esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
## 2 Basic Architecture
### Characteristics of IoT Sensor Data  
The IoT sensors generate data consecutively or upon the trigger of an external event. The other process involves data generated by sensor nodes that need to be gathered, aggregated, analyzed and visualized to obtain useful information. This information is then interpreted to produce the representable form, that is deliverable, and a reaction towards the external trigger. In addition to the data generated by sensor networks, other sources also have data streams. As such, the data generated are required to be aggregated and warehoused in an unprecedented manner, and streamed at a specific network data rate into remote locations for historical data analysis. However, there are several sensor data characteristics and problems associated with this. The authors in [23] discuss that sensor data exhibits information complexity due to factors like the huge volume, the dynamism of data, real-time updating, critical data aging, and interdependency between different data sources. Generally, the sensors are implanted into the human body, objects or locations. As such, the significant characteristics of IoT sensor data are as given below:  
Technical ConstraintsThe limited size of the sensor leads to technical constraints such as computing power, battery power, networking capability, storage capacity and memory. As such, these sensors are highly vulnerable to failure, attacks, and easy breakdown, thus leading to losses of sensor data and inaccurate information;  
Real-Time ProcessingThe sensor network will be capable of more complex networking tasks, and can perform the transformation of raw sensor data into more valuable and insightful information in real-time;  
ScalabilityIn the physical world, the sensor network includes data sources from numerous sensors and actuators. Sensor networks must be scalable to accommodate the exponential growth of sensors and actuators, data handling, and meet the various objective of IoT-based applications;  
Data RepresentationThe general format of sensor data is as a small-sized tuple with structured information. The various representations of sensor data are Boolean, binary, featured values, continuous data, and numeric values;  
HeterogeneityToT sensor data are heterogeneous. There are different data sources, including rigidly structured data sets, real-time data-generating information networks, embedded systems with sensors, social network media data stream, and other participatory sensor networks.
## 4 Numerical Simulations and Discussion
### Comparison with Other AOA Methods
#### 4.3.2 Array-Signal to-Noise (SNR)  
In this scenario, we still assume the number of snapshots measured is limited (\(N=3\)) but with differing SNR. The SNR at the input to the sensor array receiver plays a crucial role in the performance  
Figure 7: Average root mean square error (ARMSE) vs. number of snapshots with signal to noise ratio (SNR) = 10 dB, \(M=8\), \(D=2\), 1000 Monto Carlo simulations for each \(N\).  
of AOA in localization systems. Therefore, this simulation compared the variations in performance of AOA methods with a variation of SNR. A ULA consisting of \(M=8\) sensors and \(d=0.5\lambda\) is considered, this array receiving two BPSK signals from different directions. 1000 Monto Carlo simulations with \(D=2\) are used to generate AOAs: these angles are applied for all algorithms. The ARMSE is computed for each SNR and plotted for all techniques in Figure 8. The effect of the SNR on the performance estimation of each technique can be clearly seen in this graph: at low SNR (i.e., SNR less than 0 dB), MUSIC and PDDA methods give the best accuracy compared with other AOA techniques. However, as the SNR improves the PDDA method gives the best estimation resolution compared with all the AOA techniques presented. The reason that MUSIC has better accuracy than the PDDA method at poor SNR is because the large amount of noise diffuses the phasors of arriving signals and this in turn affects negatively normalizing the phase of the first sensor and eliminating the dependency on the signal time series. On the other hand, MUSIC has higher complexity than PDDA as shown in Table 4.
## 4 Experimental Results and Evaluation
### Experiment C  
In large-scale experiments, we demonstrated the effectiveness of the proposed algorithm in terms of computational time and the optimal solution to the most difficult and complex problems. We generated a dataset of container sizes and boxes consisting of 225 cases with variations in dimensions and profit per box, with only one container size being set. To test the performance of the proposed method, we designed datasets with different levels of problem complexity: low, medium, and high. We defined the complexity according to the complexity indicator (CI), which was calculated as follows:  
* CI = 100%, where the size of every box arranged in the container was unique. * CI = 50%, where the size of every box arranged in the container was 50% unique. * CI = 25%, where the size of every box arranged in the container was 25% unique. Table 15 presents the Pareto front results of the ABC algorithm with the Pareto-based approach. It is important to note that LINGO cannot find a solution within a reasonable computing time (10 h) for small problem sizes, thereby restricting its ability to handle large and complex problems. In contrast, the ABC algorithm with the Pareto approach has proven to be capable of solving optimization problems of varying complexities and sizes, with an average resolution time of only 114.94 s and a very low average standard deviation of 4.26, which is significantly shorter than the 15 min average required for manual planning. This has resulted in an 87.23% improvement in time efficiency. Based on the results of small problems (instances 11-15), the ABC algorithm with the Pareto-based approach easily finds the optimal solution and gives the best answer regarding the empty space and appropriate profit in every case. As a result of medium problems (instances 6-10), the proposed method shows a significant or approximately double gain of profit, even though the empty space is slightly increased. Finally, in the case of large or complex problems of maximum box size (instances 1-5), the proposed method shows a significant increase in profit while the computational time remains the same, although the remaining space increases slightly. Consequently, it can be concluded that the proposed approach is effective at solving real-world problems based on the solution quality and computation cost.
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
## Appendix A  
**Proof of Equation (27).** In the initial stage, i.e., for \(u=0\), the approximation error \(\epsilon_{A}\) is the only nonzero component of the MSE. After each incrementation of \(u\) by 1, the truth of the following condition shall be tested:  
\[\epsilon_{E}\geq\epsilon_{A}\]Substituting with Equations (24) and (26), the above inequality transforms to:  
\[\begin{split} u\sigma_{W}^{2}\geq\sum_{i=1}^{M-u}|\hat{\mathbf{h}}_{C }(m(i))|^{2}-(M-u)\sigma_{W}^{2}\\ u\sigma_{W}^{2}+(M-u)\sigma_{W}^{2}\geq\sum_{i=1}^{M-u}|\hat{ \mathbf{h}}_{C}(m(i))|^{2}\\ M\sigma_{W}^{2}\geq\sum_{i=1}^{M-u}|\hat{\mathbf{h}}_{C}(m(i))|^{ 2}\end{split}\]  
The smallest value of \(u\) that satisfies the above condition is the desired value of \(U\).
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems
#### 3.2.3 Zero-Knowledge Proof (ZKP)  
Zero-Knowledge Proof (ZKP) is an innovative cryptographic method used for authentication and access control. Zero-Knowledge Proof (ZKP) is a cryptographic protocol that allows one party to prove the validity of information to another party without disclosing the actual data. In on-chain systems, ZKP enables users to verify their credentials without revealing sensitive data. For instance, in the healthcare sector, a patient's medical records can be validated without disclosing personal information, ensuring data privacy and security [109; 115]. This method is widely utilized in public and financial sectors for blockchain-based identity verification [119; 130; 131; 132]. Despite its high security benefits, ZKP is computationally expensive, which limits scalability. In hybrid models, ZKP performs privacy-focused off-chain computations, while the verification process takes place on-chain [56; 113; 133]. This approach is particularly effective for use cases where identity verification is required without exposing personal credentials, such as secure system access control.
# Investigation of the Influence of Sublayer Thickness on Pairing of Metallic MEMS Shutter Blades
###### Abstract  
For the 3D self-assembly of microstructures, the net intrinsic stress within a hybrid metal layer stack is utilised to study the dynamics of the pairing of metallic MEMS shutter blades with dimensions of 150 \(\times\) 400 \(\upmu\)m\({}^{2}\), representing a highly nonlinear Casimir system. The study focuses on two main geometries: (i) unpaired (freestanding) and (ii) paired metallic Micro-Electro-Mechanical Systems (MEMS) shutter blades. The hybrid metal stack comprises three metal layers that are under intrinsic stress, which enables the curling of the freestanding shutter blades. The top aluminum layer thickness is varied systematically, creating tailored stress in the shutter blades, resulting in the freestanding blades curling differently, as well as geometrical changes in the paired shutter blades. Concerning the thickness variations, a large technological processing window has been identified, during which the pairing of the neighbouring shutter blades occurs (the top aluminum layer thickness ranges between 70 and 88 nm). Circles and ellipses have been fitted to micrographs obtained by scanning electron microscopy (SEM) and Confocal Laser Scanning Microscopy (CLSM). We observed a trend of the reduction of the radius of curvature RoC (ranging between 17 and 79 \(\upmu\)m) for the unpaired shutters, and eccentricity (0.36 \(<\)\(\epsilon\)\(<\) 0.67) occurs throughout the system for the paired shutters. Concerning the specific shape of the overlapping region of the two blades of the paired shutters, three pairing scenarios (I, II, and III) have been identified, classified, evaluated, and reported. The overlapping length \(l_{0}\) between the paired shutter blades ranges between 31 and 8 \(\upmu\)m. These scenarios also reveal nonlinearities in the pairing process.  
MESM; microshutter blades; Casimir effect; 3D self-assembly; tight attachment; thin film layers; stress; curling +
Footnote †: journal: Applied  
Acticle Investigation of the Influence of Sublayer Thickness on Pairing of Metallic MEMS Shutter Blades.
###### Abstract  
Accurate Total Organic Carbon (TOC) prediction in the deeply buried Lower Cambrian Qiongzhusi Formation shale is constrained by extreme heterogeneity (TOC variability: 0.5-12 wt.%, mineral composition Coefficient of Variation > 40%) and ambiguous geophysical responses. This study introduces three key innovations to address these challenges: (1) A Dynamic Weighting-Calibrated Random Forest Regression (DW-RFR) model integrating high-resolution Gamma-Ray-guided dynamic time warping (\(\pm\)0.06 m depth alignment precision derived from 237 core-log calibration points using cross-validation), Principal Component Analysis-Deyang-Anyue Rift Trough Shapley Additive Explanations (PCA-SHAP) hybrid feature engineering (89.3% cumulative variance, VIF < 4), and Bayesian-optimized ensemble learning; (2) systematic benchmarking against conventional \(\Delta\)logR (R\({}^{2}\) = 0.700, RMSE = 0.264) and multi-attribute joint inversion (R\({}^{2}\) = 0.734, RMSE = 0.213) methods, demonstrating superior accuracy (R\({}^{2}\) = 0.917, RMSE = 0.171); (3) identification of Gamma Ray (r = 0.82) and bulk density (r = \(-\)0.76) as principal TOC predictors, contrasted with resistivity's thermal maturity-dependent signal attenuation (r = 0.32 at Ro > 3.0%). The methodology establishes a transferable framework for organic-rich shale evaluation, directly applicable to the Longmaxi Formation and global Precambrian-Cambrian transition sequences. Future directions emphasize real-time drilling data integration and quantum computing-enhanced modeling for ultra-deep shale systems, advancing predictive capabilities in tectonically complex basins.  
Keywords:total organic carbon; Qiongzhusi formation; random forest regression; PCA-SHAP; real-time drilling data integration +
Footnote †: journal: Applied sciences
# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
## 5 Conclusions  
Photographing with a non-standardised device like a smartphone or a digital camera to document the skin changes is becoming increasingly popular among dermatologists. Due to the rapid development of new devices, diagnostic methods and applications based on a recognition of skin cancer through captured images, it is necessary to ensure credible methodologies and standards for assessing camera systems.  
In this paper, the authors discussed measurement results that we made to assess the image quality of digital imaging systems for melanoma detection. In particular, we measured such variables as sharpness, resolution and colour accuracy. The standard methods, based on the eSFR of a slanted edge, were applied for measuring the sharpness and resolution of a camera system. Colours of known reference values were captured from the test target to evaluate colour error as a CIELAB \(\Delta E^{*}_{ab}\) colour difference.  
The results of our measurements yielded two significant findings. First, that currently on-the-market cameras, when operating in the auto mode, render overly saturated colours. Second, the applied sharpening level, especially estimated for images captured by some of the smartphone cameras tested, is set too high. Both the auto mode feature and applied sharpening technology are incorporated into the camera software by default by the manufacturers, to produce images that have the most \"eye-appeal\" for the broadest population of end-users.  
A two-step camera calibration procedure was performed to improve colour accuracy on dermoscopy images. The applied method is simple and does not take too much effort and dermatologist's time. In the first step, the white balance on the camera was adjusted to match the light source of a dermoscope, and in the second, the proper exposure time was estimated regarding a reference middle grey value, to avoid under- or overexposed images. A manually adjusted camera setting gives a significant improvement in the colour accuracy where colours are much closer to the range of the human eye's detection capability.  
Conceptualisation, A.D., M.M. and B.D.; methodology, A.D., B.D. and M.G.; software, M.G. and B.D.; validation, B.D., A.D. and M.G.; writing--original draft preparation, B.D.; writing--review and editing, B.D.; M.G.; M.M. and A.D.; visualisation, B.D., A.D. and M.G.; supervision, A.D. and B.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Wildfires in the Siberian Arctic
###### Abstract
Due to the remoteness and geographic scale of the Siberian boreal and arctic regions, wildfire analysis is based primarily on remote sensing data with a resolution of 250-1000 m as provided by MODIS (the Moderate Resolution Imaging Spectroradiometer) on the Terra and Aqua platforms. MODIS also provides near real-time data on the areas burnt and location of fire ignitions including localized thermal points (\"hotspots\") [25]. Similar data is provided by the vegetation sensor on the PROBA-V platform [26] and the VIIRS instrument on the Suomi NPP satellite [27; 28]. More detailed fire impact and post-fire vegetation succession analysis is based on medium (10-30 m) and high (1-2 m) resolution instruments such as Landsat (e.g., [29; 30]), Sentinel (e.g., [31]), or Quickbird [32]. Here we report on wildfire dynamics within the Siberian Arctic in the 21 st century. We propose to test the hypotheses that climate change in the Arctic has caused wildfire to increase in frequency and extent and that the northern limit of fire occurrence has migrated northward to the Arctic Ocean. We seek to answer the following questions for the Siberian Arctic:  
1. What are the dynamics of wildfire parameters such as fire frequency, burned area extent, fire season duration, and the position of the northern boundary of fire occurrence? 2. How are wildfire parameters related to air temperature, precipitation, ground cover moisture, and occurrence of lightning strikes? 3. What the temporal patterns of post-fire recovery are of gross (GPP) and net (NPP) primary productivity?
# Cloning and Functional Identification of _Sipg49_ in _Solanum Lyopersicum_
## 3 Results
### Enzymatic Activity of SIPG49 In Vitro  
SIPG49 was heterologously expressed, and its activity was tested to characterize whether it encoded an active PG. SIPG49 was predicted to contain a signal peptide (Figure 4A). A truncated CDS, including sequences encoding GH28 domain, was cloned into the pMAL-c2x vector (Figure 4B) to improve protein solubility, using pMAL-MBP as a negative control. The predicted sizes of SIPG49 and MBP tag protein were 49.03 kDa and 43 kDa, respectively. The results of SDS-PAGE gels and Western blot illustrated that the sizes of the tag protein band and fusion protein band were consistent with the prediction (Figure 4C,D), indicating that SIPG49 protein was successfully isolated and purified. The purified proteins were incubated with polygalacturonic acid to analyze its hydrolytic activity. The results demonstrated that SIPG49 showed high enzymatic activity (Figure 4E), indicating that SIPG49 encodes a bona fide PG.  
Figure 4: _Cont._  
Figure 3: Subcellular localization of SIPG49-GFP fusion protein in living onion epidermal cells: (**A–C**) localization of SIPG49-GFP; (**D–F**) localization of SIPG49-GFP in plasmolyzed onion epidermal cells; cells were plasmolyzed by treatment with 1 mol-L –1 mannitol for 5 min. White arrowheads show the membranes separated from the cell wall. Bars = 20 μm.
# Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring
###### Abstract  
This paper presents a novel adaptive probabilistic algorithm to identify damage characteristics by integrating the use of the frequency response function with an optimization approach. The proposed algorithm evaluates the probability of damage existence and determines salient details such as damage location and damage severity in a probabilistic manner. A multistage sequence is used to determine the probability of damage parameters including crack depth and crack location while minimizing uncertainties. A simply supported beam with an open edge crack was used to demonstrate the application of the algorithm for damage detection. The robustness of the algorithm was tested by incorporating varying levels of noise into the frequency response. All simulation results show successful detection of damage with a relatively high probability even in the presence of noise. Results indicate that the probabilistic algorithm could have significant advantages over conventional deterministic methods since it has the ability to avoid yielding false negatives that are quite common among deterministic damage detection techniques.  
frequency response function; structural health monitoring; damage identification; probabilistic model; wavelet transform; optimization; structures +
Footnote †: journal: Applied Soft Matter  
1
## 2 Materials and Methods
### Performance Measures  
Different performance measures were considered in this study. Confusion matrix, Accuracy, Precision, Sensitivity, Specificity, F1-Score, and Run-time were calculated for each deep learning model on each dataset.  
Confusion matrix: A tabular design that can explain the number of true-positive, false-negative, true-negative, and false-negative results [32].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No. of Patients** & **Normal** & **Benign** & **Malignant** & **Total** \\ \hline
**Training** & 4109 & 62 & 39 & 4210 \\
**Validation** & 200 & 20 & 20 & 240 \\
**Testing** & 300 & 30 & 30 & 360 \\
**Total** & **4609** & **112** & **89** & **4810** \\ \hline \end{tabular}
\end{table}
Table 1: Number of cases in each category from the (BCS-DBT) dataset [29; 30; 31].  
\begin{table}
\begin{tabular}{c c} \hline
**Subsets** & **Number of Scans** \\ \hline
**Training** & 19,148 \\
**Validation** & 1163 \\
**Testing** & 1721 \\
**Total** & **22,032** \\ \hline \end{tabular}
\end{table}
Table 2: Number of cases in each subset from the (BCS-DBT) dataset [29; 30; 31].  
\begin{table}
\begin{tabular}{c c c c} \hline
**No. of Patients** & **Normal** & **Benign** & **Malignant** & **Total** \\ \hline
**Dataset \#1** & 99 & 62 & 39 & **200** \\
**Dataset \#2** & 199 & 62 & 39 & **300** \\
**Dataset \#3** & 499 & 62 & 39 & **600** \\ \hline \end{tabular}
\end{table}
Table 3: Details of each dataset in the proposed model.  
Accuracy: The fraction of the total samples that were correctly classified by the classifier. Equation (1) explains the accuracy measure for a single class.  
\[\text{Accuracy}=\frac{TP}{TP+TN+FP+FN} \tag{1}\]  
Precision: It tells you what fraction of predictions as a positive class were positive. Equation (2) explains the precision measure for a single class.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
Sensitivity: It tells you what fraction of all positive samples were correctly predicted as positive by the classifier. It is also known as true positive rate (TPR), sensitivity, and probability of detection. Equation (3) explains the sensitivity measure for a single class.  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{3}\]  
Specificity: It tells you what fraction of all negative samples is correctly predicted as negative by the classifier. It is also known as the true negative rate (TNR). Equation (4) explains the specificity measure for a single class.  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{4}\]  
F1-Score: It combines precision and recalls into a single measure. Mathematically it is the harmonic mean of precision and recall shown in Equation (5).  
\[\text{F1}-\text{Score}=\frac{2*TP}{(2*TP)+FP+FN} \tag{5}\]  
Run-time is the execution time for the feature extraction and classification for each deep learning model in seconds.