esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Sparse Signal Transmission Framework
### Process of Sparse Signal Transmission  
According to the above discussion and former research, it only requires 20% of the whole data to recover the original signal (such as temperature and light [15]) with no more than 20% errors. While considering that the transitional region is formed mainly due to the path loss with small vibration caused by noise, with a 20% packet reception rate being settled to sufficiently reconstruct the original signal, a rough distance can be determined for stable use.  
What is more, though the area of three regions in the spatial characteristic of the link varies with transmission environment, transmitting power _etc._, the transitional region occupies a large area compared to the connected region [28]. Stable use of a lossy link with our method can surely broaden the communication scope extensively.  
Next, we give a sketch of sparse signal transmission and then propose the problem formulation. The process of the CS-based reliable information transmission methods via lossy links is shown in Figure 3. It is composed of a lossy link transmission-random compressed sensing period and a signal reconstruction period. Instead of compressive sampling, we model the data packet loss as the measurement process. First of all, data obtained after signal sampling is directly transmitted through lossy wireless links. During wireless data transmission, there will be data loss. According to the CS theory, it is sufficient to  
Figure 2: Signal sparsity in sensor networks. (**a**) Sparsity of \(\mathrm{CO}_{2}\) emission in the Port of Oakland; (**b**) sparsity of a car engine.  
capture the signal with a small number of compressive measurements. Thus, the sink node does not care which part of the information is successfully transmitted, as long as: (i) data loss caused by lossy links is random to some extent; (ii) there is a sufficient number of received packets to allow for the reconstruction of the information; and (iii) the original sequence of the received packets is known for the convenience of designing the projection matrix. Therefore, unlike the traditional methods, our proposed method can just disregard these lost packets. Then, based on the received broken data, the receiver node designs the projection matrix \(\Phi\) through random compressive sensing (RCS) [33] as Equation (7) and gets the measurement data \(\mathbf{y}=\mathbf{\Phi x}\). Finally, through transformation and orthogonal matching pursuit recovery algorithm, the original signal will be reconstructed from those received broken measurement data \(\mathbf{y}\).
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 3 Experiments and Analysis
### Metal Identification  
To validate the ability of this magnetic field imaging system to identify metal objects, a pair of metal scissors was used as the sample. Figure 16a exhibits the experimental environment for magnetizing the scissors by generating an excitation magnetic field with a Helmholtz coil. The identification of the metal scissors after removing the background magnetic field using the software algorithm is shown in Figure 16b. Due to the tilting of the metal scissors during the measurement process, the bottom of the scissors is farther away from the magnetic sensing probe, which makes it harder to detect. The image displays the outline of the metal scissors clearly, and the detailed features of the scissors within the red  
Figure 14: Wireless charging coil and its magnetic field distribution images: (**a**) the magnetic field distribution of the coil inside the phone; (**b**) measured wireless charging coil; (**c**) 2D magnetic field distribution image of the coil; (**d**) 3D magnetic field distribution image of the coil.  
dashed circle in Figure 16a are also presented. This system identified a hole and several bumps on the surface of the metal scissors. It demonstrates the capability of this system to image detailed features of samples and its potential in the field of sample identification.
## 3 Internal Load Monitoring  
The literature has shown a wide range of methods for monitoring internal load, primarily including HR, RPE, monotony, strain training, TRIMP, physiological markers, and the use of diaries and questionnaires. Among these methods, HR is the most commonly used response to measure and quantify the internal load of players during training sessions and matches, due to the functional meaning of this variable [5; 38]. However, combining these methods is often recommended for a more comprehensive understanding of training internal load [2; 3].  
An example of the enhanced potential of combining two or more internal load methods to analyze the physiological demand imposed with training is evident in studies reporting a low association between cognitive perception of effort (e.g., RPE) and metabolic responses (e.g., blood lactate accumulation) [3; 4]. Therefore, despite the assumption that individuals can monitor physiological stress using perception, studies have shown that physiological variables, rather than perceptual ones, can better distinguish the training demands of similar exercise modes that are planned differently [5; 6; 13].  
In addition, it is interesting to consider the variability in responses to training (i.e., conditioning and performance adjustments), which can be observed when comparing different individuals experiencing the same training load (i.e., inter-individual trainability) and when comparing the same individual across different seasons (i.e., intra-individual trainability) [13]. These variabilities indicate that individuals not only respond and adapt differently to training, but their responses also oscillate over time, regardless of sex, age, and conditioning level [5,13], ratifying the importance of monitoring internal loads to avoid untoward outcomes with training [38].
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 3 BBN Design Considerations
### Mobility Management for BBN
This would let a subset of WBAN coordinators or BBN routers transmit acknowledgments so that biological sensors or mobile WBANs are not constrained to be in the proximity of a specific receiving coordinator or router for an acknowledgment. The second requirement is the clock drifting resilience which emerges in large-scale networks with random mobility, namely BBNs. In such networks, critical protocol timing needs increased clock drifting resilience. Yet, the two requirements are applicable to BBNs to ensure quite high mobility of patients in both indoor and outdoor environments. The number of acknowledgments (ACKs) in the location independence process should be customized according to the BBN application and the different WBANs' QoS demands. Clock drifting is also needed in large scale BBN scenarios. An extensive WBAN mobility model (MoBAN) is presented in [71], to deal with intra and extra-WBAN communications, for different WBAN and individual node postures. Extra-WBAN protocols take care of communications between a WBAN and its environment, with potentially several WBANs as well as an ambient network. The mobility prediction is based on a Markov model. The local motions of sensor nodes are defined by the individual posture, and the whole WBAN mobility by global WBAN posture, in order to plot the connections of the sensor nodes with each other and the WBAN with the surrounding environment. Authors use a one-level Markov template to model random posture patterns, in any location. As an example, different rooms in a building can be thought of as having statistically different posture patterns. The posture pattern in a bedroom is surely different from the pattern in the living room or the kitchen. The great benefit of MoBAN is the support of extra-WBAN mobility. Since BBNs are intensively affected by WBAN's movements, MoBAN could be a good candidate to provide mobility information that could be used as a routing parameter for BBNs, and according to WBANs specific needs/applications, this information could represent a mobility prediction parameter. In addition to the topology changes and/or mobile WBAN location considerations, that are usually made in reactive routing schemes, accurate mobility prediction methods should be adapted to anticipate the route reconstruction before that topology changes occur. Then genuine ubiquitous BBN applications could be fulfilled with high link quality and reliable data delivery.
## 3 Design and Implementation of the Multi-Attribute PLA Scheme for LoRaWAN
### Dataset Description  
The dataset used in this study originates from the LoRaWAN at the Edge (LoED) Dataset [18], a real-world dataset capturing network behavior under realistic conditions. It contains 155,422 entries and includes attributes such as the timestamp of the signal (Time), a unique identifier for each sensor (Device Address), the gateway receiving the signal, the RSSI, and SNR. To extend the dataset for the proposed multi-attribute PLA scheme, two additional attributes were synthesized. The BL was modeled to decrease over time, reflecting real-world sensor battery depletion, with sudden changes flagged as anomalies. The altitude attribute was simulated to represent static sensor installation heights with minor variations to account for GPS inaccuracies. These additions transformed the dataset into a multi-attribute dataset, combining real-world RSSI values with synthetic BL and altitude data, thereby simulating environmental and signal variations critical for evaluating spoofing detection mechanisms.
## 3 Experiments
### Results  
#### 3.6.1 Simulation  
Figure 3 presents the results of the simulated testing datasets with 60 views. The ROI is indicated with the dashed square, which is enlarged and shown for better visualization. The image profiles along the blue line in Figure 3 are drawn and shown in Figure 4.  
As expected, severe streak artifacts introduced by sparse-view sampling exist in FBP reconstruction and much less in the results of other methods. However, for DD-Net, the image is blurred, and some image structure still vanishes. For DLFBP, great unpredictable artifacts exist, which affect the visual observation of the image structure. HD-Net and DDPC-Net efficiently suppress artifacts and restore the vanished structure, while the result of HD-NET is a little more blurred compared with DDPC-Net. As presented in Figure 4, the intensity curves in the images from DLFBP and DDPC-Net are noticeably closer to the ground truth, while the intensity curve of DLFBP is relatively more undulating. Table 1 lists the FSIM and IW-SSIM values of the images in Figure 3. DDPC-Net achieves at least 5% higher values in terms of FSIM and IW-SSIM, which support the conclusion of the visual observation.  
Table 2 lists the average FSIM and IW-SSIM values of the results of the mentioned five methods. It can be observed that as the number of sampling views increases, the average FSIM and IW-SSIM values increase, and the methods except for FBP obtain values higher than 90%. In addition, DDPC-Net achieves slightly better values than the other methods. The \(rel1\) of the average values of the average FSIM and IW-SSIM values are drawn in Figure 5. The same conclusion can be drawn that DDPC-Net outperforms other methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Evaluation** & **FBP** & **DD-Net** & **DLFBP** & **HD-Net** & **DDPC-Net** \\ \hline FSIM & 0.5632 & 0.8922 & 0.8780 & 0.9153 & 0.9652 \\ IW-SSIM & 0.5673 & 0.9278 & 0.8922 & 0.9300 & 0.9793 \\ \hline \end{tabular}
\end{table}
Table 1: The FSIM and IW-SSIM values of the images in Figure 3.  
Figure 4: The profiles along the blue solid line in Figure 3.  
Figure 3: The reconstructed images of one of the results of the simulated testing datasets. This sinogram has 60 sampling views, and the reconstructed images were obtained by five methods.  
Moreover, Figure 5 shows that the image quality of the results decreases drastically with the decrease in number of the sampling views. Table 3 lists the efficiency of the four deep learning methods. As expected, the efficiency of the dual-domain reconstruction frameworks is slightly lower than that of the single-domain reconstruction framework, both in terms of the number of parameters and runtime. However, compared to HD-Net, which trains enhancement networks in the projection sinogram domain and image domain separately and cascades them, DDPC-Net is more efficient. This indicates that the proposed method can balance image quality and efficiency.
## 3 Infrared Image Segmentation
### Copper Rod Segmentation Based on the Region Growing Algorithm
#### 3.2.2 Growing Criteria and Stopping Condition  
The growth criterion of the region growing algorithm is a comparison rule used to determine the similarity between the seed and adjacent pixels or regions for extension to a larger region [16; 17]. The stopping condition is a condition used for stopping regional growth if it does not match the growth criteria or exceeds the growth range [18; 19]. In this paper, the gray difference discrimination method is used as the growth criterion and the limiting growth range condition is used as the stopping condition to segment each of the copper rods in the infrared image.
## 1 Introduction
This work aims to enhance the early classification accuracy of ASD using high-dimensional sMRI data for children aged 5 to 10 years, as well as to identify important biomarkers associated with ASD. To achieve this, a comparative empirical study is conducted using different optimization algorithms combined with seven ML algorithms using two public datasets from the Autism Brain Imaging Data Exchange Initiative (ABIDE) and local data from King Abdulaziz University (KAU) Hospital. To our knowledge, the KAU dataset is the first Saudi dataset used in ML applications for ASD classification. Recursive feature elimination with cross-validation (RFECV), Boruta and GWO-based algorithms for FS, and random search algorithm and GWO algorithm for hyperparameter tuning were all investigated. The impact of age and gender on classification performance is also examined. This study addresses the following research questions:  
1. Can the proposed FS methods improve the accuracy of ML models in ASD classification? 2. Which of the proposed optimized models performs the best in predicting ASD in terms of accuracy on the two public datasets? 3. Does combining personal features data with sMRI yield better results in ASD classification compared to using only sMRI data? The reminder of this paper is organized as follows. Section 2 reviews the related work. Section 3 describes the materials and methods employed in this study. In Section 4, we present and discuss the results and limitations of the research and suggest future work. Finally, Section 5 summarizes and concludes the paper.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 4 Simulation Results and Discussion  
This section examines the impact of the PMGS on the rate of overvoltages at the set-up (boost) transformers and control circuits in two diverse case studies, a single wind turbine and a pair of turbines. Based on the installation of several additional electrodes either at the local transformer or on the tower grounding side and adding two rings to increase the stability and reliability of grounding protection from the PMGS model (see Figure 1), the influence of other high-frequency components such as high-frequency SA and FD cables are taken into account to arrive at a completely safe model for further transient studying.  
Each turbine in a pair of turbines is equipped with a step-up transformer, which boosts the turbine generator output voltage from a few hundred volts to Medium Voltage (MV) distribution levels. These wind turbine step-up transformers are at a high risk of insulation, dielectrics stress and failure because they can cycle from low to high loads several times a day due to the nature of the wind turbine, which operates at continually changing power levels. As a result, the protection of such transformers is always a priority. Given this perspective, voltages at two different locations within the wind turbine are studied, on the LV side of the boost transformer and on the HV side from node to ground, to obtain the peak SA voltage. The other significant area that needs to be protected is the control system under nonlinear loads, as any damage or disruption to the control system would lead to down time in the operation of the single wind turbine and the pair of turbines [42].  
The peak value criteria for voltages are provided in the IEC standards. This study assumes that this amount is 2.5 kV for electrical equipment and 1.5 kV for electronic circuits such as LV control equipment.
# Model-Based Methods to Produce Greener Metakaolin Composite Concrete
## 2 The Strength Evaluation Model  
### A Hydration-Based Strength Model of Composite Concrete  
The strength evolution of hardening concrete closely relates to binder hydration. As binder hydration increases, the hydration products can fill the pore space and contribute to the development of strength. Moreover, regarding the metakaolin composite concrete, because cement and metakaolin reactions coincide, metakaolin composite concrete shows a more complex hydration process than plain concrete without metakaolin [20].  
In our previous studies [21; 22; 23], a composite hydration model was presented for metakaolin composite concrete. Cement hydration and metakaolin reaction were simulated separately. Moreover, the interactions between the reactions of metakaolin and cement were considered using the contents of calcium hydroxide and the capillary water content.  
The hydration extent of Portland cement can be calculated as \(\alpha=\int_{0}^{t}\!\left(\frac{da}{dt}\right)dt\), where \(\frac{da}{dt}\) is the rate of Portland cement hydration, and the degree of reaction of metakaolin can be calculated as \(\alpha_{MK}=\int_{0}^{t}\!\left(\frac{da_{MK}}{dt}\right)dt\), where \(\frac{da_{MK}}{dt}\) is the rate of metakaolin reaction. Our previous studies [21; 22; 23] showed details of equations for the cement hydration rate and the rate of metakaolin reaction.  
[PERSON] et al. [24] proposed that for plain concrete, strength can be expressed as a linear equation of the degree of reaction of cement. Based on a similar idea, we assumed for metakaolin composite concrete that the strength of concrete can be determined as a linear equation of the hydration degree of Portland cement and the reaction extent of metakaolin as follows:  
\[f_{c}(t)=A_{1}*\frac{C\alpha}{W}+A_{2}*\frac{MK\alpha_{MK}}{W}-A_{3} \tag{1}\]  
where \(A_{1}\), \(A_{2}\), and \(A_{3}\) are strength coefficients; \(C\), \(MK\), and \(W\) are the masses of Portland cement, metakaolin, and water in the concrete mix proportions, respectively. The unit of strength coefficients \(A_{1}\), \(A_{2}\), and \(A_{3}\) is MPa. \(A_{1}*\frac{C\alpha}{W}\) considers the effect of cement hydration on strength. \(A_{2}*\frac{MK\alpha_{MK}}{W}\) considers the effect of the metakaolin reaction on strength, and \(A_{3}\) considers strength to start after a threshold age, not immediately at time-point zero. This is like final setting (hardening of concrete starts after the time of final setting). Moreover, because the composite hydration model considers the influences of concrete mixtures and curing ages, the values of strength coefficients \(A_{1}\), \(A_{2}\), and \(A_{3}\) do not change as concrete mixtures and curing ages vary.
# Healthchain: A Privacy Protection System for Medical Data Based on Blockchain
## 2 Related Work
Because of the natural limitations of the single-server architecture, such as the trust problem of the third party and the issue of single-point failure, such schemes cannot fully guarantee the integrity and confidentiality of data, and this therefore leads to the destruction of data privacy. With the characteristics of decentralization, traceability, and non-tampering, the potential of blockchain technology in the field of electronic medicine is gradually being discovered. Therefore, many schemes based on this technology have emerged (such as [21; 22; 23]). However, these schemes still have some limitations. For example, in the electronic medical scene, the anonymity of blockchain nodes is not conducive to the traceability of transactions and related personnel--and yet if the identity is public, it will damage privacy. In addition, unlike Bitcoin [24], the particularity of the electronic medical scene means that the transaction itself is a sensitive piece of information, which is not conducive to the privacy protection of data because all transactions on the blockchain are open and transparent. In recent years, there have been many papers focusing on the use of blockchain technology to solve the problems encountered in the efficient transmission of medical data, with its need for privacy protection, such as [25; 26; 27]. In order to facilitate medical data sharing and protect the privacy of patients, this paper integrates blockchain with group signature. Taking medical institutions as group members and consensus nodes, after receiving transactions, the information is signed, and then packaged and uploaded to the blockchain, so that it cannot be tampered with. At the same time, due to the characteristics of group signature, only group managers can identify specific signers, which ensures traceability while protecting privacy. In addition, in order to further provide data confidentiality, this paper also introduces symmetric encryption and asymmetric encryption, and combines asymmetric encryption with group signature. We construct a consortium blockchain and analyze its security to prove the feasibility of the scheme.
## 3 Accuracy and Feasibility for Hardware Implementation  
The approach for obtaining quantitative performance metrics for CNRs extraction methods is based on signal reconstruction accuracy, number of samples used, and execution time. For this, a set of backscattering signals in the time and frequency domain was obtained through simulations in CST Microwave Studio, experiments in free space, and buried objects in a sandbox.  
The dimensions of the simulated objects are presented in Table 1, while the E-field vector was obtained in three different positions for every scenario. The backscattering response of a metal cylinder in free space was measured by using two RFSpace TSA600 antennas and a VNA. Likewise, the GPR measurements in a sandbox for buried objects are described in [22], where the EM responses for a metal plate and a metal can were obtained using Vivaldi antennas with operation frequencies from 0.8 GHz to 5 GHz.  
In order to measure the accuracy of the reconstruction for each method, each backscattering signal is processed using MPM, Prony's, Cauchy's, and VCM algorithms, and the reconstruction validation is calculated with \(R^{2}\) coefficient. A comparison of the number of samples used to compute the CNRs and the execution time will also provide a better insight into how these methods perform under different scenarios. This will provide a guide for an upcoming hardware implementation in a portable GPR.