earthquake recording. Thereafter, polarization filtering was expanded and applied to suppress surface-waves in multi-component seismic data, separate fast and slow S-waves, and de-noise random signals. Diverse methods were developed, including the time-domain [26], frequency-domain [27], wavelet transform domain [28], and t-f-k domain [29] methods. Several filtering methods based on vector order statistics, which are widely applied to color image processing, are very suitable for processing vector signals. [PERSON] et al. [30] and [PERSON] et al. [31] applied these methods to the random noise suppression of multi-component seismic data and achieved good results. In addition, the random noise attenuation method proposed by [PERSON] et al. [32], based on the 3C group sparsity constrained time-frequency transform, can process the multiple components simultaneously, and retain the relative amplitude relationship among the components and the weak effective signal well. Separation of the P-waves and S-waves is a special de-noising processing, which is very important in processing multi-component seismic data. The vector separation of P-waves and S-waves proposed by [PERSON] [33], [PERSON] et al. [34], and [PERSON] [35] can retain the intrinsic properties of a vector wavefield.
## 2 Experimental Setup and Configuration
### System Configuration
#### 2.1.1 Set-Up
The measurements of the heat exchanger with an aluminum groove fin were 820 mm (length) \(\times\) 52 mm (width) \(\times\) 270 mm (height). The fan of the condenser part was axial and propeller type. The fans applied to the condenser part circulated the air to the outside space as shown in Figure 2b. In particular, the suggested air flow mechanism of the evaporator part was optimized for the designed air conditioning system of the special purpose vehicle, effectively supplying the cooled air to the cabin of the combat policemen or equipped passengers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & & **Specifications** \\ \hline \multirow{3}{*}{Compressor} & Displacement rate (cm\({}^{3}\)/rev) & 160.0 \\  & Type & Reciprocating \\  & Size (L, W, H, mm) & 1100.0 \(\times\) 920.0 \(\times\) 220.0 \\ \hline \multirow{3}{*}{Evaporator} & Fin type & Aluminum plate \\  & Size (L, W, H, mm) & 850.0 \(\times\) 160.0 \(\times\) 163.0 \\  & Fan type & Multi-blade \\  & Fin type & Aluminum groove \\ \hline \multirow{3}{*}{Condenser} & Size (L, W, H, mm) & 820.0 \(\times\) 52.0 \(\times\) 270.0 \\  & Fan type & Axial and propeller \\ \hline Receiver tank & Size (D, H, mm) & 120.0 \(\times\) 320.0 \\ \hline Accumulator & Size (D, H, mm) & 120.0 \(\times\) 180.0 \\ \hline Expansion device & Type & Thermostatic expansion valve (TEV) \\ \hline Refrigerant & Type & R-134a \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the air conditioning system with two parallel refrigeration cycles.
# On Non-Completeness and G-Equivariance
###### Abstract  
With the growing threat of the side-channel attack (SCA) to the cryptographic algorithm's implementations, the masking method has become one of the most promising SCA countermeasures for securely implementing, for example, block ciphers. The basic principle of the masking method is that if the sensitive variable (which, by definition, depends on sensitive information) is split into some random variables and they are manipulated in a secure manner, then the relationship between the random variables and the corresponding side-channel information may look independent from the outside world. However, after the introduction of the glitch attack, there has been a lot of concern about the security of the masking method itself. And, to mitigate the threat of the glitch attack, the threshold implementation (TI) and G-equivariant gates were independently introduced as countermeasures. In this paper, we consider the main notions of two such independent glitch attack's countermeasures, say, non-completeness and G-equivariance, and investigate their relationship. The contribution of this paper is three-fold. First, we show that the widely-circulated proof that the non-complete TI with uniform inputs guarantees the security against the 1 st order DPA even in the presence of glitches is not satisfactory. Next, using the extended notion of G-equivariance to the higher-order setting, we prove that non-completeness implies G-equivariance, which, in turn, means that the non-complete TI with uniform inputs has resistance against the glitch attack. Thirdly, we prove that the set of non-complete gates is a proper subset of the set of G-equivariant gates by showing there is a gate that is G-equivariant but not non-complete.  
th pacs: +
Footnote †: journal: Applied
# Improving Multi-Label Learning by Correlation Embedding
## 3 The Proposed Method
### Optimization
#### 3.3.2 Update \(\mathbf{W}\)  
With \(\mathbf{P}\) fixed, the Equation (5) becomes:  
\[\min_{\mathbf{W}}\ \ \ \frac{1}{2}||\mathbf{XPW}-\mathbf{Y}||_{F}^{2}+\frac{ \lambda_{1}}{4}||\mathbf{C}-\mathbf{W}^{T}\mathbf{W}||_{F}^{2}+2\lambda_{2} \text{tr}(\mathbf{W}^{T}\mathbf{DW}) \tag{9}\]  
Therefore, we can obtain the gradient w.r.t \(\mathcal{W}\) as  
\[\
abla_{\mathbf{W}}\mathcal{F}=\mathbf{P}^{T}\mathbf{X}^{T}\mathbf{XPW}- \mathbf{P}^{T}\mathbf{X}^{T}\mathbf{Y}+\lambda_{1}(\mathbf{WW}^{T}\mathbf{W})+ 2\mathbf{DW}. \tag{10}\]  
Consequently, \(\mathbf{W}\) can be updated by  
\[\mathbf{W}=\mathbf{W}-\lambda_{w}\
abla_{\mathbf{W}}\mathcal{F}. \tag{11}\]  
Similarly, the step size \(\lambda_{w}\) is also determined by the Armijo rule [58]. According to the above optimization process, we give the pseudo code of the proposed method MLLCE in Algorithm 1.  
```
Input: Training data matrix \(\mathbf{X}\in\mathbb{R}^{n\times d}\), label matrix \(\mathbf{Y}\in\mathbb{R}^{n\times q}\), and weighting parameters \(\lambda_{1},\lambda_{2}\); Output: Model Coefficient \(\mathbf{W}^{*}\) and Projection Matrix \(\mathbf{P}^{*}\);
1 repeat
2 Calculate the gradient \(\
abla_{\mathbf{P}}\mathcal{F}=\mathbf{X}^{T}\mathbf{XPW}\mathbf{W}^{T}- \mathbf{X}^{T}\mathbf{YW}^{T}\);
3 Update \(\mathbf{P}\) by Equation (8);
4 Calculate the gradient \(\
abla_{\mathbf{W}}\mathcal{F}=\mathbf{P}^{T}\mathbf{X}^{T}\mathbf{XPW}- \mathbf{P}^{T}\mathbf{X}^{T}\mathbf{Y}+\lambda_{1}(\mathbf{WW}^{T}\mathbf{W})+ 2\mathbf{DW}\);
5 Update \(\mathbf{W}\) by Equation (11);
6 Update \(\mathbf{D}\);
7 until converge;
8 Return \(\mathbf{W}^{*},\mathbf{P}^{*}\);
```  
**Algorithm 1**Improving Multi-Label Learning by Correlation Embedding
# Comparison between Laser and Stamping without Die (SWD) for Micro Tapered Hole Forming
###### Abstract  
The forming of a micro tapered hole is based on nanosecond pulsed laser processing, which conforms to fast processing time and high throughput; however, the microhole quality should be improved. Micro stamping is a technology providing high precise size and speed. The greatest difficulty in forming a microhole by micro stamping is the precision alignment of the punch head to the lower die. In order to overcome the difficulty, we proposed a concept of stamping without die (SWD). Without a lower die, the tapered punch head was directly applied to the workpiece for micro stamping, and a thicker workpiece surrounding the punching area provides a better support to the stamping process. Thus, a successful forming of micro tapered holes is completed. The micro tapered hole depth is 300 \(\upmu\)m, and the maximum ratio of inlet to outlet diameter is 18:1. In order to reduce the number of experiments, the finite element analysis software DEFORM-3D was used for forming analysis. The simulation forecast result was compared with the experimental processing, which was well validated. Under different experimental parameters of laser energy and defocusing distance, drilling results by two methods show that the microhole quality by stamping process is better than by laser processing.  
micro stamping; stamping without die; micro tapered hole; DEFORM-3D; laser processing Article
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 2 Methodology  
Our ship detection approach is implemented based on an DNL-added YOLONet framework with a preprocessing module. As shown in Figure 2, all image patches are divided to different classes by DPC algorithm according to their gray-histogram. Then we use scene-adapted saliency detection method to capture the salient targets of image blocks in different background. Next, integrate the saliency maps with image blocks as training samples and fed them into network. After that, the YOLOv4 network added with the DNL module (or called saliency guided DNL-YOLO) is utilized for feature extraction and target detection. These details are elaborated upon in the following subsections.