esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 5 Simulation Results  
Massive MIMO wireless channel estimation techniques primarily evaluate their performance using mean square error (MSE). This can be demonstrated from the following three aspects:  
1. Estimation accuracy comparison experiment: The estimated multipath component is compared with the actual modeling, and the estimation performance is characterized by an intuitive method.
2. Performance simulation experiment with the change of pilot frequency: The decrease of the number of pilots will inevitably affect the estimation performance, and the number of pilot changes to characterize the estimation performance can better reflect whether the estimation algorithm can reduce the pilot overhead, without affecting the estimation performance.
3. Performance simulation experiment with SNR variation: SNR can reflect the channel state well, and SNR variation to characterize the estimation performance can well reflect the estimation of estimation algorithm under different channel conditions.  
Therefore, this program mainly demonstrates the superiority of the proposed algorithm from the above three aspects of the experiments.  
This section uses MATLAB simulation software to verify the performance of the proposed SSA-CoSaMP algorithm in a massive MIMO-OFDM wireless communication system. The system simulation parameters are in Table 1.  
In terms of performance measurement, normalized mean square error (NMSE) is used as an indicator. The smaller the NMSE, the smaller the error of the estimated result, indicating that the estimated performance is better. In this paper, the mean square error (MSE) is normalized, that is, NMSE, and is defined as follows to be used as a measure of channel estimation performance.  
\[NMSE=\frac{1}{M\!R}E|\tilde{h}-h|_{F}^{2} \tag{13}\]As a comparison algorithm, the CoSaMP algorithm [27] and the S-CoSaMP algorithm [28] are considered.
## 2 Materials and Methods
### Sampling  
Process (flotation, magnetic separation) tailings are stored in three TSFs, which are 4 m (TSF1), 8 m (TSF2) and 4 m (TSF3) high, respectively. The sampling consisted of collecting process tailings and salt precipitates from all TSFs. A total of 26 samples of process tailings were selected from the three TSF basins: 7 from TSF1, 4 from TSF2 and 15 from TSF3 (Figure 4). Two samples of salt precipitates were taken at the top surface (0-5 cm) of TSF2 and TSF3 (TSF2S and TSF3S, respectively). All sampling points were geo-referenced by Global Positioning System (GPS coordinates are given in Appendix A), according to a planned map and to accessibility to the site. Tailings samples were taken with a small caterpillar excavator at the top of TSF1 (0-4 m depth) and TSF2 (0-5 m depth) and by dredging the top of TSF3 (0-1.5 m depth) (Figure 5). The TSF1 and TSF2 materials consist of alternating unoxidized (gray, black colors) to oxidized (brownish, red, orange colours) material of a few centimeter thick layers; whereas the TSF3 materials are more or less homogeneous mixtures of oxidized and non-oxidized tailings. Five kilograms of materials were collected at each sampling point. Each sample was put in a polyethylene bag with a shovel and referenced. In the laboratory, the samples were successively mixed, homogenized, quartered, and divided by rotary dividers in order  
Figure 3: Satellite view (Google Earth) showing the three TSF ponds and the open pit of the Cu-Au mine of Guelb Moghrein.  
to constitute representative composite samples of about 1 kg for each TSF, in view of physicochemical, mineralogical and environmental characterization.
## Appendix C Estimated Results of the Scale Factor Error and Misalignment Error (FOGs)  
In Figure 11, it can be seen that the estimation of the FOG scale factor error and the misalignment error derived from the two EKFs all converge to the stable value. The scale factor errors Kxx, Kyy, and Kzz of the three-axis FOG estimated by the nonlinear EKF are \(186.11\) ppm, \(-663.73\) ppm and \(-632.08\) ppm, respectively. The scale factor errors Kxx, Kyy, and Kzz estimated by the linear EKF are \(177.93\) ppm, \(-724.73\) ppm and \(-633.40\) ppm, respectively. The three misalignment errors Kyx, Kzx and Kzy of the FOG as estimated by the nonlinear EKF are \(-9818.57\)\(\upmu\)rad, \(1878.28\)\(\upmu\)rad and \(100.68\)\(\upmu\)rad. The three misalignment errors Kyx, Kzx and Kzy of the FOG as estimated by the \(30\)-order filter are \(-9735.09\)\(\upmu\)rad, \(1940.04\)\(\upmu\)rad and \(-732.83\)\(\upmu\)rad.
## 2 Hardware Design and Implementation
### Remote Access System  
Client/Server framework is desirable to implement the idea of centralized storage and distributed sensing. It ensures a plurality of onsite monitoring system that is currently in monitoring can submit all the data collected. The monitoring personnel can get access to any one of the onsite monitoring system in remote. Figure 9 shows the C/S framework.  
The C/S framework consists of multiple onsite monitoring systems (as in the previous section), a central data server and remote access terminals. The central data server has a typical LAMP  
Figure 8: Schematic design and photo of the actual sensor. (**a**) Schematic design of fiber optical temperature sensor; (**b**) The actual temperature sensor.  
Figure 9: Client/Server framework.  
architecture, in which L stands for Linux, A stands for Apache, M stands for MySQL, and P stands for PHP. The MySQL is the world's second most widely used relational database management system (RDBMS) and most widely used open-source RDBMS. The Apache HTTP Server, colloquially called Apache is the world's most widely used web server software. The PHP is a programming language for the writing of active web pages. Together with Linux, apache, MySQL and PHP guarantee an easy approach to ensure remote access via any device connected to the Internet, e.g., Notebook, PC, as shown in Figure 9. Figure 10 shows an example interface of the homepage for the monitoring web site.
# Evolution towards Smart and Software-Defined Internet of Things
## 7 IoT Challenges and Future Directions
### Security and Privacy  
The more devices we bring online, the higher becomes the need to secure the data being transmitted between machines and users. As we expand our mesh of inter-connectivity, our data become more prone to cyber-attacks and crimes. With this advancement comes the greater responsibility of securely transmitting and sharing our data and making communication private [100]. Some new techniques, such as blockchain-based security enhancement in IoT networks, can be used to improve security and privacy. Similarly, more robust algorithms can be designed to improve IoT network security and privacy.
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 2 Related Work
### Post-Training Quantization  
Past work on post-training quantization can be distinguished into three categories: weight-sharing quantization, uniform-partition quantization, and logarithmic quantization. [PERSON] et al. used logarithmic operations to quantify floating-point numbers into fixed-point numbers [18]. Unlike linear quantization, its quantization method is based on exponents. The weight matrix stores the value of exponents to achieve the effect of approximating fixed-point numbers. Logarithmic quantization accelerates inference through shift operations but requires specialized hardware support. This paper will mainly discuss weight-sharing quantization and uniform-partition quantization.
## 2 Materials and Methods
### Radiomic Feature Extraction  
The DCE-MRI images analyzed represent a homogeneous dataset in terms of spatial resolution along the \((x,y)\) plane and slice thickness along the \(z\) axis. For this reason, the extraction of the features was performed without any resampling to avoid interpolation artifacts. Radiomic features were extracted from the 3D ROIs delineated in the previous step. Five texture-feature categories were extracted and considered in this study:  
* Gray-level co-occurrence matrix (GLCM) [23; 24]: spatial relationship between pixels in a specific direction, highlighting the properties of uniformity, homogeneity, randomness, and linear dependencies;
* Gray-level run length matrix (GLRLM) [25]: texture in specific direction, where fine texture has shorter runs while coarse texture presents more long runs with different intensity values;
* Gray-level size zone matrix (GLSZM) [26]: regional intensity variations or the distribution of homogeneity regions;
* Gray-level dependence matrix (GLDM) [27]: quantifies gray-level dependencies;
* Neighboring gray tone difference matrix (NGTDM) [28]: spatial relationship among three or more pixels, closely approaching the human perception of the image.  
Conversely, shape-based and first-order features were not considered in the study because they are independent of the quantization level.  
Figure 1: Examples related to a benign (on the top) and a malignant (on the bottom) breast mass. For each example, the DCE-MRI image area is shown without (on the left) and with (on the right) delineation contours. Segmentation results yielded by the automated delineation (red contour) and manual delineations—performed by the first (green contour) and the second (blue contour) radiologist—are compared. All figures are shown with a 2.5\(\times\) magnification factor.  
It is worth noting that texture features, such as the GLCM features (also known as Haralick's features [23; 24]), are calculated from the co-occurrence matrix that is based only on the gray-level values. Therefore, the ROI shape does not play any role, because co-occurrence matrices consider only the pairs of gray-level values composed of two adjacent or neighboring voxels [29].
###### Abstract  
To investigate the seismic response of layered rock sites, a multidomain analysis method was proposed. Three finite element models with infinite element boundaries for layered sites were analysed. The results of this multidomain analysis show that stratum properties and elevation have an impact on wave propagation characteristics and the dynamic response of layered sites. Compared with the rock mass, the overlying gravel soil has a greater dynamic amplification effect at the sites. A time domain analysis parameter PGA(IMF) was proposed to analyse the effects of different strata on the seismic magnification effect of layered sites, and its application was also discussed in comparison with PGA. According to the frequency domain analysis, the interface of the rock mass strata has a low impact on the Fourier spectrum characteristics of the sites, but gravel soil has a great magnification effect on the spectrum amplitude in the high-frequency band (\(\geq\)30 Hz) of waves. Moreover, the stratum properties have a great influence on the shape and peak value of the Hilbert energy and marginal spectrum at layered sites. When waves propagate from hard rock to soft rock, the peak value of the Hilbert energy spectrum changes from single to multiple peaks; then, in gravelly soil, the Hilbert energy spectral peak, its nearby amplitude and the amplitude in the high-frequency band (28-36 Hz) are obviously amplified. The frequency components and amplitude of the marginal spectrum become more abundant and larger from rock to gravelly soil in the high-frequency band (28-35 Hz).  
dynamic response; layered sites; spectral characteristics; multidomain analysis; seismic excitation
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 4 Experiment and Results
### Comparative Experiments
#### 4.2.2 Comparative Experiments on SYSU-CD  
In the SYSU-CD dataset, FC-Siam-Diff showed the worst performance with an IoU of 50.09% and F1 score of 66.75%. SAM-CD achieved an IoU of 69.76% and F1 score of 81.91%. Our proposed Siamese-SAM model outperformed SAM-CD, with an IoU of 70.17% and F1 score of 82.61%. Compared to SAM-CD, Siamese-SAM achieved an F1 score increase of 0.7% and IoU increase of 0.41%. Additionally, Siamese-SAM also showed significant improvements in precision (+0.79%), recall (+0.61%), Kappa (+1.30%), and overall accuracy (+0.52%). Among all the models tested, only Siamese-SAM surpassed an IoU of 70%, further  
Figure 10: Three sets of visualization results of different algorithms on the LEVIR-CD dataset. T1_img and T2_img are images at different times, label is the ground truth. a–e are BIT, SAGNet, AMFNet, SAM-CD, and Siamese-SAM respectively.  
highlighting its superior performance. The detailed quantification of model evaluation metrics is presented in Table 5.  
Figure 11 shows the comparative visualization results on the SYSU-CD dataset, with key regions highlighted using red boxes. In the first set of data, which involves the vegetation reduction near the highway, our Siamese-SAM model detected the largest area of change, clearly distinguishing the affected regions, outperforming other models in both the extent and accuracy of detection. In the second set, where new vegetation grew on open land, our model not only identified the largest change area but also excelled in handling the complex boundary between the vegetation, highway, and buildings, ensuring relatively smooth segmentation without overfitting or underestimating the change, a challenge that other models struggled with. In the third set, which features the appearance of open land due to the reduction of vegetation, our model demonstrated superior capability in accurately detecting the boundary between the open land and the remaining vegetation, capturing fine details that other models failed to identify. These results emphasize the robustness and precision of our Siamese-SAM model, particularly in handling subtle and complex changes, and highlight its superiority over existing models in both large-scale and fine-grained change detection tasks.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 6 Applications of Machine Learning in FANET
### Resource Allocation
As a result, using the A3C algorithm circumvents these restrictions and may be deemed \"better\" in scenarios where memory size and computational resources are limited, which is frequently the case. Simulating this scenario concluded that this framework outperformed the existing solutions for resource management in terms of efficiency and learning accuracy. In addition, the application of FL for power allocation and scheduling policy over a swarm of UAVs was suggested in [155] using the derived knowledge from transmission delay and antenna angle variations on convergence. The simulation results showed that FL convergence was more effective than a baseline architecture.
## 3 Methodology
### Clustering  
K-means clustering is used for this study. The decision of the cluster number is based on the inertia and the \"elbow rule\". Therefore, using that method, the elbow seems to be between three to five, as shown in Figure 2. For this research, the number of clusters decided is three. After clustering, to determine if the three clusters of data have a significant difference between the mean value, we applied one-way ANOVA for statistical analysis. One-way ANOVA was performed using python's function from the SciPy library.
###### Abstract  
This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms--dynamic_rows and dynamic_matrix--are proposed and tested. These algorithms select the update rule quasi-randomly based on the input vector, reducing the effectiveness of synchronization-based attacks. A series of simulations were conducted to evaluate the security implications under various configurations, including different values of K, N, and L parameters of neural networks. The results demonstrate that the proposed dynamic algorithms can significantly reduce the attacker's synchronization success rate without requiring additional communication overhead. Both proposed solutions outperformed hebbian, an update rule-based synchronization method utilizing the percentage of attackers synchronization. It has also been shown that when the attacker chooses their update rule randomly, the dynamic approaches work better compared to random walk rule-based synchronization, and that in most cases it is more profitable to use dynamic update rules when an attacker is using random walk. This study contributes to improving QKD's robustness by introducing adaptive neural-based error correction mechanisms.  
quantum cryptography; artificial neural networks; tree parity machines Article
# Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells
###### Abstract  
This study thoroughly investigates the complex origins of abnormal formation pressure in offshore oil and gas wells, taking the Rio del Rey Basin in Cameroon as a case study. Renowned for its abundant oil and gas resources, the area faces unique challenges in predicting overpressure due to its high-temperature and high-pressure reservoir characteristics. By quantitatively analyzing the main mechanisms such as undercompaction, high-temperature fluid expansion, and mud diapirism, the study addresses the complexities of overpressure prediction. This paper introduces an innovative analytical framework that combines hierarchical clustering algorithms with the LightGBM model. Further refined by the application of Bayesian optimization, the model intelligently adjusts hyperparameters to enhance predictive accuracy. Utilizing well logging data and applying machine learning techniques, the paper classifies and identifies different mechanisms causing abnormal pressures, achieving a model prediction accuracy of 0.942. The research findings highlight the predominant role of the undercompaction mechanism, accounting for approximately 70% of the abnormal high-pressure events in the study area. Fluid expansion and shale diapirism contribute smaller but significant proportions of 10% and 20%, respectively. These quantitative insights into the pressure mechanisms are vital for optimizing drilling operations and reducing risks in oil and gas exploration. The study's hybrid approach, integrating geophysical analysis with advanced computational techniques, sets a precedent for future research. It provides new avenues for applying machine learning to understand complex geological phenomena in similar geological environments and makes a significant contribution to the strategic planning of hydrocarbon exploration and production activities.  
offshore oil and gas wells; abnormal formation pressure; Rio del Rey Basin; undercompaction mechanism; high-temperature fluid expansion; machine learning classification; Bayesian optimization Article
## 3 Numerical Results and Discussion
### Models for Verification
#### 3.1.2 The Gap Model  
As plotted in Figure 6, a similar oscillator subjected to pure-collision nonlinear force is applied in this section. Since no friction force is introduced in this system, it is referred to as the gap model.  
The equation of motion for this model is exactly the same with Equation (33) while the nonlinear force has the following form:  
\[f_{\tau}=\left\{\begin{array}{cc}k_{\tau}(x-g)&x\geq g\\ 0&x<g\end{array}\right. \tag{34}\]  
where \(g\) is the prescribed clearance and \(k_{y}\) is the normal stiffness.  
Figure 7 shows the response of the system with different normal stiffness and initial gaps. It can be seen that an offset of the critical speed exists in both cases. With the increase of normal stiffness or decrease of initial gap, the critical speed tends to increase. And multi-solution region emerges as a result. Results have been shown to be consistent with the work done by [PERSON] et al. [31]. Time-domain displacement results at \(\omega\) = 7.029 rad/s obtained by the HBM and TMM are compared in Figure 8 and the accuracy of the HBM can be proved.  
Moreover, the sensitivities of the response with respect to normal stiffness and initial gaps are obtained, as illustrated in Figures 9 and 10. It can be seen that multi-solution regions also appear in the sensitivity curves. With an increase of normal stiffness, the multi-solution region tends to grow larger, as shown in Figure 9a. Moreover, with an increase of the initial gap, the sensitivities of the system increase and the multi-solution region shrinks, as illustrated in Figure 10a.  
Figure 6: The gap model subjected to pure-collision nonlinear force.  
Figure 7: Response curves of the gap model with different normal stiffness and initial clearances: (**a**) Response curves of the system at \(g\) = 10 m; (**b**) Response curves of the system at \(k_{y}\) = 100 N/m.  
Figure 8: Response curve and hysteresis loop of the gap model obtained by HBM and TMM at \(\omega\) = 7.029 rad/s, \(g\) = 10 m and \(k_{y}\) = 100 N/m: (**a**) The response curve; (**b**) The hysteresis loop.  
Figure 10: Sensitivities of the response curve of the gap model to initial gaps at \(k_{y}\) = 100 N/m: (**a**) First-order sensitivities; (**b**) Second-order sensitivities.  
Figure 9: Sensitivities of the response curve of the gap model to normal stiffness at \(g\) = 10 m: (**a**) First-order sensitivities; (**b**) Second-order sensitivities.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 2 Background
### FPGAs in Wearable Medical Devices
#### 2.3.2 FPGA Technologies  
FPGAs are generally categorized into anti-fuse, SRAM (Static random-access memory), flash, or hybrid (SRAM + Flash) based on their implementation technology.  
Antifuse FPGA: Anti-fuse FPGAs are non-volatile, meaning they retain their configuration without needing power. These FPGAs typically consume less power, making them energy-efficient. However, anti-fuse FPGAs cannot be reconfigured once programmed, limiting their flexibility. They are often used in applications requiring security against the theft of configuration data.  
SRAM FPGA: SRAM-based FPGAs are highly flexible and reprogrammable, allowing numerous reconfigurations even during operation. This adaptability makes them suitable for a wide range of applications. These FPGAs store their configuration data in static RAM cells and require external memory to hold the configuration bits, which are transferred upon powering up. Their volatile nature necessitates reprogramming every time they are powered on, leading to higher power consumption. They also have higher static consumption due to leakage currents.  
Flash FPGA: Flash-based FPGAs, on the other hand, use non-volatile memory cells, retaining their configuration data even when powered off. They combine the advantages of both SRAM and anti-fuse technologies. They are non-volatile like anti-fuse FPGAs and reprogrammable like SRAM FPGAs, offering flexibility, instant-on capability, and lower power consumption, with reduced static power due to minimal leakage.  
Hybrid FPGA: Hybrid (SRAM + Flash) FPGAs combine the advantages of both SRAM and flash technologies by providing on-chip non-volatile configuration memory. This way, they offer the flexibility of SRAM with the non-volatility of flash.