esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 4 CFRP Conductivity Measurements and Techniques for Non-Destructive Testing Applications
### Conductivity for Non-Destructive Testing  
In spite of the above advantages, CFRPs are not used at their full potential in critical load-bearing structures because it is difficult to track the initiation and propagation of damage. As a consequence, CFRPs are not designed optimally, but they are designed with a high safety factor. Therefore, long-term critical structural applications, require low-cost and reliable techniques to detect possible degradation [57; 121; 122].  
Defects in all composite structures may result in a loss of mechanical properties and tend to increase with structural complexity. Defects arise in the production process, when they are expected to be repaired if detected, and during service [8]. During the manufacturing process, layers of pre-impregnated fibers or thin plies, are stacked according to the given order and the obtained whole laminate forms a structurally sound component with desired properties. However, unwanted defects may be introduced in the manufacturing process. It is difficult to detect many of these so-called defects, which significantly affect the structural integrity. Material degradation and damage may be generated in the used composite components. Such damage is typically caused by impact loading and rapid local temperature changes, continuous cyclic loading. The extent and nature of such damage are related to many parameters including prevailing environmental conditions. When the defect size reaches a critical value, it will lead to a catastrophic failure and severe consequences. Therefore, it is necessary to identify and explore such damages and defects in the manufacturing process and service life of composite materials [17; 49].  
Delamination is one of the common failure modes. In all the types of the damages in laminated structures of CFRP products, the proportion of delamination damage reaches 60% due to the low through-thickness strength. Therefore, delamination characteristics largely determine the safety of CFRP products. A delamination is a crack in the resin component between plies of different fiber orientations. Delamination seriously affects the performance of CFRP products and may cause serious failures because it is difficult to be visually detected [106; 123]. Small areas of delamination can cut off more than 50% of the compression strength. Delamination decreases the stability of the load-bearing fibers and may lead to a localized buckling-type of failure mode even under low loads [124; 125; 126]. Previous studies on delamination focus on delamination detection through electrical conductivity and the way to prevent delamination by improving CFRP conductive performance.
## 4 Discussion
### Impact of Precision Levels  
Compared to using hour-level precision, the use of calendar days systematically underreports AKI. For example, the incidence of AKI detected using a three calendar day window (14.2%) is closer to the 52-h window (13.9%) rather than a 72-h window (15.1%). Therefore, if calendar days are used, the data advocates rounding up to one calendar day longer than the intended observation time. If narrow observation windows are chosen (e.g., 24 or 48 h), the 4-h margin allows for additional pairs of SCr to be compared, which improves surveillance and increases AKI detection. Of note, new cases of AKI that were detected using the 4-h margin occurred 1-2 days later than cases detected without the 4-h margin (Supplementary data, Table S1 and Figure 2). If expanded observation windows are chosen (e.g., 168 h), the usefulness of the 4-h margin diminishes.
# Emerging Diagnostics in _Clostridioides difficile_ Infection
## 6 Conclusions  
Given their ability to persist across a network of aerobic and anaerobic environments found in community, industrial, and hospital settings, the monitoring and diagnosis of \(C\). _difficile_ endospores and vegetative cells require an integrated solution that substantially reduces community reservoirs and associated chains of transmission [9]. Increased transmission has been associated with the sporulation rates in animal and human hosts and the economic conditions (suspension of antibiotic stewardship) that favor an increase. This review initially highlighted the different spectroscopic and cytometric techniques used to examine the properties of endospores that enable their resistance to numerous environmental stressors and the microbial (dysbiosis) and chemical factors that influence germination. The surveillance potential of the combined roles of mass spectroscopy and PCR-ribotyping with machine learning in discerning epidemiological and genetic factors was also discussed.  
A consistent theme throughout this review is the inability of employed molecular techniques to sufficiently differentiate between CDI and asymptomatic colonization or the presence of non-toxic strains. Currently, no FDA-approved single rapid test can reliably diagnose \(C\). _difficile_ disease even in the presence of good-quality fecal samples [122; 123; 124]. While NAA offers excellent sensitivity for detecting toxigenic \(C\). _difficile_, they can also identify patients colonized with \(C\). _difficile_ without toxin production, potentially leading to overdiagnosis and overtreatment. On the other hand, toxin EIAs (enzyme immunoassays) provide good clinical specificity in symptomatic patients but lack sufficient sensitivity to rule out CDI. Additionally, the presence of toxins does not correlate with true CDI in asymptomatic carriers or cured patients. Even with the advent of ultra-sensitive single molecular assays and real-time monitoring, a definitive diagnosis for CDI remains out of reach, partly due to the absence of institutional agreements and poor patient selections.  
Regardless of the testing method, enhancing test utilization is essential for accurately identifying true \(C\). _difficile_ cases in animals and humans. Furthermore, to implement meaningful interventions that positively affect human and animal health, it is crucial to go beyond the hospital setting and cultivate collaborative relationships among industry, government, veterinarians, clinicians, and researchers.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study.  
The author declares no conflicts of interest.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 3 Model Design
### Influence of Blockage on Satellite Communication Transmission  
High-frequency band satellite communication is advantageous due to its small frequency bandwidth equipment and strong anti-interference tendencies. However, high-frequency band satellite communication is greatly affected by the atmospheric environment [15].  
At communication frequencies above 10 GHz, rain is the most influential propagation phenomenon on satellite communication links [16]. When the frequency is higher than 20 GHz, heavy rain may cause severe signal attenuation, to the point of interrupt  
Figure 5: Overall process of the system.  
ing the communication link. Therefore, this paper focuses on high-frequency satellite communication above 20 GHz.  
In all weather conditions, rainfall has the greatest impact on communication congestion, so this paper calculates the communication congestion according to rainfall and other parameters. Based on previous studies on rain attenuation [16], the estimated value of blockage corresponding to different rainfall types can be calculated by Formula (1). Assuming that the frequency band of the satellite is f (Hz), the cloud attenuation corresponding to this frequency band is c (dB/km), the cloud incident angle of satellite communication is a, and the cloud thickness is t, then the blockage of satellite communication in the transmission process can be obtained according to the formula [16]  
\[\text{blockage}=\text{t (km)}\times\text{csc (a^{\circ})}\times\text{c (dB/km)} \tag{1}\]  
Table 1 shows the cloud thickness and attenuation corresponding to different levels of rainfall, as well as the blockage in the final satellite communication transmission process obtained from different incident angles. Take heavy rain as an example, assuming that the cloud height is 8 km, the absorption loss of rainfall to radio waves is 20 dB/km, and an antenna elevation angle of 45 degrees, the rain attenuation of frequency band downlink is 226 dB. The table shows that when rainfall increases, the route loss of satellite transmission increases. If the estimation of rain attenuation is not accurate, the design of parameters is unreasonable, which will affect the quality and effectiveness of communication. Therefore, prediction of blockage on communication links is crucial in satellite communication engineering design.
## 4 Experimental Results  
A commercial antenna operating in the HF, VHF, and UHF bands was selected as a reference antenna to verify the performance of the proposed antenna [28]. When the same power is transmitted from the transmit antenna [29], the received power of the proposed and the reference antenna was measured and compared.  
Figure 10: Simulated realized gain of a proposed antenna on the +\(z-\)axis.  
Figure 9: Simulated radiation pattern of the proposed antenna in the xz and yz\(-\)planes: **(a)** 14 MHz; **(b)** 200 MHz; **(c)** 370 MHz.  
Figure 11 shows the received power measurement environment. The size of the proposed antenna is \(500\times 500\times 15.8\) mm\({}^{3}\) and the height of the commercial reference antenna is 1000 mm and the diameter is 37 mm. The far-field conditions of the antennas are 0.6 m and 2.4 m or more based on the size of each antenna and the highest operating frequency, respectively. The transmit and receive antennas were spaced 70 m in the line of sight. According to the datasheet, the reference antenna is placed on the ground plane with a dimension of \(1000\times 1000\) mm\({}^{2}\). The main polarization of the reference antenna and transmit antenna is vertical. Therefore, when measuring the proposed antenna, the received power of vertical and horizontal components was measured by rotating the proposed antenna according to the line shown in the Figure 11.  
Figure 12 presents the measured received power of the proposed and reference antennas. The transmitted power from the transmit antenna is 17 dBm, and the received power is measured at 10 MHz steps. The received power difference between the reference and proposed antennas is 1.9 dB on average. The difference in received power is considered to be caused by the difference in the proposed and reference antenna sizes. However, considering the size and height difference between the proposed and reference antennas, the proposed antenna has good receive power characteristics. Therefore, the proposed antenna can be used as a receive antenna in HF, VHF, and UHF bands.  
Figure 11: Received power measurement environment.
# Vertical Dynamic Response Prediction of the Electromagnetic Levitation Systems
## 2 Vertical Dynamic Modeling of the Electromagnetic Levitation System
### Analysis of Typical Speeds  
We select 80 and 160 km/h as typical speed values for analysis, and its amplitude spectrums are shown in Figure 5. The track amplitudes \(A_{r}\left(\lambda\right)\) are labeled as \"Track\" in Figure 5. According to Figure 5, the amplitudes of the track irregularities generally rise with the wavelengths, which means that the longer wavelength track irregularities have larger amplitudes.  
According to Figure 5, when the speed is 80 km/h, the maximum amplitude response values of the two levitation gap appear at the wavelength of 8.3 and 11.3 m, respectively. The amplitude response of the front levitation gap is smaller than that of the track in a wide range of wavelengths, and the amplitude response of the back levitation gap is larger than that of the track in a certain range of wavelengths. The back levitation unit has a good tracking ability for the track input when the wavelength is greater than 9.88 m.  
When the speed is 160 km/h, the maximum values of the two levitation gap appear at the wavelength of 16.5 and 18.6 m, respectively. The two levitation units have good tracking ability for the track input when the wavelength is greater than 19 m.  
From the analysis above, it can be concluded that with the increase of the speed, the wavelength range that the levitation units have strong tracking ability decreases. However, due to the larger track amplitude of the longer wavelength irregularity, the maximum points of the amplitude spectrums of both levitation units are larger with the increase of the speed, which leads to the rise of the amplitude spectrum area.  
Figure 5: Amplitude spectrums at different speed vs. track wavelengths. (**a**) at 80 km/h, (**b**) at 160 km/h.
## 5 Experiments
### Experimental Results  
Figure 4 reports the number of generated concepts \(\sharp\)_concepts_ and the running time of Jay, DFSP and MNG-SC algorithm on the tested datasets. As we can see, the performance of both DFSP and our MNG-SC algorithm will degrade with the increase in the number of concepts. At a glance, our algorithm significantly outperforms than its competitors on all considered datasets.  
Table 3 provides detailed information about a comparison between the MNG-SC algorithm and other algorithms for computing stability in terms of running time. In the second column of the table, the MNG-SC algorithm sharply outperforms [PERSON]'s algorithm by many orders of magnitude. It is mainly related to the time-consuming process of extracting minimal generators of [PERSON]'s algorithm. In contrast, MNG-SC can directly obtain maximal non-generator through the proven equivalence between maximal non-generator and lower neighbor concept (c.f. Theorem 2). Besides, \"-\" means that the [PERSON]'s algorithm end with memory overflow on the remaining Football and Neural datasets. It is because the extracting minimal generator method of [PERSON]'s algorithm is sensitive to the number of minimal non-generators. Compared with the third and fourth column, the MNG-SC algorithm also significantly outperforms than [PERSON]'s algorithm and DFSP algorithm. Indeed, because [PERSON]'s algorithm needs to explore the entire concept lattice to calculate the concept stability value, MNG-SC only explores the lower neighbors of the formal concept to calculate the stability. Besides, our algorithm does not need to design a separate method of calculating minimal generators such as DFSP, which reduces the computational complexity. DFSP does not rely on existing knowledge in concept lattice to calculate stability value, which may cause a waste of computing resources, even in the case of small data.  
However, it is because our algorithm relies on the lower neighbor concepts of the concept \((A,B)\) to speed up the stability calculation. The more number of the concept's lower neighbor concept, the more obvious the limitation of our algorithm. Noted that an interesting situation in the experiment that time-consuming concepts have a common feature that the number of their lower neighbor concepts is more than 25. For example, when our algorithm runs on Football dataset, a concept that has 25 lower  
Figure 4: The number of generated concepts and the running time of Jay, DFSP and MNG-SC on the experimental datasets.  
neighbors accounts for about 69% of the computation time of all concepts. In addition, another concept that has 24 lower neighbors accounts for 12% of the total time, while the remaining 3269 concepts only account for 19%. However, the number of such time-consuming concepts accounts for a small proportion of the total, and it is easy to limit it by adding judgment conditions.
## 3 Results and Discussion
### Characterization of Nanocom
#### 3.1.2 The XRD Analysis of AC/CFO  
The XRD pattern of AC/CFO with the crystal phase structure of magnetic nanocomposite had diffraction peaks at 20 of about 18.42\({}^{\circ}\), 30.25\({}^{\circ}\), 35.58\({}^{\circ}\), 53.59\({}^{\circ}\), 57.15\({}^{\circ}\), 62.71\({}^{\circ}\) and 74.14\({}^{\circ}\) (Figure 2).  
These peaks, that are well indexed to the cubic spinel phase of CoFe\({}_{2}\)O\({}_{4}\), are also in agreement with the literature data (JCPDS 96-591-0064) and reveal that, even in the reaction with AC, the crystal structure of the CoFe\({}_{2}\)O\({}_{4}\) is well preserved. Similar results have been reported in the literature for the magnetic oxide-supported activated carbon [37].
## 3 Reconstruction Method and Protection Algorithm
### Characteristics of Current Trajectory Images
#### 3.2.3 Data Set Processing  
The principle of deep learning largely depends on its generalization ability. The more diverse and comprehensive the data features used to train the neural network, the stronger its generalization capacity, resulting in better fault identification accuracy, robustness, and adaptability to various operating conditions. To address this, a data-enhanced convolutional neural network (A-CNN) based on cross-overlapping datasets is proposed. Waveform data from both the half cycle (10 ms) and one full cycle (20 ms) after the fault occurrence are used for A-CNN training and learning. The first 10 ms data set is enhanced to ensure rapid fault detection for quick system response, while the second 10 ms data set acts as a backup to ensure reliability and stability over a broader range of conditions. Together, these datasets improve the algorithm's performance under various fault scenarios, especially in complex grid environments with high variability and uncertainty. As shown in the train set example, the data set processing is illustrated in Figure 11, highlighting critical steps for data augmentation, data cleaning, feature extraction, and training optimization. This dual data set approach ensures rapid fault identification while maintaining a high degree of reliability, accuracy, consistency, and fault tolerance, even in the most challenging environments, ensuring long-term operational stability and resilience.  
Figure 11: Processing of cross-overlapping data set.  
Figure 10: Current trajectory images (AB-phase internal faults). (**a**) Phase A; (**b**) phase B; (**c**) phase C.  
Considering the influence of CT saturation, the data set consists of two types of CT saturation and normal under various fault conditions. The fault conditions mainly include fault type, fault location, fault resistance, and whether CT is saturated. A total of 5664 current trajectory images were collected, including 2832 pictures of 10 ms after the fault moment and 2832 pictures of 20 ms, with a resolution of 128 \(\times\) 128. Images are divided into a training set and a test set according to the ratio of 4:1, and Gaussian noise is added to the training set for data enhancement.
## 4 Probabilistic Fatigue Reliability Assessment  
### S-N Approach and Miner's Rule  
The Miner's linear fatigue damage accumulation theory together with the \(S\)-\(N\) curve (stress life) has been widely employed for structural fatigue damage assessment. Design codes such as AASHTO [29] and Eurocode 3 [30] have proposed a set of \(S\)-\(N\) curves, defined as the nominal stress range versus life in cycles, for fatigue assessment on various detail categories. In the present study, the AASHTO specification is used, in which the \(S\)-\(N\) curve is expressed as follows:  
\[S=\left(A/N\right)^{1/m}, \tag{10}\]  
where \(S\) = constant stress range, \(N\) = fatigue life of a detail, \(m\) = 3 is the slope of the \(S\)-\(N\) curves, and \(A\) = detail constant.  
It is noteworthy that the fatigue stresses of the suspenders caused by the stochastic loads are random variables, whereas the \(S\)-\(N\) curve is originally applicable for constant-amplitude fatigue stresses. To this end, the [PERSON]'s linear fatigue damage accumulation theory, termed as Miner's rule, was proposed to account for the variable amplitude stresses [31]. The Miner's rule simply assumes that the fatigue damage fraction at any particular stress range level is a linear function of the corresponding number of cycles, and therefore, the total fatigue damage accumulation can be expressed as the sum of damage caused by each individual stress range level.  
Figure 5: Comparison between the predicted distribution of the gross vehicle weight of various vehicle types in the simulated stochastic traffic flow and the target distribution (**a**) V1, (**b**) V3, (**c**) V5, and (**d**) V7.  
\[D_{a}=\sum\
olimits_{i}\frac{n_{i}S_{ri}^{m}}{A}, \tag{11}\]  
where \(D_{a}\) = the fatigue damage accumulation and \(n_{i}\) = the number of cycles at stress range \(S_{ri}\). The number of cycles \(n_{i}\) and stress range \(S_{ri}\) are obtained from the stress range bin histogram using the rain-flow counting method [31].  
The fatigue damage accumulation \(D_{a}\) is typically equal to 1.0 at failure, while it may be less than 1.0 due to various uncertainties [32]. By applying the Miner's rule, the equivalent fatigue stress range and corresponding number of cycles can be derived as follows:  
\[S_{re}=\left(\sum\
olimits_{i}\frac{n_{i}}{n_{t}}S_{ri}^{m}\right). \tag{12}\]  
where \(S_{re}\) = the equivalent fatigue stress range and \(n_{t}\) = \(\sum\
olimits_{i}(n_{i})\) is the total number of stress cycles. It should be noted that the \(S_{re}\) and \(n_{t}\) in Equation (12) are computed from individual stress bin histogram under one combination of wind and traffic loads.
## 2 Materials and Methods
### Feature Selection and Preprocessing  
A flowchart of feature selection and preprocessing is presented in Figure 2. The standardization of the time-dependent continuous variables was performed using the below formula:  
\[\Delta\text{Value}_{\text{standard}}=\frac{\text{measured value one year after surgery }-\text{measured value before surgery}}{\text{measured value before surgery}} \tag{1}\]  
The standardization of all the included variables, which is a common requirement for machine learning algorithms, was then performed. Standardization adjusts the data distribution of each feature with a mean of zero and a standard deviation of one:  
\[\text{Data}_{\text{standard}}=\frac{\text{Data}-\overline{\text{X}}}{\text{SD }}, \tag{2}\]  
Figure 1: Example of body composition measurement using automated artificial intelligence software. Skeletal muscle area (red), subcutaneous fat area (brown), and visceral fat area (purple) decreased (**b**) one year after gastrectomy compared to (**a**) before surgery.  
Figure 2: Flowchart of feature selection and preprocessing. Asterisk (*) indicates the number of deaths.  
where \(\overline{\text{X}}\) and SD are the mean and standard deviation values, respectively, for each feature from the training dataset. The standardization was applied to both training and validation datasets.  
Through the standardization process, 47 features were generated from 59 variables, as presented in Supplementary Table S1.  
In order to select critical features which significantly influence overall survival rates, we initially investigated the contribution of each of the 47 features to the overall survival rates with feature importance analysis using eXtreme Gradient Boosting (XGBoost) algorithms [20]. Two expert clinicians also selected important features based on their clinical expertise. Based on feature importance analysis and experts' opinions, we finally selected the features for AI training.