esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antibacterial Analysis  
Figure 15 shows that the initial PET fiber has a 0 cm circle of inhibition against _E. coli_ and _Staphylococcus aureus_, indicating that PET fiber does not have antibacterial activity. Compared to PET fibers, CuBTC@PET fibers showed an increase in the size of the zone of inhibition with the increase in the growth cycle. The zone of inhibition of the SCP was 0.25 cm for _E. coli_ and 0.55 cm for _S. aureus_. For the difference in the antimicrobial performance of the two strains, current research suggests that there are two main reasons:  
(1) Differences in the cell wall structure of the two. In Gram-negative bacteria (_Staphylococcus aureus_), the cell wall of the outer membrane component is a lipopolysaccharideacharide, and lipopolysaccharideacharides have a strong toxicity and can resist the invasion of metal ions and other substances, while the inhibitory mechanism of CuBTC through the destruction of the DNA structure and the important enzyme interferes with the signaling, accumulates in the cell wall and reduces its permeability, and induces the leakage of substances in the cell, which in turn inhibits bacterial growth or kills the bacterium directly. So, it is not likely that Gram-positive bacteria (_E. coli_) are susceptible to killing by copper ions [47].  
Figure 14: Filtration efficiency for different samples: (**a**) filtration efficiency of different PET fiber membrane thicknesses, (**b**) filtration efficiency of different treatment stages, (**c**) relationship between filtration efficiency and filtration resistance of 3 \(\upmu\)m NaCl particles.  
(2) Metal ion chargeability effect. CuBTC consists of Cu\({}^{2+}\) ions and benzotrizoic acid, and CuBTC@PET can slow the release of copper ions and provide an antibacterial effect based on metal ions. Cu\({}^{2+}\) ions can interact with cell wall proteins to disrupt the structure of the cell, and cellular internalization triggers the leakage of cellular contents, which in turn react with enzymes and DNA to block cellular functions and inhibit the growth of bacteria. The bactericidal effect against _E. coli_ is mainly attributed to the substantial cell damage caused by copper ions. In contrast, the efficient antibacterial effect against _Staphylococcus aureus_ is attributed to the fact that positively charged Cu\({}^{2+}\) ions tend to attach to the surface of negatively charged Gram-positive bacteria, binding tightly and then leading to the rupture of the cell [48]. This also explains the higher antibacterial activity of CuBTC@PET against _Staphylococcus aureus_ than _E. coli_. This is a physical killing mechanism that does not involve chemicals or antibiotics.
## 3 The LOBIN Prototype: A Practical Study Case
### System Description
#### 3.1.2 Location Subsystem
It is worthwhile to remark upon the fact that the location algorithm needs at least four pairs (BP identifier, LQI) in order to work properly.
## 3 Joint Motion Planning Method
### Cubic Polynomial Planning  
Polynomial interpolation is commonly used to interpolate between discrete points, which is consistent with PTP motion characteristics of industrial robots, so it is often used in motion planning of industrial robots. Among the polynomials, the cubic polynomial is widely used in the PTP motion planning of industrial robots. Cubic polynomial interpolation function is shown as below formula (5):  
\[\theta(t)=a_{0}+a_{1}t+a_{2}t^{2}+a_{3}t^{3} \tag{5}\]  
where \(t\) is the time variable and \(a_{0}\), \(a_{1}\), \(a_{2}\) and \(a_{3}\) are four coefficients of the cubic polynomial, which need to be found out according to the boundary constraints of angular displacement and angular velocity at two adjacent target points.  
Put the start time, the end time, the angular displacements and the angular velocities at the two target points into formula (5) and its first derivative, respectively. After sorting, Equation (6) for solving the coefficients can be obtained.  
\[\left[\begin{array}{c}a_{0}\\ a_{1}\\ a_{2}\\ a_{3}\end{array}\right]=\left[\begin{array}{cccc}1&t_{0}&t_{0}^{2}&t_{0}^{3} \\ 1&t_{1}&t_{1}^{2}&t_{1}^{3}\\ 0&1&2t_{0}&3t_{0}^{2}\\ 0&1&2t_{1}&3t_{1}^{2}\end{array}\right]^{-1}\left[\begin{array}{c}\theta_{0 }\\ \theta_{1}\\ \omega_{0}\\ \omega_{1}\end{array}\right] \tag{6}\]  
where, \(t_{0}\) is the start time, and \(t_{1}\) is the end time. \(\theta_{0}\) and \(\omega_{0}\) are respectively angular displacement and angular velocity at the first target point of a joint, and \(\theta_{1}\) and \(\omega_{1}\) are respectively angular displacement and angular velocity at the next target point. \(\theta_{0}\), \(\theta_{1}\), \(\omega_{0}\), \(\omega_{1}\) can be obtained through inverse kinematics. For general PTP motion, \(\omega_{0}\) and \(\omega_{1}\) are both zeros. For velocity-constrained PTP motion, \(\omega_{0}\) and \(\omega_{1}\) may not be zeros. After finding out the coefficients, the cubic polynomial planning function can be obtained, which will satisfy the boundary constraints of angular displacement and angular velocity at the target points.  
Because of simplicity, clarity and small computation, the cubic polynomial is widely used in PTP joint motion planning. However, if a cubic polynomial is applied to PTP motion planning with constrained velocity, angular acceleration is discontinuous at the connecting points between every two adjacent planning segments, as shown in Figure 4c. Broken points of acceleration will cause vibrations and impacts, which is disadvantageous to the working performance and service life of the industrial robot.  
Figure 3: PTP motion with constrained velocities.
## 4 Results and Discussions
### Impact of Lens Parameters  
From the transfer function of lens given in Equation (12), the phase modulation or the focusing performance of lens is not only related to the light field intensity of incident beam \(E_{0}\) and the value of nonlinear coefficient \(\alpha\), but also to the parameters of lens, including the focal length \(f_{0}\) and refractive index \(n_{0}\). In view of this fact, we also studied the influence of focal length and refractive index of lens on the normalized field pattern of focal spots in the nominal focal plane for the three different incident laser beams of field intensity \(E_{0}=5\times 10^{8}\) V/m as shown in Figure 14. By comparing the results shown in Figure 14a-f, we can see that, for a shorter focal length of lens with \(f_{0}=1\) m, the sizes of focal spots shrink proportionally and the patterns of focal spots change in comparison with the cases with \(f_{0}=2\) m under the same refractive index of lens \(n_{0}=1.5\), especially for the case of Hermite-Gaussian  
Figure 12: The different patterns of focal spots in the six planes, which are of (**a**) 0.03 mm, (**b**) 0.02 mm, and (**c**) 0.01 mm left to the nominal focal plane, respectively, and (**d**) 0 mm, (**e**) 0.01 mm, and (**f**) 0.02 mm right to the nominal focal plane for the incident Laguerre-Gaussian beam with field intensity \(E_{0}=5\times 10^{8}\) V/m.  
Figure 13: The different field patterns of focal spots in the six planes the same as those specified in Figure 12 for the incident Laguerre-Gaussian beam with field intensity \(E_{0}=5\times 10^{8}\) V/m, where the Kerr effect of optical lens is not taken into consideration by setting the nonlinear coefficient \(\alpha=0\).  
beams. On the other hand, when we change the value of refractive index from \(n_{0}=1.5\) to \(n_{0}=1.7\), the results shown in Figure 14d-i illustrate that the refractive index of lens also can influence the field patterns of focal spots under the same focal length of lens \(f_{0}=2\) m. Therefore, in practical application, the field patterns of focal spots should be calculated based on the specific laser beam and lens parameters of engineering problem.
## 4 Experiments
### Performance Comparison
#### 4.4.3 Comparative Experiments on the SYSU-CD Dataset  
The quantitative results of all kinds of comparison methods with DETDNet performed on the SYSU-CD dataset are exhibited in Table 6. As displayed in Table 5, our model outperformed the second highest model in the F1 score, IoU, and OA by 1.41%, 1.98%, and 0.71%, respectively. As for the visualization results, they are presented in Figure 9. The main changes in rows 1, 2, 3, 4, and 6 were the building expansions. It can be seen that whether it was the new buildings around the vegetation in rows 1 and 2, the expansion of the seaside buildings in row 3, or the building expansions around the highway in rows 4 and 6, our model was better able to handle the change boundaries and obtain more accurate boundaries. For the change area in the middle of row 2 and the color change of the building roof in row 4, our model recognized the pseudo-change due to the color better. For the change type of vegetation in rows 2 and 5, we can see that our model also had better prediction results. From a comprehensive point of view, since the scenes in the SYSU-CD dataset were relatively complex, there may be various types of changes in the single image, which makes it more difficult to detect, and our model was comparatively better at extracting the features of the different changes and arriving at a more accurate change map.  
Figure 8: Visualization results of several CD methods on the LEVIR-CD dataset. (**a**) T1 images. (**b**) T2 images. (**c**) Ground truth. (**d**) FC-EF. (**e**) FC-Siam-conc. (**f**) FC-Siam-diff. (**g**) CDNet. (**h**) STANet. (i) BIT-CD. (**j**) SNUNet. (**k**) ChangeFormer. (**I**) DETDNet.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Params.(M)** & **Flops(G)** & **Precision** & **Recall** & **F1** & **IoU** & **OA** \\ \hline
**FC-EF** & 1.29 & 2.92 & 82.27 & 66.28 & 73.41 & 58.00 & 97.55 \\
**FC-Siam-conc** & 1.93 & 4.55 & 86.81 & 67.66 & 76.05 & 61.36 & 97.83 \\
**FC-Siam-diff** & 1.75 & 3.99 & 86.55 & 74.38 & 80.00 & 66.68 & 98.11 \\
**CDNet** & 1.36 & 21.52 & 88.38 & 85.08 & 86.70 & 76.52 & 98.67 \\
**STANet** & 16.93 & 6.58 & 80.99 & 91.21 & 85.79 & 75.12 & 98.46 \\
**BiT** & 3.55 & 10.6 & 91.95 & 88.57 & 90.23 & 82.19 & 99.02 \\
**SNUNet(48)** & 28.34 & 97.87 & 91.66 & 88.48 & 90.04 & 81.89 & 99.00 \\
**ChangeFormer** & 267.90 & 129.27 & 91.53 & 88.86 & 90.17 & 82.10 & 99.01 \\ \hline
**DETDNet** & 56.33 & 18.08 & 89.82 & 92.19 & 90.99 & 83.47 & 99.07 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results on the LEVIR-CD dataset.
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 2 Fundamentals of the Model
### Variational Mode Decomposition  
Variational Mode Decomposition (VMD), an adaptive and entirely non-recursive signal decomposition technique, is commonly applied to handle non-stationary signals. It decomposes the original signal into IMFs with distinct frequencies. These components demonstrate specific regularities in their respective frequency bands, thus effectively reducing the complexity of the original signal. The detailed decomposition steps are as follows.  
The input data are firstly decomposed into variational problems with constraints:  
\[\left\{\begin{aligned} \underset{\{u_{k}\},\{\omega_{k}\}}{ \underset{\{\kappa\}}{\min}}\left\{\underset{k}{\sum}\Big{\|}\partial_{t} \Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}(t)\Big{]}e^{-j\omega_{k}t }\Big{\|}_{2}^{2}\right\}\\ s.t.\underset{k}{\sum}u_{k}(t)=f(t)\end{aligned}\right. \tag{4}\]  
where \(u_{k}\) means the \(k\)th mode, \(\omega_{k}\) is the \(k\)th mode center frequency, and \(\delta(t)\) means the Dirac distribution.  
The introduction of quadratic penalty function terms and the Lagrange multiplier method transforms the constrained variational problem into an unconstrained variational problem. The quadratic penalty function can ensure that the time series still maintains a high reconstruction accuracy under the premise of including noise; the Lagrange multiplier can be used to ensure the validity of the constraints of the variational constraint model.  
\[\begin{aligned} L(\{u_{k}\},\{\omega_{k}\},\lambda)= \alpha\underset{k}{\sum}\Big{\|}\partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{ \pi t}\Big{)}*u_{k}(t)\Big{]}e^{-j\omega_{k}t}\Big{\|  
where \(n\) is the number of iterations, \(\hat{u}_{k}^{n+1}(\omega)\) is the Wiener filter for the current residual, and \(\hat{f}(\omega)\), \(\hat{u}_{k}(\omega)\), and \(\hat{\lambda}(\omega)\) are the Fourier transforms of \(f(t)\), \(u_{k}(t)\), and \(\lambda(t)\).
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
###### Abstract
Forklift vibration levels are mainly dependent on the type of forklift structure, speed, payload, and road roughness, respectively. Although many published papers have focused on the exposure of vibration to the human body in forklift operators, these papers primarily observed and analyzed the vibration circumstances over the seat location [20; 21; 22]. There are no studies that have been conducted with the aim of packaging testing purposes that have obtained vibration data at or around the fork tines. Therefore, the purpose of this study was to measure and analyze the vibration levels of common forklifts types using different handling conditions. The research was developed in a laboratory environment that simulated different handling environments. The main objective of this project was to analyze the vibration levels experienced by different industrial forklifts under different conditions. Specific objectives included: (1) Measure vibration levels experienced by a gas-powered forklift, an electric-powered forklift, and a reach truck; (2) Determine the effects of road condition, unit load weight, speed, forklift type, and sensor location on the vibration levels.
# Effects of Ethanol-Diesel on the Combustion and Emissions from a Diesel Engine at a Low Idle Speed
## 2 Methodology
### Experimental Setup and Measurements  
#### 2.2.1 Engine and Experimental Setups  
A four-cylinder 2.0 L common-rail direct injection diesel engine was used for this test. The fuel injection systems (solenoid injectors, fuel pump, common rail) of Bosch and an ECU (engine control unit) were applied. The detailed specifications are shown in Table 2.  
The test engine was installed on an eddy current dynamometer (DY-230 kW, Hwanwoong Mechatronics, Gyeongsangnam-do, Korea). A piezoelectric pressure sensor (Kistler, 6056a, Winterthur, Switzerland), located at the position of the glow plug, was used to measure the combustion pressure, and the data was recorded and analyzed by a data acquisition board (PCI 6040e, National Instruments, Austin, TX, USA). The angular position of the crankshaft for the analysis was measured by a rotary encoder (E6B2-CWZ3 DE, Omron, Japan). The emission levels of NOx and CO were measured with an MK2 multi-gas analyzer (Euroton, Italy), and HC was measured with an HPC-501 analyzer (Pantong Huapeng Electronics, China). The smoke opacity level was measured using a partial flow collecting soot analyzer OPA-102 (Qurotech, Korea). The fuel flow rate used during the tests was calculated by measuring the fuel weight change over 10 min on a high-precision digital electronic weighing balance (GP-31K, A&D, Japan). The exhaust gas temperature was measured just after the turbocharger. The soot emitted from the engine was collected by a copper grid (FCF400-CU, Electron Microscopy Sciences, PA, USA), and was used to analyze the soot particle sizes by transmission electron microscopy (TEM, H-7650, HITACHI, Fukuoka, Japan). The experimental equipment diagram is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Engine Parameters** & **Unit** & **Specification** \\ \hline Engine Type & - & In-line 4 Cylinder, WGT Turbocharged, EGR \\ Maximum Power/Torque & kW/Nm & 84.6(@4000 rpm)/260(@2000 rpm) \\ Bore \(\times\) Stroke & mm \(\times\) mm & 83 \(\times\) 92 \\ Displacement & cc & 1991 \\ Compression Ratio & - & 17.7:1 \\ Number of Injector nozzle holes & - & 5 \\ Injector type & - & Solenoid \\ Injector hole diameter & mm & 0.17 \\ \hline \end{tabular}
\end{table}
Table 2: Engine specifications.  
Figure 1: Experimental fuels blended with ethanol in diesel.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Size** & **Industry/Product** & **Project Focus** & **Cost Impact (\%)** & **Time Impact (\%)** & **Reliability Impact (\%)** & **Cost Saving/yr.** & **Other Benefits** \\ \hline
13 & M & Beverages & Inventory management (ILPI7) and warehouse management (ILPI6) & \(-\)16.74\% & & +10.7\% & 228,652 & \\ \hline
14 & M & Glass Production & Logistics communication and order processing (ILPI3) & \(-\)18.92\% & & & 548,240 & \\ \hline
15 & M & Medical Equipment & Logistics communication and order processing (ILPI3) and transportation (ILPI8) & \(-\)25.00\% & \(-\)80.00\% & +27.3\% & 419,577 & \\ \hline
16 & M & Medical Supplies & Inventory management (ILPI7) and warehouse management (ILPI6) & \(-\)34.50\% & & & & Sales increase: THB & \\ \hline
17 & M & Retail & Inventory management (ILPI7) & \(-\)51.30\% & \(-\)43.70\% & +31.2\% & 520,000 & \\ \hline
18 & M & Garments & Logistics communication and order processing (ILPI3) & \(-\)16.25\% & & +7.5\% & 550,309 & \\ \hline
19 & L & Transportation & Transportation (ILPI8) & \(-\)24.89\% & & & & Sales increase: THB & \\ \hline
20 & L & Retail & Transportation (ILPI8) & \(-\)18.90\% & & & & 1,415,737 & \\ \hline
21 & L & Retail & Inventory management (ILPI7) and warehouse management (ILPI6) & \(-\)7.50\% & \(-\)5.60\% & +22.3\% & 1,779,769 & \\ \hline
22 & L & Construction & Transportation (ILPI8) & \(-\)15.04\% & & & & 2,510,139 & \\ \hline
23 & L & Poultry & Logistics communication and order processing (ILPI3) and material handling and packaging (ILPI5) & \(-\)16.02\% & & & & Sales increase: THB & \\ \hline
24 & L & Beverage & Material handling and packaging (ILPI5) and inventory management (ILPI7) & \(-\)16.50\% & \(-\)7.50\% & & 970,000 & \\ \hline
25 & L & Transportation & Inventory management (ILPI7) & \(-\)15.87\% & & +15\% & 995,000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The following sections present exemplary case studies from the factory logistics improvement projects conducted between 2022 and 2024. These cases illustrate the practical applications and outcomes of logistics enhancement initiatives across various industries in Northern Thailand. Thus, the selected case studies presented in this paper are categorized into three main logistics components, i.e., transportation management, warehouse and inventory management, and logistics administration management.
## 5 Unresolved Issues and Future Research
And, of course, participants might balk in the face of mandatory personal information or they may supply inaccurate information. Most likely, voluntary participation will generate higher-quality data and result in satisfied users. Assurances via privacy statements, and strict compliance with them, should also boost cooperation. In spite of potential data collection barriers, it's important to note that this application of predictive analytics is notably different from others in which subjects might not wish to be identified. It's unlikely, for example, that terrorist suspects or people engaging in fraud will willingly provide data about themselves. Similarly, consumers who wish not to be bombarded with advertisement have little incentive to provide personal information. Here, though, the data collected from students will be used to help them succeed. A clear statement to this effect might go a long way toward encouraging students' participation with data collection. Although the system described here is rooted in statistical predictive models, there may also be a role for text mining. We imagine a situation in which text mining is used to evaluate a student's remarks in a threaded discussion, for example. That information might then become one component of a more comprehensive assessment of student performance. The messages generated by this system are intended to be probabilistic; they are estimates, not assurances, of outcomes. Students are familiar with the deterministic world of learning assessment; e.g., that a final weighted average between 89 and 92 will result in a course grade of A\({}^{-}\). Here, students might be challenged by the more ambiguous nature of the probabilistic information provided. Consideration should be given to whether students could misinterpret estimates as guarantees. In designing the dashboard, the probabilistic nature of the information provided should be made clear via the wording, emphasis, or phrasing of a message or by including appropriate annotation to graphical displays. Similarly, struggling students might use the information not as motivation to improve but as a reason to drop out. The hypothetical student depicted in Figure 4 has made positive progress throughout the term and has reacted as we might hope in response to the messaging system. Another student, though, might learn that efforts only result in declining estimates of success and become discouraged. For her, a proactive messaging system such as the one in Figure 5 might be critical to improving motivation. The dashboard might also be designed to alert instructors to those making little progress, thereby identifying students who might benefit from individual consultation.
# Suitability Evaluation of Urban Underground Space Development: A Case Study of Qingdao City
## 4 Comprehensive Evaluation Model
### Multi-Objective Linear Function Weighted Model  
The multi-objective linear weighted function method is an approach to solving multi-objective optimization problems. Its basic principle involves transforming multiple objectives into a single comprehensive objective function by assigning different weights, thereby enabling the evaluation and ranking of decision alternatives. This method balances and integrates multiple objectives through linear combinations, aiming to optimize the comprehensive evaluation value. The specific construction process is as follows:  
1. Objective Function Determination: Suppose there are \(n\) decision alternatives, each with corresponding evaluation values for m objectives. Let \(f_{ij}\) represent the evaluation value of the \(i\)-th decision alternative on the \(j\)-th objective, where \(i\) = 1, 2, 3, \(\ldots\), \(n\) and \(j\) = 1, 2, 3, \(\ldots\), \(m\). In this experiment, \(n\) = 13 and \(m\) = 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Weight Assignment Method**} \\ \cline{2-9}
**Evaluation Depth** & \multicolumn{3}{c}{**Analytic Hierarchy Process**} & \multicolumn{3}{c}{**Analytic Hierarchy Process with Variable Weights**} \\ \cline{2-9}  & **Min** & **Max** & **Avg** & **SD** & **Min** & **Max** & **Avg** & **SD** \\ \hline Shallow layer & 0.256 & 0.987 & 0.789 & 0.096 & 0.245 & 0.965 & 0.738 & 0.098 \\ Middle to deep layers & 0.238 & 0.929 & 0.718 & 0.093 & 0.226 & 0.907 & 0.678 & 0.097 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison table of results for different weighting methods.  
Weight Assignment: Weight assignment is the essence of the multi-objective linear function weighting method. In this study, the Variable-Weighted Analytical Hierarchy Process (AHP) method, which yielded superior results after comparison, was adopted for weight assignment in the model. Additionally, the weights were standardized to ensure that the sum of all weights equals 1, represented as \(\sum_{j=1}^{m}w_{j}=\)1, thereby ensuring the rationality and comparability of the weights.
3. Constructing the Weighted Composite Objective Function: For the \(i\)-th solution, its weighted composite objective function is defined as: \(F_{i}=\sum_{j=1}^{m}w_{j}\cdot f_{ij}\), where \(f_{ij}\) represents the rating value of the \(i\)-th decision solution on the \(j\)-th objective, and \(w_{j}\) denotes the weight of the \(j\)-th objective.