esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Advantage Distillation Scheme for Bidirectional Secret-Key Agreement
## 4 Multiple Rounds of Secret-Key Agreement
### SKA from [PERSON] to [PERSON]  
(A2. 1)Let  
\[T_{2i-1}=V_{2i-2}. \tag{28}\]  
Then, [PERSON] sends the raw key \(M_{ab}^{i}\) to [PERSON] by  
\[U_{2i-1}=T_{2i-1}\oplus M_{ab}^{i}. \tag{29}\]  
(A2. 2)[PERSON] and [PERSON] can receive the noisy version of \(U_{2i-1}\) as  
\[V_{2i-1}=U_{2i-1}\oplus E_{ab}^{i}=M_{ab}^{i}\oplus U_{2i-2} \oplus E_{ba}^{i-1}\oplus E_{ab}^{i}, \tag{30}\] \[W_{2i-1}=U_{2i-1}\oplus E_{ae}^{i}=M_{ab}^{i}\oplus U_{2i-2} \oplus E_{ba}^{i-1}\oplus E_{ae}^{i}. \tag{31}\]  
(A2. 3)[PERSON] and [PERSON] have the knowledge of \(U_{2i-2}\) and \(W_{2i-2}\). Therefore, they can obtain the noisy version of \(M_{ab}^{i}\) by XOR \(U_{2i-2}\) and \(W_{2i-2}\) to \(V_{2i-1}\) and \(W_{2i-1}\), respectively. The result is calculated as  
\[\bar{M}_{ab}^{i}=V_{2i-1}\oplus U_{2i-2}=M_{ab}^{i}\oplus E_{ba}^{i-1}\oplus E _{ab}^{i} \tag{32}\]  
and  
\[Z_{ae}^{i}=W_{2i-1}\oplus W_{2i-2}=M_{ab}^{i}\oplus E_{ba}^{i-1}\oplus E_{be}^ {i-1}\oplus E_{ae}^{i}. \tag{33}\]  
### SKA from [PERSON] to [PERSON]  
(B2. 1)In the \(i\)-th round, [PERSON] has to make  
\[T_{2i}=V_{2i-1}. \tag{34}\]  
Figure 5: The \(i\)-th round of bidirectional secret-key agreement.  
Then, he sends the raw key \(M_{ba}^{i}\) in the noisy form, which is  
\[U_{2i}=T_{2i}\oplus M_{ba}^{i}, \tag{35}\]  
over the main channel. Meanwhile, [PERSON] may wiretap it by the eavesdropper's channel.  
**(B2. 2)**: [PERSON] can receive \(V_{2i}\), consisting of the noisy vector \(E_{ba}^{i}\) and \(U_{2i}\), and [PERSON] can receive \(W_{2i}\), which consists of the noisy vector \(E_{be}^{i}\) and \(U_{2i}\),  
\[V_{2i}= U_{2i}\oplus E_{ba}^{i}=M_{ba}^{i}\oplus U_{2i-1}\oplus E_{ab}^{i} \oplus E_{ba}^{i}, \tag{36}\] \[W_{2i}= U_{2i}\oplus E_{be}^{i}=M_{ba}^{i}\oplus U_{2i-1}\oplus E_{ab}^{i} \oplus E_{be}^{i}. \tag{37}\]
**(B2. 3)**: [PERSON] and [PERSON] have the knowledge of \(U_{2i-1}\) and \(W_{2i-1}\), respectively. Therefore, they can obtain the noisy version of \(M_{ba}^{i}\) by XOR \(U_{2i-1}\) and \(W_{2i-1}\) to (36) and (37). The \(\bar{M}_{ba}^{i}\) and \(Z_{be}^{i}\) can be received by  
\[\bar{M}_{ba}^{i}=V_{2i}\oplus U_{2i-1}=M_{ba}^{i}\oplus E_{ab}^{i}\oplus E_{ba }^{i}, \tag{38}\]  
and  
\[Z_{be}^{i}=W_{2i}\oplus W_{2i-1}=M_{ba}^{i}\oplus E_{ab}^{i}\oplus E_{ae}^{i} \oplus E_{be}^{i}. \tag{39}\]  
As shown in Figure 6, the secret sequence \(M\) is transmitted bidirectionally between [PERSON] and [PERSON] in our proposed scheme. The BER of the main channel and eavesdropper's channel in the \(i\)-th round is the same as that of the first round. Transmission rate after \(l\) rounds of the proposed AD is  
\[\eta_{bi}^{l}=\frac{2l-1}{2l},\]  
which is close to 1 when \(l\) is large enough.  
Figure 6: Proposed bidirectional advantage distillation scheme.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 3 Development of the MINLP Model Formulation for Truss Superstructures
### The MINLP Model Formulation for Truss Superstructures  
The above-mentioned general formulation MINLP-G was adopted for truss synthesis. As a result, the MINLP truss superstructure formulation MINLP-TS was developed. As a basis for the MINLP-TS formulation, the already developed continuous NLP truss optimization model was used. The NLP model proved successful for the continuous shape and sizing optimization of steel trusses [65], composite trusses [66] and timber trusses by considering joint flexibility [67].  
The proposed MINLP-TS model formulation for planar truss superstructures consists of the objective function, structural analysis and logical constraints with continuous and binary variables:  
Objective function:  
\[\min MASS=\rho\cdot\sum_{i=1}^{n}\quad\sum_{\begin{array}{c}j=1\\ el_{i,j}=1\end{array}}^{n}A_{i,j}^{\mathrm{sf}}\cdot s_{i,j}^{\mathrm{sf}} \tag{1}\]  
subjected to:  
Structural analysis constraints:  
Boundary constraints:  
\[u_{k,i}=0,\;k\in K,\;\forall\;i\in I|\mathbf{s}_{\mathrm{x},i}=1 \tag{2}\]  
\[v_{k,i}=0,\;k\in K,\;\forall\;i\in I|\mathbf{s}_{\mathrm{y},i}=1 \tag{3}\]  
\[d_{i,j}-\mathrm{d}_{i,j}^{\mathrm{ex,LO}}\cdot y_{i,j}^{\mathrm{top}}\geq 0,\ \forall\ (i,j)\Big{|}e |l_{i,j}=1 \tag{20}\]  
Logical relations for common variables:  
\[d_{i,j}\geq d_{g}^{\mathrm{com}}-\mathrm{d}_{i,j}^{\mathrm{UP}}\cdot\Big{(}1-y _{i,j}^{\mathrm{top}}\Big{)},\ \forall\ (g,i,j)\Big{|}gel_{g,i,j}=1 \tag{21}\]  
\[d_{i,j}\leq d_{g}^{\mathrm{com}}+\mathrm{d}_{i,j}^{\mathrm{UP}}\cdot\Big{(}1-y _{i,j}^{\mathrm{top}}\Big{)},\ \forall\ (g,i,j)\Big{|}gel_{g,i,j}=1 \tag{22}\]  
Logical relations for discrete dimensions:  
\[d_{i,j}=\sum_{l\in L}q_{i,j,l}\cdot y_{i,j,l}^{\mathrm{gt}},\ \forall\ (i,j)|e |l_{i,j}=1 \tag{23}\]  
\[\sum_{l\in L}y_{i,j,l}^{\mathrm{gt}}=y_{i,j}^{\mathrm{top}},\ \forall\ (i,j)|e |l_{i,j}=1 \tag{24}\]  
Variables:  
\[(\mathrm{d}_{i,j}^{\mathrm{LO}}=0)\leq d_{i,j}\leq\mathrm{d}_{i,j}^{\mathrm{UP }},\ \forall\ (i,j)\Big{|}e |l_{i,j}=1\]  
\[{}^{\mathrm{c}}\mathrm{x}_{i}^{\mathrm{LO}}\leq\ ^{\mathrm{c}}\mathrm{x}_{i} \leq\ ^{\mathrm{c}}\mathrm{x}_{i}^{\mathrm{UP}},\ i\in I\]  
\[{}^{\mathrm{c}}\mathrm{y}_{i}^{\mathrm{LO}}\leq\ ^{\mathrm{c}}\mathrm{y}_{i} \leq\ ^{\mathrm{c}}\mathrm{y}_{i}^{\mathrm{UP}},\ i\in I\]  
\[y_{i,j}^{\mathrm{top}}\in\{0,1\},\ \forall\ (i,j)\Big{|}e |l_{i,j}=1\]  
\[y_{i,j,l}^{\mathrm{gt}}\in\{0,1\},\ l\ \in\ L,\ \forall\ (i,j)\Big{|}e |l_{i,j}=1\]  
A detailed description of the MINLP-TS model formulation is given in the following sub-sections.
## 9 Conclusions  
In order to improve the measurement accuracy of the FOG-based MWD under conditions of drilling vibrations, this paper proposed an innovative scheme for the vibration error of FOGs-based MWD. First, a nonlinear error model was established. Second, a 36-order nonlinear EKF combined with a 24-position calibration method was designed. Additionally, in order to obtain more accurate error models, an iterative calibration method was put forward to suppress calibration residuals. Then a simulation experiment was carried out to verify the effectiveness and correctness of this proposed method. Next, a calibration experiment and vibration experiments under the lab conditions were accomplished. With the nonlinear EKF method, the coefficients of the linear and nonlinear error items could be calibrated correctly. The periodic and random vibration experiments verified the effectiveness of compensating nonlinear error items. Compared to the original data, the linear error items are compensated; the error of 3D borehole trajectory can only be reduced by a ratio from 10% to 34%. While the nonlinear error items of the FOG-based MWD were compensated, the error of 3D borehole trajectory can be reduced by a ratio from 44.44% to 97.22%. In conclusion, the compensated vibration nonlinear error items of the FOG-based MWD can improve the measurement accuracy under the process of drilling vibration. The new method can also be applied to other high dynamic fields.  
Conceptualization, L.W. and Y.H.; methodology, L.W. and B.L.; software, L.W. and T.W.; validation, L.W. and T.W.; writing--original draft preparation, L.W. and Y.H.; writing--review and editing, T.W. and B.L.; supervision, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 41902320, the Fundamental Research Funds for the Central Universities, grant number 2652018089, National Key Research and Development Program, grant number 2018 YFC0603405, and Key laboratory of deep GeoDrilling Technology, Ministry of Natural Resources 2018 Open Project Fund, grant number PY201801.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
**Acknowledgments:** The authors would like to express their appreciation to [PERSON] and [PERSON] at Beihang University who provided the experimental instrument for this research work. The authors would like to thank greatly [PERSON] and [PERSON] at the China University of Geoscience (Beijing) for their helpful discussions and comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 3 Results  
### Hyperparameter Tuning  
As was described, two DL models that were examined for their ability to detect diseases in grapevine plants. The training was performed using the data from the grapevine dataset of the Eden Library online dataset. The models that were considered were the MobileNet V2 and EfficientNet B0.  
For this reason, the first thing that had to be determined was the selection of the most suitable hyperparameters for the optimal training of the models. The grid search method involves exhaustively searching the set of the hyperparameter values that were defined in Table 3 to determine the best combination that yields the highest performance in the models. The results have shown that the [PERSON] optimization algorithm with 20 epochs and a batch size of 16 was chosen for both tested classifiers. The best dropout rate was found to be 0.2 for EfficientNet B0 and 0.1 for MobileNet V2 and, finally, the learning rate also varied for each model, with the most suitable value for EfficientNet B0 being \(10^{-3}\), and for MobileNet V2, being \(10^{-4}\).  
Additionally, in order to further optimise the grid search methods and the overall performance of the training process, the implementation of certain callbacks was involved. Two key callbacks utilised were the early stopping and model checkpoint. The early stopping callback monitors the validation loss, allowing for early termination of the training if no improvement is observed over a defined number of epochs (defined as \"patience\"). The patience level was chosen to be four epochs in this study. The model checkpoint callback was utilised to save the weights of the model at the end of each epoch, keeping every time the epoch with the best validation accuracy, ensuring that the best performing model will be preserved for use in the deployment of the model.
# Precipitation-Use Efficiency and Its Conversion with Climate Types in Mainland China
## 2 Materials and Methods  
### Study Area  
This study focused on mainland China. There are great climatic differences between east and west and south and north of China. Precipitation decreases from southeast to northwest, and climate transitions from humid to arid, presenting a basic pattern of humid in the east and south, but arid in the west and north, and there is a narrow zonal climate transition zone between humid and arid climate regions. We divided the study region into three sub-regions according to the distribution of average annual precipitation (P): the arid region (P < 200 mm), transition zone (600 > P \(\geq\) 200 mm), and humid region (P \(\geq\) 600 mm) [27]. The distribution of meteorological and flux stations and sub-regions is shown in Figure 1.  
Figure 1: Locations of 693 meteorological stations (black solid dots), 44 flux stations (red stars), and the three sub-regions (i.e., arid region, transition zone, and humid region) (**a**), and the Köppen-Geiger climatic zones (**b**).
# A High-Utility Differentially Private Mechanism for Space Information Networks
## 3 Proposed Mechanism
### Privacy Analyses  
This subsection analyzes the proposed mechanism in terms of the privacy guarantee. In particular, Theorem 1 analyzes the privacy guarantee aspect to the user, and Theorem 2 analyzes the privacy guarantee between each satellite and the master satellite. Theorem 1: _The proposed mechanism \(M\) is a \(e\) differentially private mechanism._  
Proof: For the output \(a\) of the proposed mechanism \(M\), we have  
\[ \begin{split} a&=\sum_{i=0}^{n}a_{i}\\ &=\sum_{i=0}^{n}(q(\bar{D_{i}})+\sqrt{B_{n-1}}l_{i})\\ &=\sum_{i=0}^{n}q(\bar{D_{i}})+\sqrt{B_{n-1}}\sum_{i=0}^{n}l_{i} \end{split}  \tag{10}\]  
Let \(\bar{D}=\bar{D_{1}}\cup\ldots\bar{D_{i}}\cup\ldots\bar{D_{n}}\) and \(l=\sqrt{B_{n-1}}\sum_{i=0}^{n}l_{i}\). Due to the linear property of query \(q\), we have \[a =q(\bar{D}_{1}\cup\ldots\bar{D_{i}}\cup\ldots\bar{D_{n}})+\sqrt{B_{n-1 }}\sum_{i=0}^{n}l_{i} \tag{11}\] \[=q(\bar{D})+l\]  
The record \(x\) is in a dataset, as indicated by the index \(+x\). For instance, the record \(x\) in \(\bar{D}\) is indicated by the notation \(\bar{D}_{+x}\). The record \(x\) is not in a dataset, as shown by the index \(-x\). Record \(x\), for instance, is not in \(\bar{D}\), which is shown by \(\bar{D}_{-x}\). In the case when the dataset acquired by the sampling procedure \(S\) is \(\bar{D}_{+x}\), let \(Lap(\bar{D}_{+x})\) represent the output \(a\). When \(x\) is in \(\bar{D}\), we have  
\[Lap(\bar{D}_{+x})=a=q(\bar{D}_{+x})+l \tag{12}\]  
Therefore,  
\[Pr\{Lap(\bar{D}_{+x})=a=q(\bar{D}_{+x})+l\}=Pr\{l=Lap(\bar{D}_{+x})-q(\bar{D} _{+x})\} \tag{13}\]  
Let \(c=max(c_{1},\ldots,c_{i},\ldots,c_{n})\). \(l\) complies with probability density function \(f(x)=\frac{\epsilon}{2c}e^{-\frac{c|x|}{c}}\) and the Laplace distribution. Therefore,  
\[Pr\{Lap(\bar{D}_{+x})=a=q(\bar{D}_{+x})+l\}=\frac{\epsilon}{2c}e^{-\frac{c|Lap (\bar{D}_{+x})-q(\bar{D}_{+x})|}{c}} \tag{14}\]  
Similarly, when \(x\) is not in \(\bar{D}\), we have  
\[Pr\{Lap(\bar{D}_{-x})=a=q(\bar{D}_{-x})+l\}=\frac{\epsilon}{2c}e^{-\frac{c|Lap (\bar{D}_{-x})-q(\bar{D}_{-x})|}{c}} \tag{15}\]  
Therefore, we have  
\[ \begin{array}{l}\frac{Pr\{Lap(\bar{D}_{-x})=a\}}{Pr\{Lap(\bar{D}_{+x})=a\}} \\ = e^{\frac{\epsilon}{2}(|Lap(\bar{D}_{+x})-q(\bar{D}_{+x})|-|Lap(\bar{D}_{-x}) -q(\bar{D}_{-x})|)}\\ = e^{\frac{\epsilon}{2}(|a-q(\bar{D}_{+x})|-|a-q(\bar{D}_{-x})|)}\\ \leq e^{\frac{\epsilon}{2}(|a-q(\bar{D}_{+x})-a+q(\bar{D}_{-x})|)}\\ = e^{\frac{\epsilon}{2}(|q(\bar{D}_{-x})-q(\bar{D}_{+x})|)}\\ \leq e^{\frac{\epsilon}{2}LRS_{x}}\end{array}  \tag{16}\]  
For the symmetry, we have  
\[\frac{Pr\{Lap(\bar{D}_{+x})=a\}}{Pr\{Lap(\bar{D}_{-x})=a\}}\leq e^{\frac{ \epsilon}{2}LRS_{x}} \tag{17}\]  
Let \(D=D_{1}\cup\ldots D_{i}\cdots\cup D_{n}\). Without loss of generality, we assume \(x\in D_{i}\). For the proposed mechanism \(M\), we have \[
## 5 Molecular Imprinting by Sol-Gel Derived Techniques
The sol-gel solution was prepared by mixing PTEOS, TEOS, trifluoroacetic acid (TFA) and the template in ethanol/water. After homogenization, pyrrole and LiClO\({}_{4}\) were added to this solution as well. The ITO modified electrode was immersed in this precursor solution and the film was obtained by electrodeposition in the \(-0.8\) and 0.4 V potential range, for 10 scan cycles at a scan rate of 50 mV/s. CV and DPV were used to evaluate the performance of the MIP film, resulting in a limit of detection of 0.02 uM. Moreover, the sensor presented a good selectivity when contacting compounds with similar structures [158]. Another interesting approach for MIP-based sensors was presented by [PERSON] and his co-workers [159] for mephedrone detection. Mephedrone is a stimulant drug from the class of amphetamines. In their study, a precursor solution containing PTEOS, TEOS, H\({}_{2}\)O, ethanol, TFA and mephedrone was prepared, after which another solution containing tyramine, MWCNT@AuNPs nanocomposite and sodium dodecyl sulfate(SDS) was added. The electrode was placed into this final solution, and the MIP films were obtained by electrodeposition (potential between \(-0.8\) and 1.2 V at 50 mV/s). The synthesized films were studied by different techniques. The ability of the films to detect the mephedrone was quantified by SWV, which indicated a detection limit of 0.8 nM [159]. In 2018, [PERSON] and colleagues [160] developed an MIP-based sensor for diethylstilbestrol detection. This compound is used as a growth promoter for animals and produces negative effects on humans. During the experiments, a classical three-electrode system containing a GCE (working electrode), an Ag/AgCl (reference electrode) and a Pt electrode (counter electrode) was used. In this study, the MIP film was deposited on the GCE after its modification with MWCNTs, chitosan and AuNPs. For the synthesis of films, a precursor MIP solution was prepared from APTES, OTOMS (octyltriethoxysilane), TEOS, template, 2-ethoxyethanol/ water and hydrochloric acid as catalyst. Subsequently, the electrode was immersed in this solution to obtain the MIP film by electrodeposition (potential range \(-0.5\) and 0.5 V). The electrochemical behavior of the films was studied by DPV, resulting a low LOD of 24.3 fg/mL [162]. [PERSON] and colleagues [161] proposed an electrochemical MIP-based sensor for determining the content of ketamine. In this respect, sol-gel films were deposited on the surface of a pencil graphite electrode. The precursor solution contained TEOS, PTEOS, water and ethanol mixed with a solution containing TFA and ketamine. After 2 h, this solution was mixed with another solution containing tyramine, SDS and MWCNT@AuNPs in DMF, and afterwards used for film electrodeposition, by cyclic voltammetry (potential range \(-0.8\) and 1.2 V for 10 scan cycles). The characteristics of the films were studied using EIS and SWV, in which case a limit of detection of 0.7 nM was registered. Moreover, the sensors presented reproducibility, sensitivity and selectivity [161].
## 6 Methodology of Sample Implementation
### Implementation of VR Conferencing
#### 6.1.2. Scenes, Avatars, and Interactions  
To simplify the workload, the modeling of the scene [51] and avatar [52] were from free resources on the Internet. Figure 5 shows how our VR conferencing room looks. Figure 6 is the avatar of the user. After a participant enters the virtual conference room, they are assigned to a random seat. At this time, they can change the direction of view and observe the surrounding environment by moving the mouse or rotating the HMD (head-mounted display). When another participant also enters the conference room, the two participants can see each other in the scene and talk to each other by voice. The view of a user is shown in Figure 7. The sound effect produces a real sense of space and orientation. When there are multiple participants in the conference room, everyone can feel
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 3 Design of a Vision-Based Deep Learning Loading-Arm Positioning System
### Description of the Positioning System
#### 3.1.1 Overview of Hardware Equipment  
* Monocular camera A: It is installed on the loading arm column, about 2 m above the ground, and is mainly used for the initial positioning of the target flange during the coarse docking phase. The camera has a wide field of view and can detect the target over a large area. Combined with the YOLOv8 model, it can complete flange target detection and obtain 3D spatial information.  
Figure 7: (**a**) Monocular camera A. (**b**) Monocular camera B and ultrasonic sensor.  
* Monocular camera B: It is installed near the end of the loading arm at an angle and is dedicated to the identification and alignment of the flange during the fine alignment stage. The flange is accurately segmented using a YOLOv8 model to obtain the target 3D spatial data.
* Ultrasonic sensor: The end of the loading arm is equipped with four ultrasonic sensors that monitor the distance between the upper, lower, left and right edges of the flange in real time. These sensors provide fine distance data, effectively supplementing the visual inspection information from the camera to improve the reliability and accuracy of precise docking.
# A Multi-Stage Method for Logo Detection in Scanned Official Documents Based on Image Processing
## 3 Materials and Methods
### Region Feature Vectors  
In order to perform classification over the test set, a feature vector for each candidate logo region was extracted. The features that we used for this vector were the Euler number, region area, connected components, and aspect ratio. These attributes are calculated based on relative measures and not absolute ones, making the size of the logo not relevant for its detection.  
**Euler number:** The Euler number of a binary image is defined as the total number of objects in an image minus the total number of holes in those objects. The Euler number has been previously used in OCR applications [35] to distinguish between letters and non-letters, and we use it in this paper to distinguish between logos and non-logos, which are expected to be formed by letters (signatures, body text, dates, etc.) with a very high probability because we are working in the domain of official documents.  
**Region area:** We define the region area of a candidate as follows, where we used the value of the width and height of our region:  
**Connected components:** Let \(B\) be a binary image, and let \(B(x,y)=B(x^{\prime},y^{\prime})=v\), where \(v=\{0,1\}\). Then, the pixel \((x,y)\) is connected to the pixel \((x^{\prime},y^{\prime})\) with respect to \(v\) if there is a sequence of pixels \((x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\), all of them equal to \((x^{\prime},y^{\prime})\), that forms a _connected path_ from \((x,y)\) to \((x^{\prime},y^{\prime})\). A _connected component_ in \(B\) is a set of pixels \(C=\{(x,y)\in B\mid(x,y)\text{ and }(x^{\prime},y^{\prime})\text{ connected with respect to }v\}\).  
A connected components labeling of \(B\) is another image \(LB\) in which the value of each pixel is the label of its connected component. In our experiment, we extracted connected components with a neighborhood of 8 pixels.  
**Aspect ratio:** This is the relationship between the image width and height.
## 2 Materials and Methods
### Standard Solutions, Calibration Standards and Quality Control (QC) Samples  
Stock solutions of ERDA were prepared by dissolving 5.0 mg of ERDA in DMSO to yield a concentration of 2.0 mg/mL. Separate working solutions (100 ug/mL) for calibration curves (CCs) and quality control (QC) samples were prepared by further dilution of the stock solution with methanol. IS stock solution (1.0 mg/mL) and its working solution (100 ug/mL) were prepared with the same method as ERDA. Then, the working standards of CCs and QC samples were spiked to blank human plasma samples to attain the CCs range of 0.5-1000 ng/mL and QC samples (1.5, 150 and 750). Spiked plasma samples were kept in a deep freezer at \(-80\)\({}^{\circ}\)C until further analysis while the solutions were stored in a refrigerator.
## 3 Results and Discussion
### Performances Evaluation of \(p\)-DAS on Image Quality  
In this subsection, the image qualities obtained with the DAS and \(p\)-DAS (\(p=2\) and \(p=3\)) beamformers are investigated, in order to understand the influence of the value of \(p\) on images. The images obtained for single plane wave imaging (1 PW), and 11 plane wave imaging (11 PW) are presented and analyzed.