esa-sceva/satcom-chunk-collection huggingface dataset:
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
## 2 Antenna Design and Performance
### 1 \(\times\) 2 Array Antenna Design and Performance
Therefore, we inserted optimized slits (Slit 2) on the edge of the ground plane to disturb the flow of the surface current, as shown in Figure 11a,b. The mutual coupling on the substrate between the directors is reduced due to the weakened electromagnetic coupling by disturbing the flow of the surface current at the edge of the ground plane. The mutual coupling reduction can be shown by the simulated E-field distribution in the E-plane (_xy_ plane) at 28 GHz. As shown in Figure 9c, without the slits, the mutual coupling is mainly due to the propagation of the surface wave along the \(y\) direction from port 1 to port 2. By inserting only Slit 1, the surface wave propagation between the radiators is suppressed, as shown in Figure 10c. In Figure 11c, the surface wave propagation is further suppressed by inserting both Slit 1 and Slit 2. Figure 12a shows the simulated S\({}_{11}\) and S\({}_{21}\) of the \(1\times 2\) array antenna with directors considering the slits. The simulated \(-10\) dB S\({}_{11}\) of all the cases cover the target band. The simulated S\({}_{21}\) of the antenna without the slits is lower than \(-11.85\) dB over the target band. However, the simulated S\({}_{21}\) of the antenna with only Slit 1 is lower than \(-15.91\) dB over the target band. In the case of the antenna with both Slit 1 and Slit 2, the simulated S\({}_{21}\) is lower than \(-19\) dB over the target band. Figure 12b shows the simulated radiation patterns of the antenna with and without the slits. The simulated gains of the antenna without slits, with only Slit 1, and with both Slit 1 and Slit 2 in the \(+z\) direction are 1.12, 2.48 and 5.02 dBi, respectively. However, the gains in the \(-z\) direction in all cases remain almost the same, around 0.98 dBi. Therefore, the slits contribute to reduce the mutual coupling and enhance the radiation performance. Figure 4:0 The \(1\times 2\) array antenna with only Slit 1: (**a**) top view, (**b**) simulated surface current distribution at 28 GHz, and (**c**) simulated E-field distribution in the E-plane at 28 GHz.
# Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions
## 3 Experiment and Results
### Experimental Key Indicators  
In this paper, the performance of the original YOLOv5 model and YOLOv5_Ours model is evaluated based on Precision, Recall, F1-score, AP (average precision), and mAP (mean average precision).  
* Precision: \[\text{Precision}=\frac{\text{TP}(\text{True Positive})}{\text{TP}(\text{True Positive})+\text{FP}(\text{False Positive})}=\frac{\text{TP}(\text{True Positive})}{\text{All Detections}}\] (8)
* Recall: \[\text{Recall}=\frac{\text{TP}(\text{True Positive})}{\text{TP}(\text{True Positive})+\text{FN}(\text{False Nagative})}=\frac{\text{TP}(\text{True Positive})}{\text{All Ground Truths}}\] (9)  
Figure 10: The structure of experimental method.  
Precision refers to the percentage of all detection results that are correctly detected. Recall is used to indicate how well a positive prediction is made when a positive input is given. Simply put, it means how well model detect it.  
TP (True Positive) is a number detected to fit an object. FP (False Positive) means that it is detected as an object of another class. In other words, it is a false detection. FN (False Negative) means an object that should have been detected but not detected, and the TN (True Negative) means nothing that should not be detected.  
* F1-score: \[\text{F1-score}=\frac{2*\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}}\] (10)  
It is calculated as the harmonic mean of precision and recall and not the arithmetic mean. F1-score has a value between zero and 1; the higher the value, the higher the accuracy of detecting an object. mAP (mean average precision) is the average value of the AP (average precision), indicating how accurate the predicted result is.  
* AP: \[\text{AP}=\int\limits_{0}^{1}P(R)dR\] (11)
* mAP: \[\text{mAP}=\frac{1}{|\mathcal{Q}_{R}|}\sum\limits_{q=Q_{R}}\text{AP}(q)\] (12)
## 2 Trustworthy Assumptions and Threat Models
These two examples indicated that the instruction register (IR) and the instruction bus to IR in CPU were vulnerable to hardware Trojan attacks; therefore, their instruction code faults should be detected and repaired. * The second situation is that instructions were maliciously modified in I-Cache instead of processor core. The hardware designers in [6; 22] were developing specific hardware Trojans towards CPU cache memories to modify the cached instruction codes, here, the instruction modifications in I-Cache are manifested as the data bit flips. * The third situation is that instruction codes were modified in the instruction memory (such as flash memory) induced by hardware Trojans, where the main memory was hidden for the sake of brevity. For example, the designers in [23; 24] were leaning flash-memory-oriented hardware Trojans to tamper with the instructions in non-volatile memory. * The fourth situation is that instruction codes that were modified by artificial intentions arose from the program code phase (including software and application) before compiling and linking. For example, the programs in C and C++ programming languages were modified via malicious code injections to take control of hardware platforms [25]. The above-mentioned four threat models are assumed according to the system's potential locations where instruction tampering attacks may occur, which is induced by both human and hardware Trojans. Hence, an instruction monitor is implemented into the SPU to monitor the instruction execution security in real-time. For convenience, the instruction tampering detection can be normalized to aim at the instruction register (IR) monitoring, which is the final phase of instruction codes before entering the CPU's execution pipeline, this monitoring mechanism can not only effectively guarantee secure program executions against the above-mentioned four threat models but also facilitates hardware implementation of the instruction FSRM. The post-detection strategy of the instruction fault self-repairing scheme is tightly coupled with the real-time instruction integrity monitoring. When a tampered instruction is executed and detected, the self-repairing is triggered and reverts to the recently-saved checkpoint for resuming normal program execution. This recovery scheme is adopted based on the strong randomness of hardware Trojan activation. It is noteworthy that source program tampering by an adversary before compiling and linking processes can also be detected and reported but cannot be self-repaired by the checkpoint-rollback scheme.
# A Reconfigurable Radio-Frequency Converter IC in 0.18 \(\upmu\)m CMOS
## 4 System Implementation and Validation  
This section presents the results obtained from the experimental characterization of the proposed system, as well as for its main building blocks, which have been laid out and fabricated as separate test blocks. The technology used for this development is a cost-effective standard 0.18 \(\upmu\)m CMOS process supplied by TSMC, fed at 1.8 V and 2.5 V, respectively, for its digital and analog blocks.  
A micro-photograph of the full system with its different parts highlighted is presented in Figure 7. It occupies 2.5 mm \(\times\) 2.5 mm and it has been encapsulated in a QFN-32 package for on-board testing. Figure 7 shows a photograph of the PCB fabricated for the experimental characterization of the reconfigurable radio-frequency converter. In the following sections, a description of the main characteristics of the measured operation of the whole system and its main building blocks (i.e., frequency synthesizer and mixers) will be presented.
# Survey of Blockchain-Based Applications for IoT
###### Abstract  
The rapid growth of the Internet of Things (IoT) has introduced critical challenges related to security, scalability, and data integrity. Blockchain technology, with its decentralized, immutable, and tamper-resistant framework, presents a transformative solution to address these challenges. This study explores blockchain applications in the IoT, focusing on security, automation, scalability, and data sharing. Industry-specific applications, including supply chain management, smart cities, and healthcare, highlight the potential of blockchains to optimize operations, ensure compliance, and foster innovation. Additionally, blockchain technology enables robust audit trails, enhances accountability, and reduces fraud in sensitive IoT applications, such as finance and healthcare. The synergy between blockchains and the IoT creates a secure and transparent platform for managing device interoperability and data exchange, fostering seamless communication between diverse IoT components. Furthermore, this paper discusses layer 2 scaling techniques and tokenization to address scalability, ownership, monetization, and cost challenges, providing practical solutions for real-world deployments. Future directions emphasize integrating blockchain systems with artificial intelligence (AI), machine learning (ML), and edge computing, offering groundbreaking capabilities to further revolutionize IoT ecosystems. By merging these advanced technologies, organizations can build secure, scalable, and intelligent systems to drive innovation and trust.  
Internet of Things (IoT); blockchain; decentralized ledger; immutability; tamper resistance; blockchain-enabled federated learning; privacy-preserving machine learning; smart contracts; secure data sharing; IoT analytics; consensus mechanisms; tokenization; tokenomics +
Footnote †: journal: Applied Sciences
## 3 Results and Analysis
### Compressive Strength  
After 28 days of curing, the concrete compressive strength was enhanced up to 74 MPa, depending on the content of cement and the types and content of water-reducer additives. In the case of polycarboxylate-based superplasticizers and naphthalene- or melamine-based superplasticizers, electrostatic and steric repulsion mechanisms work together to weaken the cohesiveness of the cement particles.  
The compressive strength of concrete was predicted using nonlinear and M5P models based on data from 483 tests using three distinct mixtures and three different water-reducer additives, as shown in Figure 6. Additionally, it explores how mixed proportions affect concrete compressive strength.  
Figure 6: The mix proportions’ impact on the concrete compressive strength using (**a**) nonlinear and (**b**) M5P models.
## 3 Proof of Stability
### Tracking Error Convergence Proof  
The system output error is defined as:  
\[\mathbf{e}(k)=\mathbf{y}^{*}-\mathbf{y}(k). \tag{8}\]  
When substituting Equations (6) and (7) into (8), we obtain:  
\[\mathbf{e}(k+1)=\mathbf{e}(k)-\mathbf{\[PERSON]}_{c}(k)\Delta\mathbf{u}(k)=\Bigg{[}\mathbf{I}-\frac{ \rho\mathbf{\Phi}_{c}(k)\mathbf{\hat{\Phi}}_{c}{}^{T}(k)}{\lambda+\left\|\mathbf{\hat{\Phi }}_{c}(k)\right\|^{2}}\Bigg{]}\mathbf{e}(k). \tag{9}\]  
Based on the conclusion of the matrix spectral radius, there exists an arbitrarily small positive number \(\varepsilon_{1}\), such that:  
\[\left\|\mathbf{I}-\frac{\rho\mathbf{\Phi}_{c}(k)\mathbf{\hat{\Phi}}_{c}{}^{T}(k)}{\lambda +\left\|\mathbf{\hat{\Phi}}_{c}(k)\right\|^{2}}\right\|_{y}<s\Bigg{(}\mathbf{I}-\frac{ \rho\mathbf{\Phi}_{c}(k)\mathbf{\hat{\Phi}}_{c}{}^{T}(k)}{\lambda+\left\|\mathbf{\hat{\Phi }}_{c}(k)\right\|^{2}}\Bigg{)}+\varepsilon_{1}\leq 1-\rho M_{1}+\varepsilon_{1}<1, \tag{10}\]where \(\|\mathbf{A}\|_{v}\) is the compatible norm of the matrix \(\mathbf{A}\). Letting \(d_{2}=1-\rho M_{1}+\varepsilon_{1}\) and taking the norm on both sides of the formula, we obtain:  
\[\left\|\boldsymbol{e}(k+1)\right\|_{v}\leq\left[\boldsymbol{I}-\frac{\rho \boldsymbol{\Phi}_{c}(k)\boldsymbol{\hat{\Phi}_{c}}^{T}(k)}{\lambda+\| \boldsymbol{\hat{\Phi}_{c}}(k)\|^{2}}\right]_{v}\left\|\boldsymbol{e}(k)\right\| _{v}\leq d_{2}\|\boldsymbol{e}(k)\|_{v}\leq\ldots\leq d_{2}^{k}\|\boldsymbol{e} (1)\|_{v}. \tag{11}\]  
Through the above calculation, the system tracking error sequence convergence is proven.
## 4 Conclusions  
The significant variability of the different methods used to estimate and compare the effective dose in CT exams was identified in this study. The effective dose calculated using the tissue-weighting factors of ICRP 103 values comparable to ICRP 60 differed significantly by an average of 2.3, 2.9, and 4.5 mSv for the head, chest, and abdomen, respectively. The use of coefficient factors to convert DLP to E\({}_{\text{d}}\) resulted in an underestimation of the effective dose relative to the E\({}_{60}\) calculated using CT-Expo, E\({}_{60}\), and E\({}_{103}\) by 45-54%, 55-59%, and 77-79%, respectively. When the recommended ICRP 103 was used instead of ICRP 60 and tissue weighting factors were used to calculate the effective dose, the abdomen and the chest effective doses increased by 1.39 mSv due to the increase in the tissue weighting factors. Monte Carlo simulation is regarded as the most comprehensive computational method for estimating organ and tissue radiation doses. When compared to organ dose-based estimations, the calculated effective dose values calculated using Monte Carlo were lower in this study for all of the organs under investigation. Because all of the methods used to estimate the effective dose are energy-independent and subject to the biases imposed by the assumptions of the phantom model, scanner geometry, beam quality, and exam input variables, no significant agreement was found. The study's novelty shows that the variability associated with the employment of different methodologies to estimate and compare the effective dose \(\mathrm{E_{d}}\) in CT exams is significant.  
Conceptualization, E.A., A.A. and A.T.; Methodology, A.T., O.M., E.A. and A.A.; Validation, O.M. and S.A.; Software, E.A., E.O. and M.E.; Analysis, A.M., E.A., W.A. and A.A.; Investigation, S.A., R.A., A.T., E.A., M.A. and R.A.; Writing--original draft and preparation, E.A., A.T., A.A. and O.M.; Writing--review and editing, R.A., S.A., E.A., A.M., A.A.-O. and A.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This study was approved by the IRB Committee at Imam Abdulrahman bin Faisal University, reference number: IRB-2022-03-122. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data will be available on request. The authors would like to thank [PERSON], [PERSON], and [PERSON] for their help in this work. The authors declare no conflict of interest.
# A Method for the Calibration of a LiDAR and Fisheye Camera System
## 2 Materials and Methods
### Three-Dimensional Camera Coordinates
#### 2.2.3 System of Equations  
The final objective is to find the coordinates of the four points in space, corresponding to the corners of the reference rectangle, providing a total of twelve unknowns, three coordinates for each of the four points. The equations of the lines developed in Equation (2) provide a total of two equations per point (3), which makes a total of eight equations for the four corners of the rectangle.  
Since it is a plane, a new equation can be created to restrict that all the points belong to the same plane. To achieve this, the following Equation (4) is proposed:  
\[\begin{vmatrix}x_{1}-x_{2}&x_{1}-x_{3}&x_{1}-x_{4}\\ y_{1}-y_{2}&y_{1}-y_{3}&y_{1}-y_{4}\\ z_{1}-z_{2}&z_{1}-z_{3}&z_{1}-z_{4}\end{vmatrix}=0 \tag{4}\]  
As discussed above, the dimensions of the rectangular polygon (width \(w\), height \(h\), and diagonal \(d\)) are known, so a total of six equations can be stated (5) based on the distances between the points:  
\[\begin{cases}w^{2}=(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}+(z_{1}-z_{2})^{2}\\ w^{2}=(x_{3}-x_{4})^{2}+(y_{3}-y_{4})^{2}+(z_{3}-z_{4})^{2}\\ h^{2}=(x_{1}-x_{3})^{2}+(y_{1}-y_{3})^{2}+(z_{1}-z_{3})^{2}\\ h^{2}=(x_{2}-x_{4})^{2}+(y_{2}-y_{4})^{2}+(z_{2}-z_{4})^{2}\\ d^{2}=(x_{1}-x_{4})^{2}+(y_{1}-y_{4})^{2}+(z_{1}-z_{4})^{2}\\ d^{2}=(x_{2}-x_{3})^{2}+(y_{2}-y_{3})^{2}+(z_{2}-z_{3})^{2}\end{cases} \tag{5}\]  
Finally, a total of 15 equations (8 equations from (3), 1 from (4), and 6 from (5)) for 12 unknowns have been formulated, resulting in an overdetermined system. The system can be solved by least squares. In this case, the function optimize.fsolve from the Scipy Python library is used, which requires an initial estimation of the roots. The 3D coordinates of the corners obtained from the LiDAR are chosen as the initial estimation, thus eliminating negative solutions on the depth axis of the LiDAR reference system. Thus, the system of equations is solved, obtaining the 3D coordinates of the corners of the rectangle in the camera reference system, \((x_{i},y_{i},z_{i})\forall i\in[1,2,3,4]\).  
It should be pointed out that this initial estimation could not be used to solve the system if the LiDAR and the camera had a different orientation.
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 4 Experiments
### Experimental Setups
#### 4.1.2 Evaluation Criteria  
In order to explain the consistency between objective evaluation and HVS, it is mainly evaluated from the following two aspects: first, accuracy, that is, there is little difference between the results obtained by objective quality evaluation method and subjective judgment. The second is monotonicity. Judging the quality of an image subjectively is consistent with the quality evaluation results obtained by objective methods. The following four evaluation indexes are used for objective quality assessment [36]. Spearman rank order correlation coefficient (SROCC) and Kendall rank order correlation coefficient (KRCC) are used to measure the prediction monotonicity, Pearson linear correlation coefficient (PLCC), and root mean squared error (RMSE) are calculated after the suggested monotonic logistic mapping to measure the prediction accuracy.  
For the calculation of PLCC and RMSE, regression analysis is used to provide a nonlinear mapping between the objective scores and the subjective mean opinion scores (MOS). For the nonlinear regression, the following mapping function suggested by [PERSON] et al. [37] is used.  
\[f(x)=\beta_{1}\bigg{(}\frac{1}{2}-\frac{1}{1+e^{\beta_{2}(x-\beta_{3})}}+\beta _{4}x+\beta_{5}\bigg{)} \tag{15}\]  
where \(x\) denotes the original objective score, and \(\beta_{i}(i=1,2,3,4,5)\) are regression model parameters to be fitted. A good objective evaluation algorithm has higher SROCC, KROCC, and PLCC, and lower RMSE.
# Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM
## 4 Conclusions  
In summary, we successfully established an efficient and accurate multi-objective optimization model and applied it to actual drilling operations. We established the PSO-Bi-LSTM algorithm to optimize the controllable parameters in the drilling process. The optimization goal of this algorithm is to maximize ROP and minimize MSE, aiming at minimizing the energy loss of the bit while ensuring the maximum drilling speed.  
In the experiment of optimizing the drilling parameters of a well in Xinjiang, the optimized result is that the optimized WOB is between 92.38 KN and 113 KN, the optimized flow rate is between 29 L/s and 30.35 L/s, and the optimized rotation speed is between 25 r/min and 34 r/min. The optimization results show that the drilling speed is increased by 81% and the energy loss of the bit is reduced by 28%. The optimization effect is good, achieving the goal of multi-objective controllable parameter optimization.  
This work provides an intelligent model basis for multi-objective optimization of drilling parameters, expands the application of the combination of multi-parameter search algorithms in high dimensional space and neural network models in the field of oil drilling, and provides new ideas and methods for reducing costs and increasing efficiency in drilling operations.  
Conceptualization, J.W. and X.S.; methodology, J.W. and Z.Y.; software, Z.Y.; validation, T.P., Z.Z. and D.Y.; formal analysis, T.P.; investigation, Z.Y.; resources, T.P.; data curation, Z.Z.; writing--original draft preparation, Z.Y.; writing--review and editing, X.S.; visualization, D.Y.; supervision, X.S.; project administration, J.W.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support of the Strategic Cooperation Technology Projects of CNPC and CUPB (No. ZLZX2020-03), the National Key Research and Development Program (No. 2019 YFA0708300), the National Science Fund for Distinguished Young Scholars (No. 52125401), China Petroleum Innovation Fund Project (No. 2022 DQ02-0308) and the Science Foundation of China University of Petroleum, Beijing (No. 2462022 SZBH002).  
Not applicable.  
The authors declare no conflict of interest.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 2 Related Works
### Attention Mechanisms  
The attention mechanism is a crucial factor in boosting performance by allowing the network to focus on essential parts. Vision transformers [30, 31] divide an image into a sequence of patches to capture the relationships between these patches. Although they have demonstrated promising performance across various vision tasks, they have the drawback of requiring substantial computational resources. To address this issue, the Swin transformer [32] introduced window-based local attention that learns the interactions between elements within shifted windows. [PERSON] [33] proposed pooling attention that applies individual h-axis and w-axis pooling operations. Restormer [7] effectively restored clean images by designing attention in the channel direction. SegNeXt [16] was developed with convolutional attention using multi-branch depthwise strip convolutions and achieved performance comparable to that of transformer-based models. In this study, we designed a novel attention mechanism that integrates the advantages of both POTTER and SegNeXt. Our attention compresses features into global statistical information via unidirectional pooling operations and then effectively aggregates spatial information using multi-scale, depthwise strip convolutions.
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Data from Ratio Index Parameters (DRIPs)  
Table 3 presents the performance evaluation of three machine learning algorithms (Gradient Boosting, XGBoost, and Random Forest) based on data from the ratio index parameters. The results show that:  
Random Forest shows the best overall performance with an accuracy of 30.72%, an F1 score of 30.54%, and an MCC of 14.48% while maintaining the shortest execution time (2.34 ms).  
Figure 7 illustrates the ROC curve and the confusion matrix of the model that achieved the highest accuracy.