esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 3 Results and Discussion  
### Pre-Processing  
Within this phase, the tiling process was applied to Dataset 1 and Dataset 2 using the OpenCV environment. Then, masking was performed to exclude regions that were not marine areas. After these steps, 37,570 images were obtained for Dataset 1, while 8320 were obtained for Dataset 2 within the relevant ports in Table 1. A subsampling process was then applied to Dataset 1 to generate HR-LR image pairs for the super-resolution model. During this subsampling, the size of the images was reduced four times using the Bicubic subsampling, resulting in HR-LR image pairs with spatial resolutions of 12 m and 3 m, respectively. Then, the splitting process was applied to Dataset 1 and Dataset 2. For Dataset 1, 70% was used for training and 30% for validation. For Dataset 2, 70% was used for training, 20% for validation, and 10% for testing. Detailed information about Dataset 1 and Dataset 2 is presented in Table 2 and Table 3, respectively.
# An On-Demand Fault-Tolerant Routing Strategy for Secure Key Distribution Network
## 6 Conclusions  
In this paper, we propose a practical on-demand fault-tolerant routing strategy to improve the availability and reliability of communication key distribution in the presence of network layer fault. We consider a combination of path discovery and fault detection mechanisms to balance the effectiveness and fault tolerance of SKDN. In particular, the strategy adopts a fault-free on-demand path discovery and selects the appropriate path for key forwarding based on the local key status. In addition, an acknowledgment-based fault detection mechanism is integrated during the distribution process to locate abnormal links, and the identification accuracy is improved by identifying possible causes based on DH evidence theory. The system's reliability is enhanced by varying responses to different causes. The simulation results demonstrate the effectiveness and scalability of the proposed solution compared to comparative methods under different faulty node ratios. Moreover, the proposed solution has a relatively low local key overhead, indicating certain practicability. In future work, we will consider the impact of changes in bandwidth resources, including the reduction of fault identification accuracy. Additionally, a solution to dynamically join and exit nodes is considered to prevent delays in path information dissemination. We will incorporate additional evidence from other intrusion detection systems and reputation systems to analyze network status more comprehensively, and develop a more refined exception response mechanism to further enhance the fault tolerance of SKDN.  
Conceptualization, Z.W. and Y.L.; Funding acquisition, H.D.; investigation, Z.W.; Methodology, Z.W., H.D. and Y.L.; software, Z.W.; supervision, Y.L. and H.D.; validation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W., Y.L. and H.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Strategic Priority Research Program of Chinese Academy of Sciences: Information Collaborative Service and Data Sharing (Grant No. XDA031050100).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data are contained within the article.  
Figure 12: Hop count comparison of the proposed solutions in the BHA scenario.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 4 Experiments and Results
### Data Preprocessing  
The blood smear images were first preprocessed by four stages: image augmentation, enlarging, sharpening, and blurring.  
To increase the number and diversity of the images, we not only applied horizontal flipping, vertical flipping, and rotation to the original images but also converted them from RGB color space into grayscale. Since it is difficult to detect small objects, such as platelets, we also used bicubic interpolation [48] to enlarge the images.  
We then utilized the idea of unsharp masking to sharpen the blurry images. The procedure of unsharp masking can be divided into three phases: blur the original image, acquire the mask by deducting the blurred image from the original, and add a weighted portion of the mask back to the original [49]. In this paper, we computed the mask of an image by applying a 3 \(\times\) 3 OpenCV built-in Laplacian filter and then removed a weighted portion of the mask with a parameter \(k\) from the image, shown as Equation (6).  
\[f_{sharpen}(x,y)=f_{original}(x,y)-k\times f_{mask}(x,y) \tag{6}\]  
We then blurred the images with a 3 \(\times\) 3 Gaussian kernel to make them smoother.
## 2 Background and Theory
### Classification Algorithms
#### 2.3.4 SVM Polynomial  
A polynomial kernel-based support vector machine (SVM) [56] is employed as the fourth supervised learning classifier. This model effectively handles nonlinear re lationships between EEG features and their categories by projecting data into a higher-dimensional space using a polynomial kernel. The kernel function is mathematically described in Equation (27):  
\[K\big{(}\mathbf{x},\mathbf{x}^{\prime}\big{)}=\Big{(}\gamma\,\mathbf{x}^{\top}\mathbf{x}^{\prime }+r\Big{)}^{d} \tag{27}\]  
In this context, \(K(\mathbf{x},\mathbf{x}^{\prime})\) represents the kernel function, which quantifies the similarity between two feature vectors. The variables \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\) correspond to input feature vectors extracted from EEG signals. The parameter \(\gamma\) serves as a scaling factor, adjusting the impact of different features within the kernel function. Additionally, \(r\) acts as a constant that balances the contribution of both lower-order terms and higher-order terms. To determine the polynomial degree, the value of \(d\) is used, which influences the complexity of the decision boundary.  
Within the SVM framework utilizing a polynomial kernel, the decision function is formulated in Equation (28):  
\[f(x)=\sum_{i=1}^{N}\alpha_{i}y_{i}K(x_{i},x)+b \tag{28}\]  
In this context, \(N\) represents the number of support vectors chosen during training, while \(\alpha_{i}\) denotes the Lagrange multipliers derived from solving the SVM optimization problem. The variables \(y_{i}\) correspond to the class labels assigned to the training samples, and \(x_{i}\) refers to the support vectors extracted from the training dataset. Finally, \(b\) serves as the bias term or intercept, adjusting the decision boundary accordingly.
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## 4 Results
### Real-World Datasets  
#### 4.2.1 Registration Accuracy Metrics  
As summarized in Table 4, HiGoReg and the batch method achieved identical registration accuracy on the WHU-TLS subway station dataset across varying voxel sizes, demonstrating the reliability of HiGoReg. At a voxel size of 0.03 m, both methods reached an RMSE of 0.0240 m, with a posterior unit-weight standard deviation of 0.0078 m, a rotation error of \(2.8612\times 10^{-4}\) rad, and a translation error of 0.0041 m, confirming consistent and precise alignment. When the voxel size increased to 0.05 m, a marginal increase in RMSE (0.0243 m) and translation error (0.0044 m) was observed, along with a rise in the number of iterations from 5 to 6. This slight performance degradation was attributed to reduced feature quality due to the coarser voxel grid.  
For the statue dataset, Table 5 further confirms the agreement between the two approaches under two different stochastic modeling strategies. Under equal weighting, both methods yielded identical accuracy metrics. When nominal accuracy weighting was applied to reflect scanner precision, a slight improvement in RMSE and posterior variance was observed (with only a 1% difference in RMSE, see Figure 11); nevertheless, the results remained consistent across methods. This similarity stemmed from the close proximity of the TLS to the target and the inherently high precision of the point cloud data, which diminished the influence of the weighting strategy on registration accuracy. These findings reinforce the robustness of HiGoReg under varying real-world noise and data conditions.  
Compared with the simulation experiments, the registration error in the real-world dataset was relatively higher. This was primarily due to the presence of noise and occlusion in the real-world data, which could lead to missing regions and mismatched feature point pairs, thereby introducing additional errors into the matching process.
## 1 Introduction
### Research Challenges  
This study utilized a quantitative approach in collecting field data from a large West City Auckland shopping mall parking lot; a propagation measurement approach. The idea was to set up and evaluate the system's performance in the shopping mall parking lot. Various OPNET-based simulation scenarios were developed to analyze the system's performance. Another major challenge was to find the closest match between the field trial measurements and propagation models. In this paper, we address the three following research questions:  
* **Question 1:** What Wi-Fi-based VANET can be deployed in a large shopping mall parking lot to reduce car accidents?
* **Question 2:** What best practice guidelines can be provided to network designers and planners for implementing a low-cost VANET in parking lots?
* **Question 3:** What propagation model would best fit (closest match) with field measurement data?
## 4 Signal Processing
### Range Migration Compensation  
Due to GPS communication quality or other external environment interference, the UAV platform will inevitably generate relative motion even in hovering mode. In this study, the platform movement that is less than the range resolution of radar is defined as tiny platform motion, and the platform movement that is greater than the range resolution of radar is defined as great platform motion. The great platform motion will cause the problem of range migration (illuminated in Figure 4a), which refers to the fact that at two different time points, the target is located in different range bins of radar echo data. In order to accurately select the range bin where the human target is located, it is necessary to correct the range migration caused by great platform motion.  
Figure 3: The block diagram of the radar signal processing.  
For removing range migration, the cross correlation between the first frame and each subsequent frame of raw two-dimensional matrix is calculated [21]. The difference between two frames means that the human subject is located in different range bins at these two time points, and the position of the maximum cross correlation value indicates then lead or retardation range unit. Then, each frame is circularly shifted on the basis of maximum cross correlation value to compensate for range migration [22]. As shown in Figure 4b, after range alignment, the target is located in the same range bin at any time point.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 4 Modeling the Performance of Bluetooth Low Energy for Opportunistic Sensor Data Collection  
This section provides analytical models of crucial performance parameters for OSDC with BLE: sensor node current consumption and lifetime, and the maximum amount of collected data per contact interval. We assume a non-ideal channel, with uncorrelated bit errors. The section is divided in two subsections, which offer the aforementioned models for the advertisement-based and connection-based approaches, respectively.
# Chosen Plaintext Combined Attack against SM4 Algorithm
###### Abstract
For example, [PERSON] [27] proposed a chosen plaintext power attack on CRT_RSA and [PERSON] [28] proposed a chosen plaintext power attack on post-quantum authenticated encryption. More generally, [PERSON] et al. [29] showed that a generic strategy can be applied to any differential power or electromagnetic analysis attack, against unprotected or protected devices and exploiting profiled or non-profiled leakage models. To sum up, chosen plaintext power attacks have already been applied to many algorithms, especially AES and SM4. However, the above chosen plaintext power attacks (here we just discuss the attacks against SM4) still require analysis of four rounds of SM4 one by one. That is, it is necessary to know the previous round's key value when analyzing the current round. Moreover, different special plaintexts are required for CPA/DPA to recover the different round's key. Hence, it is necessary to collect power consumption curves four times to recover the initial key. (Each time, the power curve of the next round can be collected only after the key value of the previous round is determined by the power analysis.) Reducing the rounds of this type of analysis means the attack will fail. Moreover, the attacks are mainly aimed at the linear operations and lack the analysis of the nonlinear S-box (strong leakage point). The problems above will make the attack more complicated and it may fail (because of the lower leakage of linear operations). Hence, we think that there is still room for further improvement.
# Therapeutic Applications of Halloysite
## 4 Recent Trends in HNT Research
### Metalized HNT Applications
A strategy for controlled weight deposition of positively charged metal ions on negatively charged. HNTs dispersed in an aqueous medium where metallization can be controlled using changes in voltage, solvent medium, time, and other electrolytic parameters without using any toxic chemicals, expensive reagents, or lengthy pre-processing steps [88]. This rapid and low-cost method was used in various applications, from 3D printing to surface coats. PLA 3D printed scaffolds incorporating metal coated HNTs for bone tissue regeneration have recently been published. [PERSON] et al. [87] used a surface modification method to reduce the hydrophobicity of PLA. This study used fetal bovine serum and NaOH to coat PLA scaffolds containing Zn-coated HNTs (Figure 6). The scaffolds possessed high mechanical strength and showed an osteoinductive potential. Furthermore, the external coating of antibiotics preserved the osteogenic properties but also significantly reduced bacterial growth. [87] A similar study used 3D printed PLA scaffolds modified using an alkali treatment to increase hydrophilicity and the surface-functionalized using a suspension of Zinc/HNTs-Ag-Chitosan Oligosaccharide Lactate [88].
## 3 FPTLIR/PTL Dynamics Modelling
### Calculation of the Motion Equations of the System  
The equations of motion for the FPTLIR can be obtained using the Lagrange equation as follows:  
\[\frac{\mathrm{d}}{\mathrm{d}t}\left(\frac{\partial T_{B}}{\partial\dot{\mathbf{q}}_{ r}}\right)-\frac{\partial T_{B}}{\partial\mathbf{q}_{r}}=\mathbf{Q}_{grav}+\mathbf{Q}_{\text{con}} \ +\mathbf{F}_{c} \tag{57}\]  
Substituting Equation (52) into Equation (57) yields:  
\[\mathbf{M}_{\text{B}}\ddot{\mathbf{q}}_{r}=\mathbf{Q}_{\text{grav}}+\mathbf{Q}_{\text{con}}\ + \mathbf{F}_{c} \tag{58}\]  
The gravitational force acting on the FPTLIR in the inertial coordinate system is given by \(\mathbf{Q}_{\text{grav}}=m_{B}\text{g}^{\text{I}}\), and the control force in the inertial coordinate system is:  
\[\mathbf{Q}_{\text{con}}\ =\ \left[\begin{array}{c}\dot{I}_{\text{IB},D}^{\text{B}}\\ \dot{I}_{\text{IB},R}^{\text{B}}\end{array}\right]^{T}\left[\begin{array}{ ccc}0&0&\mathbf{F}_{\text{z}}^{\text{B}}&\mathbf{M}_{\text{x}}^{\text{B}}&\mathbf{M}_{ \text{y}}^{\text{B}}&\mathbf{M}_{\text{z}}^{\text{B}}\end{array}\right]^{T} \tag{59}\]  
This study investigates the system's performance under the condition of no external intervention, where the control force is set to zero. The PTL is suspended at both ends by rigid insulators attached to power towers, which impose fixed constraints on the system at both ends. The generalized coordinates of the FPTLIR are constrained by the contact point with the PTL. Since the relative deformation of the insulators is negligible, it is not considered in this analysis. The kinematic constraints are incorporated into the constraint equations pertaining to the generalized coordinates of \(\mathbf{q}\). The matrix \(\mathbf{q}\) comprises the complete set of elements, \(\mathbf{q}_{\text{a}}\). Therefore, the govern equations for the entire system are formulated using the Lagrange equation and a set of constraint equations, as follows:  
\[\mathbf{M}\ddot{\mathbf{q}}+\left(\frac{\partial\mathbf{\Phi}}{\partial\mathbf{q}}\right)^{ \text{T}}\mathbf{\lambda}=\mathbf{Q} \tag{60}\]  
\[\mathbf{\Phi}(\mathbf{q})=\mathbf{0} \tag{61}\]  
In the above equation, the matrix \(\mathbf{M}\) is constituted by the mass matrix of the PTL \(m^{\alpha\beta}\) and the mass matrix of the FPTLIR \(\mathbf{M}_{\text{B}}\). The coefficient matrix of the constraint equation is represented by \(\mathbf{\Phi}\), while the vector of Lagrange multipliers is represented by \(\mathbf{\lambda}\). The generalized force, denoted by \(\mathbf{Q}\), is a function of the previously calculated forces \(\mathbf{Q}_{c}^{\kappa}\), \(\mathbf{Q}_{f}^{\kappa}\), \(\mathbf{Q}_{N}^{\kappa}\), \(\mathbf{Q}_{\text{grav}}\), \(\mathbf{Q}_{\text{con}}\), and \(\mathbf{F}_{c}\).
## 4 Discussion
### Correlation with Clinical Data  
The spectral changes collected from cranial and extracranial sites in patients strongly correlate with clinical measures of disease severity. PLS models displayed high predictive power for both EDSS and FSS scores, with performance varying across anatomical sites. The finding that spectra collected at both the cranial and extracranial sites are predictive of disability and fatigue reinforces the notion that both central neural and peripheral muscular dysfunction contributes to clinical disability and to the most debilitating symptom of MS, fatigue.
## 1 Introduction
However, closed systems have several major drawbacks, including difficulties in construction, maintenance, scaling up, operation, overheating, and biofouling, as well as high costs [32; 33; 34]. According to [35], a combination of an outdoor photobioreactor and a raceway pond can be used to produce astaxanthin from _H. pluvialis_ with more commercial benefits than chemically synthesised astaxanthin. A number of factors must also be taken into consideration for the feasible mass production of microalgae such as _H. pluvialis_ to achieve high astaxanthin yield, including the accessibility of nutrients (e.g., phosphorous, potassium, nitrogen, magnesium, and zinc); oxygen; light intensity; temperature; pH; inorganic carbon, carbon dioxide, and salinity levels; bioreactor dimensions; mixing; dilution rates; harvesting frequency; nutrition modes; genetic modifications; and supplementation of chemicals or growth factors [5]. Light is the prime factor regulating microalgal growth, as it controls the photosynthesis, growth, reproduction, and metabolism of microalgae. Importantly, excessive cell density can prevent the penetration of light and thus diminish the rate of photosynthesis. The use of nitrogen-deficient nutrient medium also plays a key role in astaxanthin accumulation, as nitrogen is a necessary nutrient for enzymatic activity and growth in _H. pluvialis_[34; 36]. Apart from projects related to _Chlorella_-derived food and dietary supplements, the development of microalgal mass cultivation systems is in its initial stages in South Korea, principally targeting applications in the biofuel industry [37]. In this study, we established an efficient, innovative, economically viable hybrid open-closed mass cultivation system for _H. pluvialis_ to fulfil semi-industrial demands for natural astaxanthin in South Korea, while reducing contamination. We investigated various parameters for achieving optimum astaxanthin production. We analysed the effects of varying light intensities on cell density, biomass, chlorophyll content, and astaxanthin accumulation. We discuss the application of the proposed hybrid open-closed _H. pluvialis_ mass cultivation system as a cost-effective, profitable method for the large-scale bioproduction of natural astaxanthin. Many laboratory-based studies have been conducted on _Haematococcus_ due to its unique ecological characteristics; however, there have been very few field trials. Therefore, we cultured _Haematococcus_ at the laboratory scale and applied the same methods to the field.
## 4 Future Directions
### Human-Centric Design and Usability Optimization  
Despite advancements, usability gaps remain a barrier for non-technical users. Future platforms must adopt context-aware interfaces that dynamically adjust complexity based on user expertise [72; 73; 74]. For example, [PERSON] et al. [20] propose a tiered training system where citizen developers receive real-time guidance via AI-powered tooltips, reducing onboarding time in electronics manufacturing.  
Additionally, [PERSON] et al. [17] advocate for collaborative development models, where domain experts and developers co-design applications using shared visual dashboards. Case studies in equipment manufacturing show this approach reduces requirement misalignment.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 4 Application of Modulation Spectroscopy for Methane Detection
### Wavelength Modulation Spectroscopy Applied for Methane Detection  
#### 4.1.1 Principle of Wavelength Modulation Spectroscopy  
In a typical TDLAS with WMS, the laser frequency is modulated by applying a low-frequency ramp and a high-frequency sine wave [23; 70]. After interacting with the absorption line of the target gas, the modulated light generates the signals at different harmonics of modulation frequency [75]. At a fixed harmonic, the amount of signal attenuation is proportional to the gas absorption, and the modulated wavelength is demodulated by a phase-sensitive detector [76]. The demodulated wavelength at a particular harmonic _nf_ (\(n=2,3,4\), etc.) is usually directed for the detection, and the detection bandwidth is shifted to higher frequencies, where 1_/f_ noise is smaller. The output spectrum of the laser modulated by a radio frequency is shown in Figure 1[77].