esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Optimization of Thermomechanical Effect
The response surface for both skin temperature T\({}_{\rm max}\) (Figure 10a) and stress state S\({}_{\rm max}\) Figure 10b can be presented in terms of beam intensity (I) and exposure time (T). The value of both T\({}_{\rm max}\) and S\({}_{\rm max}\) can also be extrapolated as a function of beam intensity (I) and exposure time (T) in Figure 11c,d, respectively. Figure 11c,d show the possibility of obtaining minimum values for the skin temperature T\({}_{\rm max}\) and its stress state S\({}_{\rm max}\) at low values of beam intensity (I) and exposure time (T). Equations (11)-(14) are the regression equations as a function of the processing parameters (I, S, T) for predicting the value (S\({}_{\rm max}\))\({}_{532}\), (S\({}_{\rm max}\))\({}_{800}\), (T\({}_{\rm max}\))\({}_{532}\), and (T\({}_{\rm max}\))\({}_{532}\), respectively. \[\rm(S_{\rm max})_{532}=0.409-0.059\;I-0.006\;S+0.00145\;T+0.0003\;I^{2}+0.002\;S ^{2}-0.000006\;T^{2}+0.0000\;I\cdot S+0.000813\;I\cdot T \tag{11}\]  
\[\rm(S_{\rm max})_{800}=-1.191+0.581\;I-0.006\;S+0.00622\;T+0.0003\;I^{2}+0.002 \;S^{2}-0.000006\;T^{2}+0.0000\;I\cdot S+0.000813\;I\cdot T \tag{12}\]  
\[\rm(T_{\rm max})_{532}=28.93+3.28\;I-0.18\;S+0.0760\;T+0.023\;I^{2}+0.051\;S^{2 }-0.000124\;T^{2}+0.000\;I\cdot S+0.01662\;I\cdot T \tag{13}\]  
\[\rm(T_{\rm max})_{800}=29.03+3.24\;I-0.18\;S+0.0757\;T+0.023\;I^{2}+0.051\;S^{2 }-0.000124\;T^{2}+0.000\;I\cdot S+0.01662\;I\cdot T \tag{14}\]  
The determination factors are (R-sq)\({}_{\rm max}\) = 93.48% and (R-sq)\({}_{\rm max}\) = 97.89%. By applying the response optimization feature in the Box-Behnken approach, it is possible to query the levels of parameters corresponding to a specific goal after analyzing and comparing the responses corresponding to the distribution of levels in the experiment array. In this study, the appropriate goal is to determine the minimum limits for each of the two responses: skin temperature (T\({}_{\rm max}\))\({}_{\rm min}\) and stress (S\({}_{\rm max}\))\({}_{\rm min}\). The results of the analysis indicated that the minimum values for both T\({}_{\rm max}\) and S\({}_{\rm max}\) can be obtained according to the following levels: (I = 1, S = 1.8, T = 30, W = 800). These levels were tested in the simulation model (FEA), and the response results were: (T\({}_{\rm max}\))\({}_{\rm min}\) = 38.29 [\({}^{\circ}\)C], (S\({}_{\rm max}\))\({}_{\rm min}\)= 0.174 [MPa]. Figure 10: Main effects of parameter levels on: (**a**): skin temperature Tmax, and (**b**) stress S\({}_{\rm max}\).
## 3 Research Design
### Forecasting Project Performance Based on RCF and RBF
#### 3.2.2 Determine Input and Output Variables  
Input Variable  
Project performance is generally forecasted by Eamed Value Management (EVM) methods, which can be used to comprehensively examine project schedule and cost performance.  
In this paper, we focus on schedule-related indicators, including Planned Value (PV), Earned Value (EV), Schedule Variation (SV), and Schedule Performance Indicator (SPI), all of which can reflect the difference between the actual schedule and the planned schedule, but SPI is more conducive to the training of the neural network model compared with SV, and is therefore chosen as an input variable. Variables that are closely related to project performance also include the project's planned duration (PD) and budget at completion (BAC).  
Since this paper investigates the impact of project resilience on the disruption and recovery of project performance metrics after the disruption, the input variables also need to consider the level of disruption encountered by the project. In this paper, the risk level (RL) of disruption is scored based on the impact on project objectives, schedule, cost, or quality caused by the occurrence of the risk event. Risk events with a minor impact on project objectives, schedule, cost, or quality can be resolved through the efforts of the project team or simple adjustments, with scores ranging from 1 to 3. Risk events that have a moderate impact on project objectives, schedule, cost, or quality require specific countermeasures, with a score range of 4-7. A risk event can have a significant impact on project objectives, schedule, cost, or quality and may require urgent assessment or re-planning of the project program to address it. The severity of the risk is rated on a scale of 8-10. Finally, the data are processed, with a score range of 0-1. Standardized score values help to harmonize metrics. In risk management, different risk events may have different levels of impact and probability of occurrence, and these values may span a wide range. By standardizing the score values to a range of 0-1, it is possible to use the same metrics for all risk events, thus facilitating subsequent calculations. Meanwhile, in this paper, maximum-minimum normalization is used to linearly transform the original values to the range of 0-1. (maximum-minimum normalization: X_norm = (X - X_min)/ (X_max - X_min), X_norm is the normalized value, and X, X_min, and X_max are the original values, the minimum value in the original data, and the maximum value in the original data, respectively).  
Therefore, SPI, PD, BAC and RL are selected as input indicators for the model, which are calculated as shown in Table 2.
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 4 Development of the Response Surface Model
### Principle and Modeling of Response Surface Methodology  
Response surface methodology (RSM) is an optimization technique combining mathematical and statistical methods, widely applied in studies exploring the relationship between multiple input variables (factors) and one or more output variables (responses). Its core idea is to obtain data through systematic design of experiments (DoE), construct a mathematical model (typically a polynomial model) to approximate the true response surface within the factor space, and subsequently analyze the influence of each factor and their interactions on the response, ultimately finding the optimal combination of factor levels or understanding the key influencing factors of the process [16].  
Considering the multi-parameter interactive effects within the multi-physical fields of the ship's central cooling system and simultaneously balancing fitting accuracy and computational efficiency, this paper selected a quadratic polynomial model to construct the energy consumption response surface. This model can effectively capture the surface curvature characteristics and the interaction effects between factors while achieving a balance between fitting accuracy and computational efficiency. Its general mathematical expression is [20]:  
\[y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\sum_{i=1}^{k}\beta_{ii}x_{i}^{2}+ \sum\sum_{i<j}^{k}\beta_{ij}x_{i}x_{j}+\varepsilon \tag{8}\]  
where \(y\) is the response value, \(\beta_{0}\) is the constant term, \(\beta_{i}\) and \(\beta_{ij}\) are the regression coefficients, \(x_{i}\) and \(x_{j}\) are the factor levels, and \(\varepsilon\) is the error term; \(k\) is the number of factors, which is 5 in this paper.
## 6 Experimental Results and Discussion
### Analysis of Tampering Detection  
In order to measure the performance of the proposed method in change detection, different modifications were applied to the test watermarked images. Afterward, the authentication process was executed using the six authentication watermarks and the hierarchical authentication method to discard most of the detection errors, specifically the false negatives.  
The tampering detection process evaluation consists of two alteration schemes: the first one was used to estimate the ability to detect alteration rates between 10% and 90% by adding a regular square area of pseudo-random noise into the watermarked image, as displayed in Figure 4. In the second scheme, the alterations were performed by modifying one or multiple irregular areas in the watermarked image using Adobe Photoshop software, maintaining the structure and original nature of the image and avoiding significant falsity in the alteration. Figure 5 illustrates the watermarked images with irregular alterations of Kodak-1 in 46.41%, Kodak-3 in 34.34%, Kodak-11 in 25.06% and Kodak-14 in 40.08% of the entire image.  
The detection's _Precision_ results for the changes using the first scheme are shown in Table 5. It can be noticed that the _Precision_ performance is enhanced as the alteration rate is increased. The Recall metric resulted in values of 1.0 for all the alteration rates between 10% and 80% and 0.9999 for a change of 90% of the image. This is due to the hierarchical method of authentication.  
The evaluation of the second scheme, which uses multiple and irregular alterations in the images, is displayed in Figure 6, where images in Figure 6a-d represent the ground truth of the alterations, and images in Figure 6e-h are the results of the change detection obtained by SR-HTR. The pairs of (_Precision_, _Recall_) values are: (0.9190, 0.9995) for Kodak-1, (0.9416, 1.0) for Kodak-3, (0.9214, 1.0) for Kodak-11 and (0.9382, 0.9998) for Kodak-14.  
Figure 5: Multiple and irregular alterations, (**a**) Kodak-1 original, (**b**) Kodak-3 original, (**c**) Kodak-11 original, (**d**) Kodak-14 original, (**e**) Kodak-1 altered, (**f**) Kodak-3 altered, (**g**) Kodak-11 altered, (**h**) Kodak-14 altered.
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## Appendix A  
### Single-Channel MARA with Spline Correction (SC-MARA-Spline)  
SC-MARA (Movement Artifact Reduction Algorithm) with spline correction is the original algorithm version that performs MA reduction by separating identification and correction phases [21]. MA detection is based on the computation of the moving standard deviation of \(\Delta OD(t,\lambda)\) signals according to an odd sliding window of \(w=2k+1\) samples large enough to contain MA features:  
\[mSTD(t)=\frac{1}{2k+1}\sqrt{\sum_{j=-k}^{k}\Delta OD(t+j,\lambda)-\frac{1}{2k+1} \left(\sum_{j=-k}^{k}\Delta OD(t+j,\lambda)\right)^{2}} \tag{1}\]  
In the present work, we set the moving sliding window to five samples, which almost correspond to epochs of three seconds according to our experimental set-up (details are presented in Section 2.8).  
Since MAs are mainly associated with higher signal variations compared to typical hemodynamic oscillations, the resulting \(mSTD(t)\) signal allows us to detect epochs of the signals affected by MAs. Hence, MA windows are defined by setting a user-defined threshold over the resulting \(mSTD(t)\) signal. Namely, in this work we set a threshold of 5, which indicates that a portion of the signal is labelled by MA if exceeding \(5\cdot mSTD(t)\) within the considered window length of three seconds. Additionally, other methods can be implemented to automatically define a threshold. Among them we cite the Triangle method for unimodal histograms [62]. In brief, the choice of the threshold is given by computing the maximum distance of the perpendicular segment to the line connecting the first and last bin of \(mSTD(t)\) histogram. Thus, MA are identified over time periods where \(mSTD(t)\) is above the computed threshold.  
Finally, the MA-labelled windows are corrected by subtracting the anomalous baseline fitted via spline interpolation. The spline parameter \(p\) plays the major role: \(p=1\) would spline through all samples thus cancelling all dynamics since corresponding to the natural cubic spline interpolant; \(p=0\) would implement a linear detrend since implementing least-square straight-line fitting. We followed the recommendation in the original work setting \(p=0.99\), which identifies the trend to be subtracted, even in the presence of high discontinuities, leaving the informative dynamics around it.
# Review of Weed Detection Methods Based on Computer Vision
## 3 Traditional Machine Learning Weed Detection Methods
### Multi-Feature Fusion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Advantages** & **Disadvantages** \\ \hline Texture & Has high accuracy, strong adaptability, & Grey-level co-occurrence matrix (GLCM takes a long time and does not meet the real-time processing requirements. \\ Shape & Independent of geometric translation, & Shapes are deformed by disease, insect eating, and man-made or mechanical damage and incomplete under overlap and occlusion. \\ Color & Insensitive to the adjustment of proportion, & Crops and weeds with similar color will fail; leaf lesions and plant seasonality will change color. \\ Spectral & Robust to partial occlusion & Spectral features vary in different growth stages of plants, are easily affected by the collection environment, and are unstable. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the advantages and disadvantages of four common features. should be designed manually and cannot be learned automatically, and other important features of leaves are not utilized. In conclusion, these multi-feature fusion methods can solve the problem of weed detection and improve the accuracy of experiments, but some problems have not been completely solved. For example, for many interference factors under nonideal conditions, the accuracy and stability of experiments should be further improved.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Purpose of Grounding Systems
#### 2.1.1 Protection of Personnel from Electric Shock  
The fundamental purpose of grounding is to provide a low-resistance path for fault currents to flow into the earth, thereby preventing electric shock hazards. This is particularly critical when faults such as short circuits or insulation breakdowns occur. In the absence of a good earthing system, fault currents could actually travel through a person, resulting in extreme injury or even death from electric shock. Touch voltage refers to the voltage difference between any two points that may be simultaneously contacted by a person and serves as an indicator of electric shock risk [7]. The touch voltage \(V_{touch}\) can be calculated as follows:  
\[V_{touch}=I_{fault}\times R_{ground} \tag{1}\]  
where \(I_{fault}\) is the fault current, \(R_{ground}\) is the resistance of the grounding system. To ensure personnel protection, the touch voltage must be kept below a threshold value, typically defined by safety standards like IEEE 80 or IEC 60364 [3].
## 1 Introduction
[PERSON] and [PERSON] have engineered a tuned liquid column damper (TLCD), tailored for wind and seismic applications, particularly for lightly damped asymmetric structures such as bridges and arch dams [13]. This damper is noted for its simplicity, low maintenance, and superior vibration reduction capabilities. Through computational analysis and model testing, [PERSON] implemented a series of engineering measures to enhance the seismic stability and safety of the Dagangshan Hydropower Station's ultra-high arch dam. These measures included the installation of seismic reinforcement bars oriented along the beams, inter-seam dampers, resistance anchor cables, and grouting of the weak rock masses in the dam foundation. [PERSON] further demonstrated that the placement of dampers at the transverse seams of the dam effectively restricts crack propagation [14]. [PERSON], [PERSON], and their team proposed a damper that harnesses liquefied sand for energy dissipation [15]. Employing finite-element numerical analysis, they demonstrated that installing such dampers at the crest of concrete gravity dams can result in nearly a one-third reduction in crest displacement under seismic loading. The widespread application of dampers in dam engineering would markedly increase construction costs. Hence, there is a pressing need to identify cost-effective damping materials that offer high performance without compromising on efficacy. Sand is prevalent in natural environments, and the conditions for the liquefaction of saturated sandy soil are readily achievable. Enhancement of dam seismic resistance through material reinforcement, such as the addition of steel rebar and fiber materials, is more costly compared to utilizing saturated sandy soil, which offers superior effectiveness at a lower material expense. This paper proposes a new type of hollow concrete gravity dam containing saturated sandy soil for seismic mitigation. A three-dimensional model has been constructed, employing the EOS (equation of state) for viscous fluids to simulate the liquefied saturated sandy soil. Discrete field explicit dynamic analysis is used to model the dam under dynamic loading conditions. Numerical simulations were conducted to primarily analyze the energy dissipation level of liquefied saturated sandy soil and its impact on the displacement response of the gravity dam under seismic loads. Additionally, small-scale shaking table tests were performed to validate the feasibility of using saturated sandy soil as a damping material to reduce the seismic response of gravity dams.
## 2 Experiments
### Shielding Effectiveness (SE) Test  
The low-frequency magnetic field shielding effect of the material was measured by referring to the MIL-DTL-83528D standard that utilized the opening surface of the shield room wall and the MIL-STD-188-125-1 standard, which presented the low-frequency shielding effect measurement standard, as shown in Figure 3. In order to identify the difference between vertical and horizontal polarization, the shielding performance was measured while rotating the electromagnetic wave generator at 90 degrees.  
The aperture shielding effectiveness (SE) was calculated by the ratio of the reference mutual inductance (_Mi_) and the aperture mutual inductance (_Ms_), as shown in Equation (1) [41].  
\[SE=20\log_{10}(\frac{Mi}{Ms}) \tag{1}\]  
An effort was made to minimize the measurement error in the entire range of the SE test by making the opening size of the measuring device 1000 mm in width and length. As the size of the opening decreased, the measurement error increased. Therefore, it was possible to increase the accuracy by making a specimen as large as possible.
## 3 Results and Discussion
### Antimicrobial Evaluation  
Compounds **1-9** were tested on Gram-positive and Gram-negative bacteria, and the yeast _Candida albicans_ by the broth microdilution method. Moreover, compounds **24-26**, closely related to **6-9**, were also assayed against _B. alvei_, _B. megaterium_, and _B. pumilus_ for SAR studies. The results (see Table 1) showed that spore-forming bacteria, such as the genus _Bacillus_ are more sensitive to these compounds, among which compounds **6** and **24** exhibiting higher activity (MIC values of 1 and 2.5 \(\upmu\)g/mL against _B. subtilis_ and _B. cereus_, respectively) than cephalosporite, used as a positive control. Furthermore, the effectiveness against the Gram-positive cocci was restricted to compound **6** (_S. epidermidis_ and _E. faecalis_, MIC 5-2.5 and 20-10 \(\upmu\)g/mL, respectively). On the other hand, compounds **1-9** were inactive against the Gram-negative bacteria and the yeast _C. albicans_ (MIC \(>\) 40 \(\upmu\)g/mL), which is in line with previous studies on this type of compound [20; 21; 22; 23; 25].
## 3 Results and Discussion
### Identification of Cu(II) Inhibition Extents in Anaerobic and Aerobic Stages
#### 3.4.2 Inhibition Causes in PHA Metabolism  
Unlike phosphorus metabolism, Figure 5a shows that 38-56% of the anaerobic PHA synthesis was inhibited when 0.5-2 mgL\({}^{-1}\) of Cu(II) was added at the anaerobic stage. Meanwhile, no significant difference was found for the inhibitions of aerobic PHA degradation between the cases of Cu(II) initially present in anaerobic and aerobic conditions. This result indicated a deterioration in the ability of PAOs to utilize PHAs in aerobic conditions, resulting from the inhibition of aerobic PHA metabolism itself, and it was irrelevant to the inhibition of the previous anaerobic PHA metabolism. The inhibition percentages of PHA metabolisms were differentiated from different metabolic stages, as shown in Figure 5b. At 0.5 mgL\({}^{-1}\) of Cu(II), 38% of the anaerobic PHA synthesis was inhibited, whereas 48% of the aerobic PHA degradation was inhibited from the Cu(II) toxicity at the aerobic stage itself. At 1.0 mgL\({}^{-1}\) of Cu(II), 48% of the anaerobic PHA synthesis was inhibited, whereas 80% of the aerobic PHA degradation was inhibited, among which, only 2% of inhibition came from the previous anaerobic stage, and 78% was ascribed to the inhibition at the aerobic stage itself. At 2.0 mgL\({}^{-1}\) of Cu(II), 56%  
Figure 4: Cu(II) inhibition of phosphorus metabolisms at 20 \({}^{\circ}\)C. (**a**) Cu(II) appearance at different stages; (**b**) inhibition contributions from different stages.  
of the anaerobic PHA synthesis was inhibited, whereas 80% of the aerobic PHA degradation was inhibited from the Cu(II) toxicity at the aerobic stage itself.
# A Psychoacoustic Investigation on the Effect of External Shading Devices on Building Facades
## 3 Results and Discussion
#### 3.1.1 Average Loudness Differences: Traditional versus Sound Absorbing Louvres  
The loudness levels do not significantly vary in the case of the traditional shading system, with all the source positions, with the louvres tilted at 0\({}^{\circ}\) and 30\({}^{\circ}\) toward the sound source. The situation changes when the louvres' tilt angle rises to 45\({}^{\circ}\): The presence of the traditional louvres worsens the loudness situation, with an increase of up to 3.5 phons. This is a low variation, corresponding to less than a doubling in some, but it constitutes a worsening of the loudness over the building facade, due to the presence of the shading devices. The differences are quite similar for the loudness calculated from both signals used. The sound absorbing shading system has a good effect on the average variation of the loudness levels over the portion of the studied facade. The loudness level reduction has a maximum value of 14 phons (standard traffic noise, sound source in position S1, un-tilted louvres--Figure 7). The minimum value of attenuation in terms of loudness is even positive, around 3 phon: It is again a low difference, but this time it represents a reduction in the loudness level. In general, it is possible to assume that, with reference to the loudness levels variations, the shading device has linear dependency on the sound source position: the protection increases inversely proportionally to the angle between the source and the facade plane. Referring to Figure 5c, the shading device offers greater protection in loudness for higher floors. Figure 7 shows a linear dependency even between the louvres' tilt angle and the given loudness level reduction. The effect of the shading device on the loudness level variations is more relevant when the standard traffic noise is studied: This is to be ascribed to the higher levels at higher frequencies in this type of signal. According to an equal-loudness contour [29], it is clear how a high frequency pure tone needs a lower sound pressure level to reach the same loudness as a low frequency pure tone.