esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 2 Materials and Methods
### Permanent Displacement Detection and Estimation  
This study uses the permanent displacement calculation formula proposed in [5] to detect and estimate the permanent displacement that occurs at certain observation points. This formula uses a characteristic function, \(D\), based on the parameters of the short-term average and long-term average as follows [5]:  
\[D=|STA(t)-LTA(t)|-SD(LTA(t)), \tag{9}\]  
where \(STA(t)\) and \(LTA(t)\) are the short-term and long-term averages of a time series in a certain \(t\), expressed as follows [5]:  
\[STA(t)=\frac{\sum_{i=t-\alpha+1}^{t}(p_{i}\cdot x_{i})}{\sum_{i=t-\alpha+1}^{ t}p_{i}},\ LTA(t)=\frac{\sum_{i=t-\beta+1}^{t}(p_{i}\cdot x_{i})}{\sum_{i=t- \beta+1}^{t}p_{i}}, \tag{10}\]  
where \(x_{i}\) is the norm of the horizontal components at \(t=i\), \(\alpha\) and \(\beta\) are the suitable time window lengths based on the noise properties of the real-time kinematic GPS time series (this study assumes \(\alpha=60\)s and \(\beta=600\)s), and \(p_{i}\) is the weighting parameter [5].  
For the standard deviation, the generic formula is as follows:  
\[SD(x)=\sqrt{\frac{1}{N-1}\cdot\sum_{i=1}^{N}(x_{i}-\overline{x})^{2}}, \tag{11}\]where \(\{x_{1},x_{2},\ldots,x_{i}\}\) are the observed values of the sample items, \(\overline{x}\) is the mean of the data observed, and \(N\) is number of data points observed.  
\(D\) is a key value for detecting permanent displacement. Permanent displacement is said to occur and be detected when the value of \(D\) passes a certain threshold value. The threshold used is expressed in \(K\), which is defined as follows [5]:  
\[K=4\cdot SD(D), \tag{12}\]  
where \(SD(D)\) is the standard deviation of \(D\).  
To detect permanent displacement, the formula uses only the norm of the horizontal components to minimize the chance of false detection. The horizontal component has a higher signal-to-noise ratio than the vertical data [5]. However, to estimate the displacement value, the vertical components are also used in the estimation formula.  
The algorithm used to detect and estimate permanent displacement is shown in Figure 3. The estimation process begins when the system detects the occurrence of permanent displacement. The process is completed when no more movement is detected at the observation point and \(D\) has reached its maximum value. The permanent displacement value is calculated as the difference between the observation point coordinates before and after the earthquake event occurs [5].  
Figure 3: Flowchart of the algorithm: (**a**) permanent displacement detection; (**b**) permanent displacement estimation.
## 3 Upper-Level Multiple Resources Coordinated Optimization Modeling and Solving
### Mathematical Model for Multiple Resources Coordinated Optimization
#### 3.2.3 The Grid Security and Stability Constraints  
The grid security and stability constraints include the transient frequency nadir, the quasi-steady-state frequency constraints, rotor angle stability constraints, bus voltage constraints, and the power flow constraints of all transmission lines. The grid security and stability constraints can be expressed as  
\[f_{\mathrm{dn}}\geq f_{\mathrm{dn,min}} \tag{7}\]  
\[f_{\mathrm{ss}}\geq f_{\mathrm{ss,min}} \tag{8}\]  
\[\Delta\delta_{0,w}\leq\Delta\delta_{\max},o,w=1,\cdots,N_{\mathrm{gen}} \tag{9}\]  
Figure 6: Cost factor curve of VPP emergency power support.  
\[V_{s}^{\min}\leq V_{s}\leq V_{s}^{\max},s=1,\cdots,N_{\text{bus}} \tag{10}\]  
\[S_{\text{sp},l}\leq S_{\text{sp},l}^{\max},l=1,\cdots,N_{\text{line}} \tag{11}\]  
where \(f_{\text{dn}}\), \(f_{\text{dn,min}}\) are the transient frequency nadir and its security threshold, respectively. \(f_{\text{ss}}\), \(f_{\text{ss,min}}\) are the quasi-steady-state frequency and its security threshold, respectively. \(\Delta\delta_{o,w}\) is the rotor angle between the \(o\)th generator and the \(w\)th generator, and \(\Delta\delta_{\max}\) is the maximum allowable rotor angle difference between any two generators. \(V_{s}\) is the \(s\)th bus voltage, and \(V_{s}^{\min}\), \(V_{s}^{\max}\) are the lower and upper allowable limits of the \(s\)th bus voltage, respectively. \(S_{\text{sp},l}\), \(S_{\text{sp},l}^{\max}\) are the \(l\)th line overload rate and its threshold value, respectively.
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
## 3 Results
### Analytical Results
#### 3.3.2 Roughness: Raking Light Photography  
Surface roughness evaluations were based on naked-eye observations and raking light photography, comparing surface roughness before and after laser cleaning. Caps of the spray cans often gave remarkably uneven paint films, characterized by small bubbles or local agglomerations. The roughness of the original surface is primarily influenced by the evenness of the paint film. Therefore, we assessed this parameter within the same sample instead of making comparisons among different samples.  
Table 8 displays grazing light photos of sample Acry-AcrB and their evaluation scores. It is evident that under both unaged and aged conditions, the surface treated with the LQS laser instrument appeared more engraved compared to the surface treated with the QS laser. Consequently, higher roughness indicates potential damage to the bottom layer (in  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Condition** & **Unaged** & \multicolumn{6}{c}{**Aged**} \\ \hline
**Laser** & **QS \(\Delta\)E** & **QS \(\sigma\Delta\)E** & **LQS \(\Delta\)E** & **LQS \(\sigma\Delta\)E** & **QS \(\sigma\Delta\)E** & **QS \(\sigma\Delta\)E** & **LQS \(\sigma\Delta\)E** \\ \hline StyY–AcrB & 34.85 & 3.09 & 50.90 & 6.56 & 25.17 & 0.74 & 21.19 & 0.97 \\ StyY–AcrFB & 26.39 & 1.33 & 32.10 & 7.67 & 22.02 & 0.82 & 19.80 & 1.07 \\ StyY–AlkB & 29.40 & 0.93 & 52.27 & 2.38 & 24.00 & 1.01 & 17.68 & 0.78 \\ AlkY–AlkB & 13.03 & 0.82 & 11.13 & 0.99 & 12.93 & 0.86 & 10.49 & 1.25 \\ AlkY–AcrFB & 16.48 & 0.62 & 14.09 & 1.84 & 13.73 & 0.66 & 9.24 & 0.48 \\ AlkY–AcrFB & 19.52 & 1.50 & 15.40 & 1.52 & 13.51 & 0.72 & 10.20 & 0.28 \\ Acry–AlkB & 10.60 & 2.06 & 7.29 & 1.92 & 11.83 & 2.19 & 9.95 & 2.17 \\ Acry–AcrB & 15.52 & 0.94 & 15.77 & 1.90 & 24.64 & 1.53 & 12.77 & 1.05 \\ AcrY–AcrFB & 14.82 & 1.77 & 9.71 & 0.53 & 11.67 & 0.88 & 11.88 & 1.13 \\ AcrB–AlkY & 4.48 & 0.46 & 8.76 & 0.50 & 5.07 & 0.57 & 6.93 & 1.45 \\ AcrB–AcrY & 5.19 & 0.89 & 8.25 & 0.68 & 6.17 & 0.37 & 7.12 & 2.70 \\ AlkB–AlkY & 4.62 & 2.38 & 6.34 & 1.86 & 4.13 & 3.25 & 18.47 & 1.06 \\ AlkB–AcrY & 2.97 & 0.53 & 3.27 & 0.63 & 3.73 & 0.72 & 3.22 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average color change (\(\Delta\)E) and standard deviation (\(\sigma\Delta\)E) of all samples after laser cleaning.  
Figure 3: Color change (\(\Delta\)E) of all samples after laser cleaning.  
the case of unaged LQS) or poor cleaning with a large amount of top layer residue (in the case of aged LQS).
## 3 Methodology and Data
### Research Design--ITOE Model and Hypotheses  
As previous section stated, this study proposes an integral ITOE research model. Further, [PERSON] et al. examined the literature of intention models and indicated that individual behavioral intention will significantly influence the actual use-behavior of infor  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **Support** & **Degree of Support** & **Evidence in SMEs** \\ \hline Relative Advantage & Supported & High & P1-3, A1-3, I1-3, N2-3 \\ Compatibility & Supported & Middle & P1-3, A1, I1 \\ Complexity & Not supported & Low & N2 \\ Trialability & Supported & High & P1-3, A2, I2-3, \\ Observability & Supported & High & P1-3, A2, II-3, N3 \\ Top Management Support & Supported & High & P1, P3, A1-2, I2, N1-3 \\ Employees’ Knowledge & Supported & High & P1, A2-3, I1-2, N1-2 \\ Absorptive Capability & Supported & Middle & II-2, N2-3 \\ Competitive Pressure & Supported & Middle & A2-3, II, I3, N1 \\ Business Partner & Supported & High & P1-3, A1-3, I1-2 \\ External Support & Supported & High & P1-3, A1-2, I2-3, N2 \\ Government’s Support & Supported & High & P1-3, A2, I2-3 \\ Performance Expectancy & Supported & High & P1-3, A1-3, I1-3, N1 \\ Effort Expectancy & Supported & High & P1-3, A2, II, N1-3 \\ User Habit & Supported & High & P1-3, A1-2, II-2, N1 \\ Data Security & Supported & High & P3, A1-3, II-3, N2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of qualitative interviews’ findings.  
mation technology [41]. [PERSON] et al. suggested that internal users' positive or negative attitudes may strongly affect ICT acceptance decisions in enterprises [46]. [PERSON] also argued that employees' attitudes are critical for an organization attempting to adopt emerging technologies [47]. Consequently, based on the results of the first phase, and in reference to the TOE framework and UTAUT by [PERSON] et al., this study developed the following hypotheses, listed in Table 2. It empirically tested the findings by Structural Equation Modeling (SEM). The research model is illustrated in Figure 2.
# Combined Effect of Ferrous Ion and Biochar on Cadmium and Arsenic Accumulation in Rice
## 4 Discussion
The application of Fe(II) showed significant reduction of the As content in rice root, indicating that Fe(II) application brings about the decrease mobility of As by raising the level of free Fe oxide in the soil. The high affinity of root iron plaques to arsenate and altered the arsenate uptake of rice seedlings [53; 54]. It can be presumed that the root iron plaque has the ability to obstruct As uptake, thus reduces the migration of As from soil to rice plant. Iron-bearing mineral and iron plaque play a key role in immobilization of As contaminated soil. Cd has a higher TFs value than As (Tables 3 and 4), indicating that the translocation of As from root to straw and from straw to rice is more difficult than that of Cd. The study has shown that aquaporin channels can take As(III) and Cd [55]. In addition, Cd can also enter the rice by an absorption channel of mineral elements [56], transported from roots to the stem through the phloem, then directionally distributed through the microtubules. Therefore, the differences between metal contents and distribution in rice tissues may be the reason for different transport mechanisms when the rice plant uptakes the Cd and As (V). As (V) is taken up via phosphate transporters of plants [57] because both arsenate and phosphate have a tetrahedral structure [58; 59]. [PERSON] reported that the application of Fe materials had a significant decreasing effect on the contents and proportions of inorganic-As (iAs) in rice grain, indicating that, compared to the translocation of organic As from shoots/roots to grains [60], the same process of iAs is much more difficult. This made the decrease in the proportion of iAs in the rice grain. The TF of As depends not only on total As, but its morphology (III, V) is also an important factor. Therefore, the treatments with addition of BC, BC+Fe(II) made a difference in the soil characteristics like pH, Fe fractions, As species, and Cd migration, resulting in different Cd and As uptake from soil to rice plants.
## 3 Experimental Results and Analysis
### Experimental Results  
To evaluate the performance of the proposed method in object detection, the model was trained on the constructed dataset using the Adam optimizer with iterative training. The initial learning rate was \(5\times 10^{-4}\), and the learning rate was adjusted by cosine annealing during training. The weights of the CenterNet-ResNet50 network pre-trained on Imagenet were used to initialize the network parameters, and the entire network was trained for 200 epochs with transfer learning. The loss curves are shown in Figure 7. It can be seen that the loss value decreased rapidly in the first ten cycles and then became basically stable after 150 cycles of training, indicating that the model gradually learned the object detection features.  
In the experiments of this paper, the test results of each class were evaluated, and the accuracy and average test time were calculated. According to the results shown in Table 3, this research model demonstrates good performance in the detection of different object categories. Taking maize borer infestation as an example, the accuracy of the model reached 93.98%, which proves that the model can accurately identify maize borer infestation. For northern leaf blight (NLB) disease, a detection accuracy of 86.60% was obtained, indicating that the model is highly reliable in detecting this type of disease.  
Figure 7: Model training loss curve.  
Moreover, in terms of average detection time, the model in this paper takes only 0.025 s, indicating the fast processing capability of the model while efficiently detecting the object. Therefore, the model is able to detect both diseases and pests with good results in terms of speed and accuracy.
## 2 Experimental Procedure
### Experimental Procedures  
Table 3 shows the experimental plan of the study. The binder types corresponded to OPC and GGBS, and tests were conducted by increasing the GGBS replacement ratio by 10% from 0% to 70% while the unit binder content and curing temperature were fixed at 330 kg/m\({}^{3}\) and 20 \({}^{\circ}\)C, respectively. In series I, the particle size distribution, SEM, X-ray fluorescence, X-ray diffraction, and grading distribution were measured for raw material analysis. In series II, the slump, air content, water content, and compressive strength were measured for engineering performance analysis. In series III, the carbonation depth, chloride penetration depth, relative dynamic elastic modulus, and weight reducing ratio were evaluated for durability performance analysis. Table 4 shows the mixing proportions of the concrete (ready-mixed concrete specification 25-24-180) used in the study.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Material**} & \multicolumn{8}{c}{**Chemical Composition (\%)**} \\ \cline{2-9}  & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **SO\({}_{3}\)** \\ \hline OPC (\({}^{1}\)) & 60.20 & 21.60 & 5.15 & 3.30 & 2.30 & 0.99 & 0.53 & 1.50 \\ GGBS (\({}^{2}\)) & 44.90 & 35.40 & 13.00 & 0.47 & 5.01 & 0.37 & 0.00 & 1.31 \\ \hline \end{tabular} \({}^{(1)}\) OPC: ordinary Portland cement; \({}^{(2)}\) GGBS: ground granulated blast-furnace slag.  
\end{table}
Table 1: Chemical compositions of the used binders.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Material**} & \multicolumn{2}{c}{**Property**} \\ \hline \multirow{5}{*}{GGBS} & \multicolumn{2}{c}{Type 1 ordinary Portland cement (KS L 5201)} \\  & OPC &
\begin{tabular}{c} Density: 3.15 g/cm\({}^{3}\), specific surface area: 3322 cm\({}^{2}\)/g \\ Type 3 Ground Granulated Blast-furnace slag (KS F 2563) \\ Density: 2.90 g/cm\({}^{3}\), specific surface area: 4592 cm\({}^{2}\)/g \\ \end{tabular} \\ \hline \multirow{2}{*}{Fine aggregate} & S1 & \multirow{2}{*}{FM (\({}^{1}\)): 2.01, density: 2.60 g/cm\({}^{3}\), absorption: 0.79\%} \\  & S2 & & \multicolumn{2}{c}{FM: 3.29, density: 2.57 g/cm\({}^{3}\), absorption: 0.87\%} \\ \hline \multirow{2}{*}{Coarse aggregate} & \multicolumn{2}{c}{Crushed granitic aggregate} \\  & & Size: 25 mm, density: 2.60 g/cm\({}^{3}\), absorption: 0.76\% \\ Chemical admixture & \multicolumn{2}{c}{Polycarboxylic acid group, density: 1.05 g/cm\({}^{3}\)} \\ \hline \end{tabular} \({}^{(1)}\) FM: fineness modulus.  
\end{table}
Table 2: Physical properties of the materials.
## 1 Introduction
The American Brain Trauma Foundation (BTF) guidelines set indications for ICP monitoring in patients with severe TBI with a normal CT scan. ICP monitoring is indicated if two or more of the following features are noted on admission: (i) age over 40 years; (ii) unilateral or bilateral motor posturing; (iii) systolic blood pressure \(<\)90 mmHg. The BTF guidelines recommend treating ICP above 22 mmHg [14]. Elevated levels of ICP are referred to as intracranial hypertension (ICH). The gold standard ICP monitoring modality is intraventricular pressure monitoring using an extraventricular drain (EVD) [15; 16]. In patients with raised ICP, the desired outcome of ICP management is to (i) maintain ICP less than 22 mmHg, as per the guidelines set out by the BTF [14], and (ii) maintain adequate cerebral perfusion pressure (CPP), which, in healthy adults, is between 50 and 150 mmHg [17]. The ability to predict the onset of ICP crises levels in neurocritical care is valuable since it could enable early intervention and treatment, allowing healthcare providers to take proactive measures to prevent or minimise secondary injury to the brain and/or death caused by elevated ICP. The capacity to predict ICP crises could also help in resource optimisation, by allocating healthcare resources to at-risk patients. Ultimately, the capacity to predict ICP crises could improve patient outcomes, optimise resource allocation and enhance overall patient care in neurocritical settings. The three main objectives of this review are (i) to identify the most efficacious machine learning approaches to the prediction of ICP crises within TBI patients, compared to invasive ICP measurements, (ii) to access the clinical suitability of existing predictive models and (iii) to suggest potential areas for future research.
## 3 Experimental Results and Analysis
### Ablation Experiment  
To evaluate the impact of each proposed improvement module on overall performance, systematic ablation experiments were conducted on a self-constructed dataset featuring occlusion and stacking scenarios. The baseline was established using the basic version of YOLOv8-URE basic version without any improvement modules (denoted Base). Building upon this baseline, the modules were introduced sequentially: C2f_UniRepLKNetBlock (Base-1), ELA mechanism (Base-2), CSPStage-Dy enhanced neck (Base-3), and ShapeIoU loss (Base-4). Furthermore, additional configurations--Base-5, Base-6, and Base-7--were constructed by progressively combining ELA, the improved neck, and ShapeIoU loss on top of Base-1.  
The experimental results, as shown in Table 5, indicate that the standalone introduction of the UniRepLKNetBlock significantly reduces the model parameters (by approximately 34.3%) and computational complexity (FLOPs decreased by 2.3%), albeit at the cost of some accuracy loss. After incorporating the ELA mechanism, both recall and [EMAIL_ADDRESS]:0.95 show marked improvements, demonstrating its strong capability to enhance key regions inshallow features. The improved neck structure (including DySample) significantly boosts the detection accuracy of primary targets ([EMAIL_ADDRESS] increased to 97.3%), highlighting its advantages in multi-scale fusion and feature consistency.  
Notably, when both ELA and the CSPStage-Dy enhanced neck are introduced simultaneously in Base-6, the mAP improvement is no longer dramatically higher compared to individual modules. However, this combination maintains a lightweight architecture while achieving a more balanced and stable performance in recall and overall metrics. The underlying mechanism is that ELA strengthens the front-end network's perception of critical target regions, making target contour features more prominent, whereas DySample enhances spatial consistency and contextual modeling in the neck's multi-scale fusion. Acting at different network stages, these two modules complement each other under a decoupled structure, improving overall perception and representation capabilities, thereby exhibiting stronger robustness and detection stability in complex occlusion and dense target environments.  
Finally, with the introduction of ShapeIoU loss (Base-7), the model's convergence speed further accelerates, bounding box regression becomes more precise, and detection accuracy reaches its highest ([EMAIL_ADDRESS] of 98.3%). This validates the complementary value and optimization potential of each module within the overall architecture.
# Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art
## 3 Results and Discussion
### Public Opinion Analysis Results
In cluster 3, the copyright problem is discussed from the perspective of Weibo users supporting human artists (also known as \"teachers\"), which reflects the absence of relevant laws, regulations, and oversight about AI Art applications and their products. The fast pace of AI Art also raises concerns among human artists, as the advancement of technology may replace humans in various industries, which might result in their being laid off (depicted in clusters 4 and 5). In cluster 6, the users expressed excessive negative emotions, resulting from the concerns in clusters 1 to 5, which may be harmful to maintaining a harmonious community environment in Weibo. The clustering results of positive posts indicate that the advancement of AI Art technology allows the public to engage in artistic creation using AI, thus reducing the barriers to entry for participation in art creation. Furthermore, AI Art technology has garnered investments from companies like Tencent, offering assistance in analyzing market trends and drawing increased interest towards AI Art technology. To conclude, the results are beneficial for the advancement of AI Art technology. On the other hand, the results of clustering analysis on negative posts highlight the limitations of AI Art technology in its current stage. The rapid development of AI Art has sparked apprehensions regarding the future. Furthermore, being an emerging technology, the copyright and intellectual property concerns associated with AI-generated artwork have incited controversy, resulting in the proliferation of highly critical comments on social media platforms. Hence, the advancement of AI Art technology necessitates the dissemination of detailed information about relevant technologies to enhance the public comprehension of it. Additionally, it calls for authorities to enact legislation pertaining to copyright to mitigate the potential misuse of AI Art technology. In light of the polarization of public opinion resulting from the inappropriate use, it is imperative for the authorities not only to penalize those who misuse the technology but also to enhance the oversight of such extreme posts.
## 4 Fabrication  
Based on the design described in Section 3, a prototype of the proposed AOC was fabricated according to the fabrication procedure, shown in Figure 5a-d, simple assembly technique: First, as shown in Figure 5a, optical devices and ICs were mounted on the backside of the substrate by die and wire bonding. The other ICs (micro control unit (MCU) and EEPROM) were mounted on the topside of the substrate. Second, the optical connector and receptacle was aligned above the optical devices by active alignment method. The optical connector consists of 12-ch optical lenses with a pitch of 250 \(\upmu\)m and a 12-ch MMF with a 1-mm-thick mechanical transfer (MT) ferrule, and the 4-ch VCSEL and 4-ch PD are aligned with the lenses, simultaneously. Next, the optical connector was fixed onto the substrate by ultraviolet light cure adhesion, and the used master plug connector was removed from the receptacle. Finally, as shown in Figure 5d, they were attached to the case.  
Figure 6 shows a photograph of the fabricated AOC. A GaAs- VCSEL and PD, as well as a SiGe-laser diode driver (LDD) and TIA with a clock and data recovery (CDR) circuit are adopted as optical devices and ICs, respectively. Total power consumption of the AOC is about 1.6 W under 4-ch operation with CDR. The breakdown of the AOC's power consumption is about 20 mW for the VCSEL, 0.7 W for  
Figure 4: (**a**) Simulation model for crosstalk analysis and (**b**) Calculation result of crosstalk jitter. PRBS, pseudo-random binary sequence.  
Figure 5: Fabrication procedure of proposed AOC. MCU, micro control unit; LDD, laser diode driver.  
the LDD about 0.8 W for the TIA and 80 mW for the control IC (MCU and EEPROM). The MMF used is an OM3 graded index (GI)-50 \(\upmu\)m.