esa-sceva/satcom-chunk-collection huggingface dataset:
# Selection of Optimal Path Control Algorithms for Probe Heads Used on Five-Axis Measuring Systems
###### Abstract  
The utilization of rotational movements of a probing system during points measurements contributes to the reduction of measurement duration and increases measurement repeatability. However, knowledge on such behavior and accuracy of probing systems is still unsatisfactory. Machines combined with articulating probing systems that have the ability of continuous indexation become redundant systems, which means that the same points can be measured using almost infinite mutual configurations of the machine and probe stylus orientations. Therefore, the proper selection of inspection path planning method becomes one of the main factors affecting the accuracy of the measurement. It is possible to assess the impact of this factor on the accuracy of the measurement by comparing the results of the measurements of gauge elements, which are done using different path controlling algorithms. After that, the best method for basic measuring tasks can be chosen in order to reduce measurement errors. Measurements of the multi-feature check gauge, using the default method for path planning and those chosen on the basis of described experiments, indicates that the improvement of accuracy may reach several microns. Results presented in this paper can be directly transferred to similar systems and measuring tasks, which are commonly met in industrial and scientific practice.  
Keywords:five-axis system; CMM; dimensional measurements; inspection planning; accuracy Article
## 2 Datasets and Algorithms
### Generated Images  
#### 2.2.1 Spectrogram Image  
Thinking of a heartbeat as an audio signal, a spectrogram helps us see the different pitches (frequencies) present in that signal over time. It does this by converting the ECG signal, originally existing in the time domain (showing changes over time), into the frequency domain (showing the mix of different frequencies). A technique called short-time Fourier transform (STFT) is used for this conversion [24]. STFT essentially breaks down the signal into smaller intervals and analyzes the frequencies present in each interval. By putting these frequency analyses together over time, we obtain a spectrogram. In a spectrogram, the x-axis and y-axis represent the time and frequency domains, respectively. This allows us to see how the frequencies within the signal change over time, revealing the different rhythmic components present in the heartbeat. The color intensity represents the strength of each frequency. Brighter colors indicate stronger frequencies at that time.
## 3 Materials and Methods
### Skin Lesion Detection Using Various CNNs
#### 3.2.3. GoogLeNet  
GoogLeNet, also recognized as Inception v1, is a convolutional neural network (CNN) framework crafted by Google researchers. It was developed for the 2014 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), where it showcased notable performance enhancements compared to prior models. The distinctive feature of GoogLeNet lies in its \"Inception module\", which integrates nine such modules side by side. This design choice enables the network to capture information across diverse spatial scales by utilizing multiple filters with varying receptive field sizes. A significant innovation occurs at the culmination of the final Inception module, where global average pooling is employed. This technique adeptly extracts both local and global features from images. Additionally, the Inception module incorporates \(1\times 1\) convolutions, which serve to reduce the dimensionality of feature maps and enhance computational efficiency.  
GoogLeNet is composed of numerous stacked Inception modules, complemented by supplementary layers for classification purposes. In total, the architecture comprises 22 layers and replaces fully connected layers at the network's end with global average pooling. This strategy curtails the number of parameters within the model and mitigates the risk of overfitting. Despite incorporating 100 independent building blocks, the network retains computational efficiency and adaptability for diverse label sets [59]. The architectural configuration of GoogLeNet is depicted in Figure 10.  
The Inception architecture is a complex neural network featuring recurrent modules (illustrated in Figure 11) accompanied by subsequent max-pooling layers, which effectively halve the grid resolution. For the purpose of optimizing training efficiency, Inception modules are exclusively employed in the higher layers, while lower layers incorporate standard convolution layers. This approach permits the augmentation of units at each stage without compounding computational intricacies.
# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 3 Materials and Methods
### Datasets  
The dataset used for cross-domain experiments in the two earlier studies was small with imbalanced samples [43]. Furthermore, it was employed in the first work with its original five points [6], but it was transformed into a binary-class dataset in the second study [7], making it difficult to compare and analyze the results consistently. In our investigation, we followed the same settings as in all sentiment domain adaptation studies on English, namely a balanced binary classification dataset with 1000 positive and 1000 negative examples for each domain (80% training and 20% testing). We employed both MSA and DA datasets as follows:  
* MSA dataset: The large, public Arabic multi-domain (LAMD) dataset [12] for sentiment analysis includes 33 K annotated reviews collected from several websites covering hotels, restaurants, products and movies. Due to size restrictions, the movies domain was excluded, while the products domain required a little oversampling to balance its samples;
* DA dataset: The public multi-domain Arabic resources for sentiment analysis (MARSA) [13] dataset consists of 61,353 manually labeled tweets collected from trending hashtags in the Kingdom of Saudi Arabia (KSA) across four domains: political, social, sports and technology. We excluded the technology domain because of its size limitation. MARSA covers one country-level Arabic dialect (i.e., the Saudi dialect), which itself consists of different local subdialects that are not specified in the dataset.  
Each dataset has two attributes: text and polarity. More details and statistics are summarized in Table 1.
# Grain Boundary--A Route to Enhance Electrocatalytic Activity for Hydrogen Evolution Reaction
## 3 Results and Discussion
### Theoretical Prediction of GB-Assisted HER
#### 3.1.4 GB-Mediated Current Density  
In fact, the active sites on the Au surface with GBs consist of GB sites and normal sites, which both contribute to the hydrogen evolution. Thus, the overall HER performance of the Au(110) surface with GBs should be described by current density (\(j\)) at a given applied potential, which results from the combination of Au GBs and a flat Au(110) surface. For this purpose, we plotted the current density of hydrogen evolution as a function of the applied potential (U) using the kinetics of electrode reactions [27], which can simulate a linear scan voltammetry (LSV) curve. Under the precondition of ruling out mass transfer effects, the LSV curve of the HER can be predicted using the Butler-Volmer Equation (Supporting Information),  
\[j=j_{forward}+j_{backward}=j_{0}\Big{[}e^{-\alpha f\eta}-e^{-(1-\alpha)/\eta} \Big{]} \tag{6}\]  
where \(\alpha\) is the transfer coefficient, \(\eta\) is a certain overpotential, and \(f\) denotes F/RT. Figure 3b shows a \(j\)-U plot of Au(110) surfaces with varying amounts of GBs by controlling the ratio of GB sites (s1-s3)/normal sites. The GB-free Au(110) surface has the largest overpotential of \(-0.73\) V (vs SHE) at a current density of \(20\) uA cm\({}^{-2}\). Surprisingly, GBs induce a significant decrease in the overpotentials required for the HER. Of particular note is that only \(0.01\%\) of the GBs on Au(110) lead to a dramatic decrease in the overpotential by almost one-half from \(-0.73\) to \(0.33\) (V vs. SHE), suggesting that quite a low GB density on Au(110) can extremely boost the overall HER performance. As can be clearly seen in Figure 3, the higher the density of the Au GBs, the lower the potential that the catalyst will have. When the density of the GBs increases to \(1\%\), the overpotential to achieve \(20\) uA cm\({}^{-2}\) further reduces to \(-0.09\) V (see inset). A total of \(5\%\) of GBs can further reduce the overpotential to \(0.01\) V.  
Figure 3: (**a**) Computational exchange current density (\(\log\) (\(j_{0}\))) for hydrogen evolution over Au GB sites, and various metal surfaces plotted as a function of the hydrogen binding energy. (**b**) Current density of hydrogen evolution as a function of the applied potentials for Au(110) with varying concentrations of GBs on Au(110), and Pt(111) is presented for comparison. The inset presents a magnified version of the plot.  
However, we cannot expect even higher HER activity by continuously increasing the GB density, because there exist only a few GBs on a real catalyst surface.
# Current Trends in Fluid Viscous Dampers with Semi-Active and Adaptive Behavior
## 3 Semi-Active Fluid Viscous Dampers
### Rheological Fluid Viscous Dampers
#### 3.1.1 Electrorheological Dampers  
Electrorheological (ER) dampers are made of a cylinder, a piston, an ER fluid, and at least a pair of electrodes that produce the electric field (Figure 12).  
The use of ER fluids was first introduced by [PERSON] [117], who exploited the variation in the rheological properties of the fluid given different electric fields to transform an electrical impulse into mechanical force [118]. When an electrostatic field is applied, the fluid viscosity changes and this permits a reversible transition from Newtonian fluid to a solid-like material. Indeed, whenever the electric field is removed, the fluid rapidly (within 10 milliseconds) returns to its original form [46].  
Figure 11: ER and MR fluids’ operating modes: (**a**) flow mode; (**b**) shear mode; (**c**) squeeze-flow mode; (**d**) magnetic gradient pinch mode (adapted from [46]). N: north pole of the magnetic field, S: south pole of the magnetic field, F: force.  
[PERSON] et al. [118] tested an ER fluid damper composed of a cylinder, a piston road, and a piston head that pusheshe ER fluid through an annular duct (Figure 13), where the electric field is applied. This configuration allows the ER fluid to operate according to the flow mode, where the level of the output force is controlled by the intensity of the electric field, whereas the damper behaves as a classical FVD when the electric field is not activated.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 4 Simulation Experiment and Analysis
### Scenario 2: Multi-UAVs Search Using Association Rules
The fitness values of the heuristic algorithms, MAC and ABC, are similar, and the advantages are not obvious. However, in scenario 2, the RI-MAC algorithm has a higher degree of distinction from the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Step** & **20** & **40** & **60** & **80** & **100** & **120** & **140** & **160** & **180** \\ \hline RI-MAC & 0.1246 & 1.2171 & 1.3252 & 2.2891 & 2.9764 & 3.7862 & 3.8999 & 3.9012 & 3.9799 \\ MAC & 0.1245 & 0.6786 & 1.1649 & 1.4461 & 1.9875 & 2.9309 & 3.6971 & 3.8457 & 3.9566 \\ ABC & 0.1132 & 0.6385 & 0.9695 & 1.3456 & 1.7984 & 2.7036 & 3.6362 & 3.7121 & 3.8791 \\ PSO & 0.1214 & 0.4356 & 0.7964 & 0.9843 & 1.4532 & 2.2761 & 2.6713 & 3.3241 & 3.6659 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Target-discovery benefit of four search algorithms with association rules. Figure 7: Target-discovery benefits under two scenarios with the same algorithm. other three algorithms, with higher applicable values and smaller deviations, indicating that the algorithm proposed in this paper achieves superior performance. In Figure 8, the convergence of the four search algorithms is further compared. In scenario 1, the fitness of the RI-MAC, MAC, and ABC algorithms is better than that of the PSO algorithm. However, in scenario 2, the RI-MAC algorithm achieves a good convergence performance, which is significantly higher than that of the other three algorithms.
## 4 Performance Evaluation
### Real Measurements  
In this subsection, real measurements will be used to validate the proposed method. The setup is identical to the one used in the simulations, as shown in Figure 9. In this real-world experiment, a gun was fired at positions listed in Table 7 and marked with red + symbols in Figure 9. Based on the sensor measurements, the location estimates were computed using the proposed F-HHT algorithm and the LS solver.  
The measurements included several outliers and it was not possible to ensure the correctness of one specific reference sensor. Thus, in the tests, each sensor was selected as the reference sensor in a rotating fashion, and the F-HHT algorithm was run for each selection. The position estimate was determined based on the highest HHT value. The process is demonstrated by the measurement position (33.37, 48.24). In this experiment, 30 sensors provided measurements, and as a result, 30 independent F-HHTs were run, each using a different reference sensor. Figure 11 shows the highest values of the HHT as a function of the reference sensor index. For better visibility, the sensor indices were rearranged to display the HHT results in decreasing order: the best experiments are on the left-hand side and the worst are on the right-hand side. The corresponding positioning errors are also shown in Figure 11. Clearly, the highest HHT values correspond to small positioning errors, while small HHT indicates bad reference sensor measurement and large positioning error. In this experiment, the best HHT value obtained was 22.6, with an error of 0.43 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**True Position**} & \multicolumn{2}{c}{**Estimated Position**} & \multicolumn{2}{c}{**Estimation Error (m)**} \\ \cline{2-5}  & **LS** & **F-HHT** & **LS** & **F-HHT** \\ \hline (33.37, 48.24) & (35.93, 41.94) & (33.44, 47.81) & 6.80 & 0.43 \\ (31.94, 57.34) & (39.92, 45.81) & (32.19, 57.81) & 14.03 & 0.53 \\ (34.61, 73.91) & (44.83, 52.51) & (34.69, 73.44) & 23.71 & 0.48 \\ (28.61, 75.62) & (34.18, 38.85) & (29.06, 75.31) & 37.19 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Localization errors of LS and F-HHT, using real measurements.  
Figure 10: RMSE of LS and F-HHT as a function of number of outliers.  
The position estimation errors are listed in Table 7 for the F-HHT and the LS solutions. The F-HHT performed well in all cases, producing errors around 0.5 m. The LS solution, due to the outlier measurements, had estimation errors in the range of 6-37 m.  
Notice that in the real experiment, the exact measurement errors were not known but were estimated to be in the range of \(\sigma_{i,1}\approx 0.3\) m. The obtained error values correspond well with the error levels of the simulation tests.
## 3 Polymerization Techniques for MIP Preparation
### Controlled Radical Polymerization (CRP)  
Polymerization usually involves reaction initiation, polymer chain propagation and reaction termination. In free radical polymerization, reaction initiation is slow while chain propagation is fast. Additionally, side reactions such as chain transfer reactions between the components of the polymerization mixture or reactions with impurities can occur. As a result, polymerization is prematurely terminated and polymer chains with variable lengths are formed. For this reason, MIP size, structure and molecular weight cannot be controlled, while heterogenous cross-linked network and imprinted site distribution can be observed. Controlled radical polymerization offers control over the polymerization reaction with the use of capping agents that prevent premature termination. Reaction initiation is fast, while chain propagation is slow and simultaneous for all polymer chains. The resulting polymer chains have homogenously distributed size and molecular weight.  
In surface imprinting, the template molecule is immobilized on the surface of a suitable material, where polymerization takes place, in order to prepare MIPs with controllable imprinted sites. The template can be completely removed and the resulting imprinted sites have improved accessibility that is important for imprinting macromolecules such as proteins. However, the number of the imprinted sites is proportionate to the surface area of the substrate material [37; 38; 39].  
Core-shell MIPs can be prepared by surface imprinting on nanomaterials such as chitosan, polystyrene, SiO\({}_{2}\), TiO\({}_{2}\), and Fe\({}_{3}\)O\({}_{4}\) combining the properties of both MIPs and nanomaterials. For the preparation of magnetic MIPs (MMIPs), appropriately prepared Fe\({}_{3}\)O\({}_{4}\) nanoparticles undergo functionalization or surface modification and then molecular imprinting takes place on the surface of the magnetite nanoparticles, either with a free radical polymerization method or the sol-gel process. MMIPs can be collected from a solution with the application of an external magnet, thus they can be used in sample preparation providing an easier, less time-consuming and efficient analyte extraction procedure [37; 38; 45; 46].
# Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects +
## 6 Applications
### Routing Protocols  
Internet routing has a dynamic nature because a link may change its status to up or down during the transmission of the packets. Besides that, routing protocols are MODM problems as well [79; 80; 81; 82], where the objectives may be listed as:  
* _Maximizing bandwidth_, thus allowing for rapid transfer of data between nodes;
* _Minimizing latency_, to keep buffering low during distributed real-time applications;
* _Maximizing redundancy_, then providing multiple data paths so that connectivity is preserved if some nodes go offline.  
Routing protocol challenges are present in streaming platforms with a high bandwidth, as well as real-time applications such as video calls requesting low latency. Given these requirements, routing can be modeled as an MODSP problem, where the system can dynamically redirect traffic flow to achieve better results.
## 4 Discussion
### Discussion on Human Health Risk Assessment  
To evaluate the potential human health risks associated with herbicide residues in local farmland soils, we conducted a comprehensive non-carcinogenic risk assessment based on the residual levels of five commonly used herbicides. The exposure assessment focused on adult and child populations, employing widely accepted methodologies and standardized parameters (Table 3) to estimate CDI via three primary exposure pathways: soil ingestion, dermal contact, and inhalation.  
The HQ, defined as the ratio of estimated CDI to the RfD of each pesticide, was used to quantify the non-cancer health risk associated with each exposure route. As shown in Table S1, the calculated CDI values (mg kg\({}^{-1}\) day\({}^{-1}\)) for adults across all exposure pathways were consistently higher than those for children. Similarly, HQ values for adult exposure to atrazine, acetochlor, and alachlor through ingestion, dermal, and inhalation routes were notably greater than those for children. This disparity primarily reflects differences in body weight and exposure frequency between the two groups.  
According to USEPA guidelines, an HQ or hazard index (HI) value of 1 serves as the threshold for acceptable risk. An HQ or HI exceeding 1 indicates a potential for adverse non-carcinogenic health effects, whereas values below 1 suggest that the risk is minimal under current exposure conditions [73; 74]. In this study, the computed HQ and HI values for all evaluated herbicides were substantially lower than the threshold, indicating that the non-carcinogenic health risks posed by soil contamination are currently negligible for both adults and children within the study area.  
Nonetheless, it is important to recognize that children may be more vulnerable to pesticide exposure due to their ongoing physiological development, increased hand-to-mouth behavior, and generally lower body weight, which can amplify internal doses relative to adults. These factors contribute to a relatively higher exposure risk for children, despite overall low HQ values [75]. Therefore, while the present findings suggest that the general population faces minimal non-cancer health risks from current soil contamination levels, special attention should still be given to protecting sensitive subpopulations.  
From a broader perspective, the co-occurrence of multiple herbicides in agricultural soils may alter their environmental fate and persistence, potentially leading to cumulative or synergistic effects that are not captured by individual risk assessments. Several environmental variables--including soil texture, pH, organic matter content, and microbial community structure--can influence herbicide degradation kinetics and thus modify exposure scenarios over time.  
To address these complexities, this study further examined the natural degradation dynamics of the five target herbicides under different tillage practices. This investigation aims to provide a scientific basis for optimizing agricultural management strategies and promoting sustainable pesticide use practices that minimize long-term environmental and health risks.
## 3 Results of Analysis of Ground Subsidence Risk Levels Using Machine Learning
### Evaluation Indexes of Machine Learning Algorithms  
For evaluation indexes of machine learning models to solve a classification problem, accuracy, F1-score, and AUC are generally used. The results of these evaluation indexes can be calculated using Equations (6)-(10) via the confusion matrix.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{6}\]  
\[\text{Recall}(\text{Sensitivity})=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{8}\]  
\[\text{F1 Score}=2\times\frac{\text{Precision}\,\times\,\,\text{Recall}}{\text {Precision}\,+\,\,\text{Recall}} \tag{9}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{10}\]  
Intuitively, it is highly convenient if the model's performance is evaluated through the model's accuracy, but it is also difficult to identify the objective model performance for imbalanced data. Thus, a model using imbalanced data is evaluated by employing the F1-score, which uses a harmonic mean of the data. The model confidence is evaluated using the AUC that uses the receiver operation characteristic (ROC) [28; 29; 30; 31; 32; 33; 34].