esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A method of IDA analysis based on two indices is proposed in this paper. The maximum and the residual inter-story drift ratios were selected as two DMs to conduct the IDA based on the correlation of two indices for a steel frame. The impact of aftershocks on the seismic performance of buildings was evaluated by comparing the structural response under the action of both the main earthquake and the main aftershock, and the impact of the correlation of the two indices on the structural vulnerability was also evaluated. The main conclusions were as follows:  
1. When selecting the two indices for IDA and vulnerability analysis, the correlation between the two indices can be more accurately considered by adopting the two-dimensional lognormal distribution, compared with the one-dimensional normal distribution, and more convincing exceedance probability can be obtained, which solves the problem that the exceedance probability is not unique when the single-index IDA is used. Moreover, the computational efficiency of double-index IDA is not affected.
2. The larger the ground motion intensity is, the greater the proportion of the probability that the maximum and residual inter-story drift ratio exceeds the limit simultaneous to the total overrun probability of the structure; that is, the simultaneous over-limit situation will increase the influence on the structural vulnerability analysis, and the effect of aftershocks will increase this influence.
3. During the vulnerability analysis under the action of main aftershocks, in addition to considering the maximum inter-story drift ratio, more attention should be paid to the influence of the residual inter-story drift ratio on the repairability of the structure.
4. When experiencing the same earthquake intensity, the structural response increases noticeably considering the aftershock effect.
5. The IDA curve drawn by the cubic Bezier interpolation function is smooth without mutation, and in the subsequent vulnerability analysis, the data can be obtained quickly, which makes data processing easier.
6. The purpose of this method is essentially to consider the different seismic performance of a structure in a more diversified way in IDA. Therefore, this method is not applicable if only one index is required for analysis purposes.  
J.Q.: Conceptualization, Methodology, Validation, Writing--Review and Editing, Supervision; C.P.: Software, Data Curation, Writing--Original Draft, Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 51778113).  
Not available.  
The authors would like to acknowledge the financial support of the National Natural Science Foundation of China under Grant No. 51778113.  
The authors declare no conflict of interest.
## 4 Results and Analysis
### Comparison  
The proposed method was evaluated against two categories of image classification methods: (i) malware and (ii) state-of-the-art (SOTA) image classification methods. All networks were trained for consistency using our pre-processed dataset. This uniform pre-processing step allowed us to fairly compare the performance of different models on the benchmark dataset.  
To accommodate the input dimensions of our dataset, we modified the input layer of all comparison models accordingly. Subsequently, we conducted comprehensive evaluations by summarizing the performance of each deep model using standard evaluation metrics such as accuracy, F1 score, precision, and recall. These metrics provide insights into the overall effectiveness of the proposed method compared to existing approaches in both malware and general image classification tasks. This rigorous evaluation process ensures the reliability and validity of our comparative analysis.
## 2 Materials and Methods
### Registration Accuracy Assessment  
Final images and metric evolutions were visually inspected by a bioengineer with a consolidated experience in muscular MRI images to assess the overall quality of the registration and to detect any large, noticeable issues with the transformation.  
To give a quantitative assessment of the transformation accuracies, the surfaces of 4 representative hip muscles--gracilis, sartorius, glutetus maximus, and tensor fasciae latae--were manually delineated, slice by slice, on the T1w images (Figure 4) at both t0 and t1 separately with ITK-Snap [34]. Region of Interests (ROIs) volumes are listed in Table 1. Contours on t1 were then automatically deformed on the corresponding t0 volumes using the resulting transformation of each registration. Nearest-neighbor interpolation was applied to keep the integer values of the original labels.  
Finally, an additional region was derived adding the volumes of all 4 muscles (Sum Region--SR). The following indexes were then calculated:  
Figure 4: Muscle ROIs delineation on T1-w MR images in 3 orthogonal views.  
Figure 3: Main window of the graphical user interface (GUI) of the software, developed in Python, that complement the paper.  
* Dice similarity index (DSI) [40], quantifying the agreement between contours according to the degree of overlap of their volumes. It ranges between 0 and 1, where 1 indicates a perfect overlap: \[\text{DSI}=2*\frac{|A|\,\cap\,|B|}{|A|\,\cup\,|B|},\] (1)
* Mean surface distance (MSD), calculated in SimpleITK as the average symmetric distance between two contours \(A\) and \(B\): \[\text{MSD}(A,B)=\frac{1}{n_{A}+n_{B}}(\sum_{a=1}^{n_{A}}d(a,B)+\sum_{b=1}^{n_{ B}}d(b,A)),\] (2) where \(d(a,B)\) and \(d(b,A)\) indicate the shortest distances between an arbitrary voxel and the contours B and A, respectively, calculated using a 3D Euclidean distance transform.
* Hausdorff distance (HD) [41], defined in SimpleITK as the maximum of the symmetric surface distance between two contours A and B [42]: \[\text{HD}(A,B)=\max(h(A,B),\,h(B,A))\] (3) where \[h(A,B)\ =\ \underset{a\in A}{\text{maxmin}}\,\|a-b\|\] (4)  
is the directed Hausdorff distance and \(A\) and \(B\) are, respectively, the set of non-zero pixels in the first and second input contours.  
The accuracy metrics were evaluated considering the muscle ROIs, delineated at t0 and t1, having the original image resolution. Therefore, t1 ROIs were previously registered to t0 using the appropriate transformation matrix.
## 2 Basic Analysis of Prediction Models and Model Verification
### Prediction Models of Viscosity and Thermal Conductivity
#### 3.4.1 Prediction Models of Viscosity and Thermal Conductivity
In short, combined with the discussion of prediction models of viscosity and thermal conductivity, it can be found that it is important to further study the fluorine-containing functional groups and the interaction parameters among the groups to improve the predictive ability of the viscosity and thermal conductivity of electronic fluoride liquids [121; 122]. In addition, some scholars have proposed combining the group contribution method with an artificial neural network (ANN), which is used to establish the relationship between groups and thermodynamic properties to improve predictive accuracy based on a large number of experimental data [123].
## 4 Development and Perspectives for Molecular Diagnostics Using Cell-Free DNA  
Targeting cell-free DNA (cfDNA) is another powerful approach to establishing tumor-specific molecules as diagnostic markers in urine. Although DNA derived from circulating tumor cells (CTCs) is very scarce, the amount of cfDNA is significantly increased in the plasma of patients with inflammation and tumors to begin with [65]. In patients with tumors, the main body of the DNA is derived from CTCs; that is, circulating tumor DNA (ctDNA). Since cfDNA suggests tumor heterogeneity and the origin of the lesion from a site distant from that of the biopsy, it may contain mutations not found by the genetic testing of tissue using biopsy materials; in addition, because cfDNA is more abundant than CTCs, the genetic analysis of cfDNA is also highly useful. However, the half-life of ctDNA is not known exactly, but fragmentation by the action of nucleases and other degrading enzymes in body fluids is inevitable and is generally around 150 bp [126; 127]. In a large-scale DNA or RNA panel analysis for cancer, it is important to ensure the quantity and quality of nucleic acids; however, by limiting the panel size as a companion diagnosis, it is possible to analyze even a small amount of DNA or RNA. Therefore, even fragmented nucleic acids can be strong candidates for molecular pathology biomarkers. Cancer diagnosis and treatment strategies using molecular pathology biomarkers require the rapid detection of genetic changes that may affect cancer progression. In metastatic or recurrent tumors, tumor changes are generally detected. It is customary to collect tumors by tissue biopsy to identify the primary site and to detect various biomarkers by IHC or tissue in situ hybridization to detect cancer-type-specific biomarkers. Multiple miRNAs combine the changes in the expression of several kinds of miRNAs, which improves the specificity. Furthermore, a number of miRNAs have already been identified as candidate biomarkers for urologic tumors (Figure 4, Table 1). In addition to the histopathological diagnosis method, a new technique--non-invasive urine collection for the analysis of a large number of target genes by NGS and genetic analysis by liquid biopsy technology--is expected to be a more useful and higher-quality histopathological diagnosis with molecular pathological support.  
Figure 3: **Accumulation of abnormal genes due to abnormal gene repair mechanisms.** Abnormal genes accumulate as a result of impaired DNA repair function. PARP repairs the damage caused in these abnormal genes, resulting in an increase in the number of cancer cells with abnormal genes.
## 3 Results
### Cases of Different Confining Pressures  
The evolution of the shear stress \(\tau\), effective normal stress \(\sigma_{n}t\), injection pressure \(p\), slip distance \(d_{s}\), slip rate \(v_{s}\), and water injection volume under different confining pressures is shown in Figure 5. The yellow circle in Figure 4b shows that the injection pressure required for the initiation of slip was 1.70 MPa at a confining pressure of \(\sigma_{3}\) = 10 MPa. In Figure 5a,b, the injection pressures required for fault slip initiation were 7.30 and 14.48 MPa, which correspond to \(\sigma_{3}\) = 20 and 30 MPa, respectively, indicating that a greater injection pressure was needed to reactivate a fault when the confining pressure was higher.  
The peak slip rate under different confining pressures varied greatly. The peak slip rate at a confining pressure of 10 MPa was 30.95 \(\upmu\)m/s, which was 16.2 times that at 20 MPa (1.91 \(\upmu\)m/s) and 30 times that at 30 MPa (1.03 \(\upmu\)m/s). For a confining pressure of 10 MPa, as shown in Figure 4b, fault reactivation started at a dynamic slip rate of 30.95 \(\upmu\)m/s and was accompanied by an obvious 0.44 MPa shear stress drop and a nonlinear decrease in the effective normal stress. After that, the slip distance curve almost went up linearly, with the slip rate fluctuating at approximately 2 \(\upmu\)m/s. There was no sudden drop in shear stress during the whole process. There was no fault-locking phase (\(\Delta d_{s}\)= 0) after the initiation of slip, which indicated that the sliding process was stable. As shown in Figure 5a, under the confining pressure of 20 MPa, two rapid accumulations of slip distance occurred during slip at 1752 and 1809 s, respectively. Two fault-locking events (slip distance curve stopped going up) occurred at 1800 and 1950 s, indicating that stick-slip events occurred during water injection. As shown in Figure 5b, at a confining pressure of 30 MPa, five nonlinear shear stress drops were identified at 2703, 2903, 2941, 3007, and 3041 s, which corresponded to the rapid increase in slip distance. After 2900 s, the slip distance curve  
Figure 5: Time evolution of the shear stress \(\tau\), effective normal stress \(\sigma_{n}t\), injection pressure \(p\), slip distance \(d_{s}\), slip rate \(v_{s}\), and water injection volume at different confining pressures in the injection-driven shear test (**a**) at \(\sigma_{3}\) = 20 MPa and (**b**) at \(\sigma_{3}\) = 30 MPa. Yellow circles mark the injection pressure at fault slip initiation.  
increased in a stepwise manner, which also indicated that the fault changed back and forth between locking and reactivation during the slip process.
## 3 Proposed Methodology
### Intelligent Vehicular System (IVS) Framework for Real-Time Data Processing  
After the outstanding results of our novel approach, we proposed to integrate modern and trending technology with our proposed solution for real-time monitoring and management of diabetes. We used the model of an IVS for healthcare issues that use VANET technology for providing healthcare services and taking care from a distance. This IVS is embedded in a VANET environment for a prompt response, providing wireless communication even when the cellular network fails due to natural disasters, heavy traffic, or other reasons. VANET helps a group of vehicles to create and maintain a correspondence network among them where there is no infrastructure and any controller or central base station [30]. Figure 7 shows the framework of the proposed methodology for real-time monitoring of a diabetic patient when he is moving, i.e., traveling or driving. The process comprises advanced CGM wearable sensors, and data are transmitted and processed using a VANET environment. Data are sent to the vehicular server to monitor and manage retrieved values. VANET provides an ideal environment to determine emergency cases rapidly and efficiently without delay. Performance results are sent to the OBU, which is responsible for our scenario's intelligent decision. Retrieved values are compared with threshold values. If an emergency occurs, the data will be sent to the nearest available physician or VANET member with the location using the GPS information of the patient for early treatment.  
Figure 7: A novel approach for the intelligent vehicular framework for drivers’ diabetic data processing.  
Three critical kinds of communication happen when we utilize the VANET environment [31]. The correspondence between the two vehicles for transmitting information during mobility is known as V2V (vehicle to Vehicle) communication. When the two infrastructures communicate with one another for quick transmission of information, this is known as I2I (infrastructure to infrastructure) communication, and when the vehicles straightforwardly communicate with the foundation when they are connected to the Web by sending the patients' well-being information, this is known as V2I (vehicle to infrastructure) communication.  
In this recent era, scholarly pursuit in innovation has become a large part of improving technology, wireless communication, the vehicle industry, and VANETs, one of the most encouraging exploration regions. This technology aims to make vehicles, sensors, and devices intelligent so they can make decisions on behalf of a human.
# Fault Detection Algorithm Based on Dynamic Global-Local Preserving Projection
## 3 Fault Detection Based on DGLPP  
### Dynamic Characterization  
The data in industrial processes are dynamic, and a time series autocorrelation exists between process variables. The autocorrelation between sample points can be characterized well by extending the original matrix into a dynamic matrix. Therefore, the first l-time delay samples can be introduced to construct the augmentation vector of the samples at the current time.  
\[X_{k}(l)=[X(k)\ X(k-1)\ \cdots\ X(k-l)] \tag{5}\]  
where \(X(k-l)\) is the \(m\) dimensional variable at time \(k-l\) and \(l\) is the delay length.
# A New Circuit Design of AC/DC Converter for T8 LED Tube
## 2 The Proposed LED Converter for a T8 LED Tube  
### Power Stage  
Figure 2a shows the overall stages of the proposed LED converter, which consists of a power stage for constant current control and a ripple remove stage to reduce the output ripple. The power stage is physically divided into two driving circuit that are electrically linked though the LED module [5]. Figure 2b shows a simplified circuit diagram of the proposed LED converter with separated driving circuit for a T8 LED tube.  
The proposed LED converter employs a single-stage buck-boost converter topology and consists of an AC driving circuit for rectification and a DC driving circuit for constant current control. The AC driving circuit consists of various filter components (CX, LF) for high frequency suppression, a full bridge rectifier (BD) and a DC link capacitor (\(\text{C}_{\text{LINK1}}\)). The DC driving circuit includes another DC link capacitor (\(\text{C}_{\text{LINK2}}\)), a power switch for energy transfer (S), an inductor (L), a diode (D) and an output capacitor (\(\text{C}_{\text{O}}\)) [5; 20]. The full bridge rectifier generates a positive full wave sinusoidal voltage from the AC line and supplies it to the DC link capacitor in the AC driving circuit. The inductor is magnetized and stores rectified input energy from the AC driving circuit when the switch S turns on.  
During the turn-off time, the inductor is demagnetized, and stored energy is transferred to the output capacitor. In the single-stage driving method, the input voltage can be defined as:  
\[v_{IN}(t)=V_{in}\cdot sin(2\pi f_{\text{L}}t) \tag{2}\]  
Figure 2: (**a**) Overall stages of the proposed LED converter; (**b**) simplified circuit diagram of AC and DC driving circuit.  
where \(V_{in}\) is the amplitude of the input voltage and \(f_{L}\) is the line frequency of the input voltage. Since the controller is operated for power factor correction, the input current waveform follows the input voltage waveform. Therefore, the input current is defined as:  
\[i_{IN}(t)=I_{in}\cdot sin(2\pi f_{L}t) \tag{3}\]  
where \(I_{in}\) is the peak value of the input current. From Equations (1) and (2), the instantaneous output power can be expressed as [5; 21]:  
\[p_{OUT}(t)=\eta_{eff}\cdot p_{IN}(t)=\eta_{eff}\cdot V_{in}\cdot I_{in}\cdot sin ^{2}(2\pi f_{L}t)=P_{o}\cdot[1-cos(4\pi f_{L}t)] \tag{4}\]  
where \(\eta_{eff}\) is the expected power efficiency and \(\eta_{eff}\cdot V_{in}\cdot I_{in}/2\) is substituted with the amplitude of the output power \(P_{o}\). When \(V_{o}\) is the output voltage, the instantaneous output current can be expressed as:  
\[i_{OUT}(t)=I_{o}\cdot[1-cos(4\pi f_{L}t)] \tag{5}\]  
where \(I_{o}\) is the amplitude of the output current. The equation (5) shows that the single-stage power factor correction driving method always contains double line frequency output ripple. If the output capacitance does not sufficiently smooth the output ripple, the high and low amplitude current flowing through the LEDs generates light flicker [5; 21; 22; 23].
# Airborne GNSS Reflectometry for Water Body Detection
## 7 Discussion  
The accuracy of water body edge localization by the proposed radar technique is affected by the inaccuracies that Google Earth encounters while processing its map imagery. One of the main perspectives of this work is to compare the results obtained for water body detection and edge localization using the proposed radar technique with the ground-truth data. This requires extensive in situ measurement campaigns with the use of precise GNSS-R positioning techniques to accurately localize the starting and ending edges of the in-land water body surfaces. In order to better analyze the variations in the reflectivity of a water body surface, we aim to make use of real-time images captured by action cameras embedded on the bottom of the gyrocopter as part of the new GNSS-R receiver setup. This would also allow us to analyze the reason of possible misses in the detection of water body surfaces, as well as to track the number of false detections by the proposed radar technique as it is practically impossible using maps.  
We have also seen from the analysis of the GNSS reflectivity measurements that it is exceptionally complicated to differentiate other surfaces in landforms such as plain land surfaces and groves. One of the interesting aspects of the mono channel bit grabber used in this work is the digitization of L1 and L5 signals. The next step in this direction would be the processing of L5 signals and the fusion of L1 and L5 data to further increase the observables of the reflected GNSS signals. An information fusion algorithm based on statistical approaches would be developed for the integration of the GPS L1 and L5 signals. The GPS L5 signals have higher transmitted power than L1 signals and they are expected to have deeper penetration capacity in land for more accurate reflectivity measurements, allowing more precise detection of other surfaces in landforms (other than in-land water bodies). In addition, for more accurate reflectivity measurements, the gain patterns of the antennas should be taken into account.
# Cellular Effects of Silver Nanoparticle Suspensions on Lung Epithelial Cells and Macrophages
## 3 Results
### Regulation of IL-8 Expression by AgNP Suspension and AgNO\({}_{3}\) Solution  
IL-8 secretion was enhanced by exposure to AgNP suspensions (Figure 4). In A549 cells exposed to AgNP-A and AgNP-B (10 \(\mu\)g/mL), IL-8 expression was 2.7- and 2.1-times higher than that in untreated cells, respectively. IL-8 secretion was markedly enhanced by exposure to AgNPs in dTHP-1 cells. IL-8 levels in the culture supernatant of dTHP-1 cells exposed to 10 \(\mu\)g/mL AgNP-A and AgNP-B suspensions were 14- and 2.8-times higher than those in untreated cells, respectively. Moreover, the gene expression of IL-8 was enhanced by exposure to 10 \(\mu\)g/mL AgNP-A and AgNP-B suspensions. The IL-8 gene expression levels were 3.0- and 5.3-times higher in AgNP-A- and AgNP-B-treated A549 cells, respectively. Furthermore, the IL-8 gene expression in AgNP-A- and AgNP-B-treated dTHP-1 cells was 25.8- and 9.7-times higher, respectively. IL-8 secretion was slightly increased by exposure to 10 \(\mu\)M AgNO\({}_{3}\). Exposure to 10 \(\mu\)mol/L AgNO\({}_{3}\) did not affect IL-8 gene expression levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Acidic Condition 1** & **DMEM** \\  & & **with 10\% FBS** \\ \hline \hline \end{tabular}
\begin{tabular}{c} Rereased Ag2\({}^{+}\) \\ (\(\mu\)mol/L) \\ \end{tabular} & AgNP-A & 22.2 & 5.6 \\  & AgNP-B & 7.4 & 4.6 \\ \hline \hline \end{tabular}  
Ag nanoparticle suspension at concentration of 10 \(\mu\)g/mL incubated for 24 h at 37 \({}^{\circ}\)C. 1 20 mM citrate buffer, pH 4.5.  
\end{table}
Table 2: Released silver ion concentration from silver nanoparticles.  
Figure 3: Effects of AgNPs (**a**) and AgNO\({}_{3}\) (**b**) on mitochondrial activity. A549 and dTHP-1 cells were treated with AgNP suspensions at 1 and 10 \(\mu\)g/mL, or with AgNO\({}_{3}\) solution at 1, 10, and 100 \(\mu\)mol/L. After incubation for 24 h, the WST-1 assay was conducted. The percentage of WST-1 was normalized to that in standardized untreated cells (100%). **_p_ < 0.01, *_p_ < 0.05 (vs. untreated cells, ANOVA and Dunnett’s test).