esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of Input Parameters on Surface Roughness  
The influence of each process parameter on surface roughness (Ra) was further analyzed using a perturbation diagram, which illustrates the effects of changing one factor at a time while keeping the others at their central values. As can be seen in Figure 4, cutting speed (A) has the greatest influence on Ra, followed by laser power (B), while auxiliary gas pressure (C) has a relatively small influence. The dominant influence of cutting speed can be attributed to the phenomenon of melt jet retardation at higher speeds, where the interaction between the molten material and the assist gas is less effective. This delay can lead to incomplete material removal and an increase in surface roughness. Laser power also has a significant impact on Ra, as more energy enters the cutting zone at higher power, increasing the amount of molten material and potentially creating a rougher surface if it is not ejected properly. Although gas pressure showed a limited impact on surface roughness within the experimental range, its presence remains essential for maintaining cut quality. It facilitates the efficient removal of molten material and prevents dross formation, contributing to cleaner and more stable cutting conditions.  
To complement the qualitative analysis of the perturbation diagram (Figure 4), a quantitative sensitivity analysis is presented in Figure 5. The contribution of each significant factor from the reduced RSM model was calculated based on the normalized sum of squares from the ANOVA. As can be seen from the bar chart, the quadratic term A\({}^{2}\) (cutting speed squared) had the greatest influence on surface roughness, contributing approximately 40.6%, followed by B\({}^{2}\) (laser power squared) at 27.7%, A (cutting speed) at 20.4%, and B (laser power) at 11.2%.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Statistic** & **Value** & **Statistic** & **Value** \\ \hline Std. Dev. & 0.26 & R-Squared & 0.8227 \\ Mean & 2.59 & Adj R-Squared & 0.7637 \\ C.V. \% & 9.98 & Pred R-Squared & 0.5898 \\ PRESS & 1.85 & Adeq Precision & 11.399 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Basic statistical data of the adopted model.  
Figure 4: Effect of process parameters on surface roughness.  
These contributions reflect the nonlinear and dominant role of cutting speed in the laser cutting process. The strong influence of A\({}^{2}\) indicates that both too low and too high cutting speeds can deteriorate the surface finish, probably due to the instability of the melt pool and insufficient material removal. Similarly, the influence of B\({}^{2}\) suggests that the surface roughness also depends on the fine-tuning of the laser power: too low a power leads to incomplete cutting, while too high a power increases the molten material, causing rougher surfaces. The lower contribution of B confirms its secondary but nevertheless important role in the energy balance of the process.
## 2 Materials and Methods
### Untargeted Metabolomics
#### 2.4.2 Analysis Condition  
Metabolomics analysis was conducted using a UPLC system (ThermoScientific; Waltham, MA, USA) coupled with a Hypesil Gold column (1.9 \(\upmu\)m, 100 mm \(\times\) 2.1 mm, Thermo Fisher, Waltham, MA, USA) and an Orbitrap Q ExactiveTM HF-X mass spectrometer (Thermo Fisher, Dreieich, Germany). In positive ion mode, the mobile phase included 0.1% formic acid in water (\(v/v\), A) and methanol (B). In negative ion mode, the mobile phase consisted of a 5 mM ammonium acetate solution in water (A) and methanol (B). The gradient elution was set as follows: 0-1.5 min, 2% B; 1.5-12.0 min, 2-100% B; 12.0-14.0 min, 100% B; 14.0-14.1 min, 100-2% B; and 14.1-17.0 min, 2% B. The flow rate was maintained at 0.2 mL/min, and the injection volume was 2.0 \(\upmu\)L [29].  
Mass spectra were acquired with an electrospray ionization (ESI) source in both positive and negative modes with the range of mass \(m\)/z 100-1500. The ESI parameters were as follows: spray voltage of 3.2 kv, capillary temperature of 320 \({}^{\circ}\)C, sheath gas flow rate of 40 arb, and aux gas flow rate of 10 arb. Samples were analyzed three times repetitively.
## 6 Experiments and Results
### Joint Operation  
The first experiment with this robot demonstrates the actuation of the joints using the MA. The experiment also measured the rotational and linear speeds of the joint actuation and the MA's travel parameters, as recorded in Video S1 (see Figure 15). In step (A), the arm, starting from its initial position, was rotated by \(-90\) degrees. In step (B), the robot is shown after the MA rotated the first joint around the \(x\)-axis by \(90\) degrees at \(14\) degrees per second. Note that the first joint can be rotated indefinitely since there is no wiring along the links. In step (C), the MA travelled along the arm's length using the locomotive mechanism at \(8\) cm per second until it reached the third joint. In step (D), the MA rotated the third joint around the \(z\)-axis to an angle of \(45\) degrees at a speed of \(14\) degrees per second. In step (E), the third joint was returned to its original position, and the MA began advancing toward the prismatic joint, leading to the situation depicted in step (F). In step (G), the MA activated the prismatic joint, and shortened it by \(10\) cm at a speed of \(8\) cm per second. In step (H), the MA moved to the end of the arm and closed the gripper, as shown in Video S1.  
This experiment highlights the robot's ability to precisely control joint movements and the MA's capability to traverse the arm and manipulate various joints effectively.
# Detecting and Localizing Dents on Vehicle Bodies Using Region-Based Convolutional Neural Network
## 3 Performance Evaluation  
### Methods and Material  
We used a vehicle body fender (Figure 5a) with 25 artificial defects (diameter: 0.5-10 mm) to collect normal and abnormal images. The body of vehicle was fixed at 80 cm high on the top of a support fixture, as shown in Figure 5b. The illuminance level of the room was controlled around 500 lux and a researcher seated on an office chair recorded videos. The lighting device employed in this study was placed 32.5 cm (range: 30-35 cm) away from the target inspection surface and was slowly moved above the surface to scan the entire inspection area. A general video camera was employed to capture videos while scanning the target surface with the lighting device. In this study, all video clips were taken from the camera and 8017 image frames were extracted from the video clips.  
This study preprocessed the images extracted from the recorded videos in three steps (gray scale conversion, image segmentation, and labeling). In the first step, 8017 images (120 \(\times\) 68 pixels) were randomly sampled from the video clips and were transformed into gray scales in order to generalize the evaluation results by eliminating the effects of car body color. In the second step, image patches (32 \(\times\) 32 pixels) for each image were prepared by visiting all locations of the image and all patches were manually classified as either normal or abnormal. Then, we randomly selected an equal number of normal (2200) and abnormal patches (2200) for training and testing the R-CNN.  
Figure 4: A heat-map to localize the location of a dent.  
Figure 5: An example of vehicle fender and experimental scene used in this study to record videos, (**a**) Vehicle body fender (**b**) Experimental scene.  
The normal and abnormal patches were randomly divided into training (70%) and testing (30%) data sets. Thus, the R-CNN was trained with 3000 randomly selected patches (1500 normal, 1500 abnormal) from the total number of patches (4400). Next, the classification accuracy of the R-CNN was quantified with the remaining 1400 patches (700 normal, 700 abnormal) that were not utilized in the R-CNN training. Lastly, the localization accuracy of the R-CNN was calculated for each whole image, and it was judged as correct when an actual dent was located in a bounding box formed by the R-CNN.
# Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
## 4 Experimental Study
### Methods Compared  
We have compared our DAE-DBC which is deep autoencoder reconstruction error based method with the unsupervised state-of-the-art methods below.  
* **OC-SVM**. OC-SVM is used for novelty detection based on building prediction model from only normal dataset [23]. Eta-SVM and Robust SVM that an enhanced OC-SVM are introduced by [PERSON] et al. [20] and they suggest an implementation which is an extension of Rapidminer. We use this extension configured by RBF kernel function for these three algorithms.
* **PCA based methods**. PCA is commonly used for outlier detection and calculates the outlier score using the principal component scores and reconstruction error which is the orthogonal distance of each data point and its first principal component score. The Gaussian Mixture Model and Cumulative Distribution Function are used to make novelty detection decision from Outlier score [35; 36]. We compare the PCA-based methods including PCA-GMM, PCA-CDF, Kernel PCA-GMM, Kernel PCA-CDF and Robust PCA [25].  
We present a series of proposed methods by modifying the components to show that the components of the proposed DAE-DBC method together are more effective. In other words, we have tested with some methods, such as the proposed approach with K-means, proposed approach with PCA and proposed approach with KPCA.  
* **DAE-DBC (Proposed method)**. The structure of the deep autoencoder is composed of the 5 number of layers like {input layer (n neurons) \(\rightarrow\) encoding layer (n/2 neurons) \(\rightarrow\) encoding layer (1 neuron) \(\rightarrow\) decoding layer (n/2 neurons) \(\rightarrow\) decoding layer (n neuron)), where n is the number of neurons. In here, the number of neurons in a bottleneck hidden layer which is the compressed representation of the original input is equal to one, other hidden layers are composed of neurons of about a half of the input neurons. The sigmoid activation function is used for encoding type-layers, the tanh activation function is for decoding-type layers respectively. Reconstruction error and compressed representation are grouped by density-based DBSCAN clustering algorithm and the novelty threshold will decide whether the group is the novelty.
* **DAE-Kmeans**. Compare with the proposed method using the K-means clustering algorithm instead of density-based clustering. K-means clustering algorithms require the number of cluster k and we have used Silhouettes analysis which is used for evaluation of clustering validity to find optimal k [37].
* **PCA-DBC, KPCA-DBC.** Our proposed method is based on reconstruction error. So, we have used PCA and KPCA in place of AE for calculating reconstruction error and compressed representation.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Algorithmic Approaches
#### 4.5.2 The Three-Dimensional Double Travelling Salesman Problem with Multiple Stacks  
The Double Travelling Salesman Problem (DTSP) is a well-known and challenging combinatorial optimisation problem that has significant relevance in logistics, transportation, and supply chain management. It is an extension of the classic Travelling Salesman Problem (TSP), a problem of finding the shortest closed tour that visits a set of given locations exactly once. The DTSP introduces an additional layer of complexity by requiring two salesmen to find their individual shortest routes while covering the same set of locations [PERSON] et al. (2018).  
The DTSP has attracted considerable attention in the field of operations research due to its real-world applications in various domains. One such extension of the DTSP is the Three-Dimensional Double Travelling Salesman Problem with Multiple Stacks (3L-DTSPMS), a problem introduced by [PERSON] et al. (2018). In this advanced variant, the salesmen are tasked with optimising their routes in a three-dimensional space that includes container-loading constraints with multiple stacks. This problem extends beyond simple route optimisation and dives into the complexities of practical logistics operations where bulky items must be efficiently loaded into containers.  
The incorporation of container-loading constraints with multiple stacks introduces a set of challenges, including the need for intelligent loading strategies and considerationsfor the stability and blocking relationships of loaded items. To address these challenges, the authors propose the utilisation of the k-means clustering algorithm as a component of their Improved Genetic Algorithm (IGA) [10]. This algorithmic approach aims to enhance the optimisation of delivery and loading routes by leveraging clustering techniques to group related items together.  
In the context of the 3L-DTSPMS, the k-means clustering algorithm plays a pivotal role in several stages of the optimisation process. Initially, it contributes to the generation of an approximate optimal delivery route within the Genetic Algorithm framework. Subsequently, the algorithm aids in optimising the loading route, considering the independence of pickup warehouses and the potential blocking relationships between items in the loading and delivery routes. The k-means clustering algorithm, coupled with heuristics, facilitates the regeneration of the loading path to align it with the current delivery route.
# Vibration Response Characteristics of Prefabricated Frame Structures Around the Subway
## 2 Modeling Method and Verification of Prefabricated Frame Structures with Semirigid Joints
### Verification of the Model  
To validate the rationality of the modeling method for prefabricated concrete frame structures in this study, based on parameters from [5] establishing the prefabricated frame structure model, and considering that superstructure responses under subway vibrations generally remain elastic, only simulated vibration frequencies were compared with experimental measurements. A \(1/5\)-scale experimental specimen was constructed as a three-story, three-bay precast RC frame consisting of inverted L-shaped edge beams, inverted T-shaped middle beams, bracket columns, and prefabricated slab components. Slab-to-beam connections employed bolted joints through slab ribs, with beam-column connections similarly bolted and subsequently grouted with concrete to fill joints. The structural configuration is illustrated in Figure 2a.  
A uniform mesh size of 0.05 m was adopted, employing B31 elements for beam/column/rib members and S4R shell elements for slabs. To account for slab-rib interaction, binding constraints were implemented between components. The finite element model configuration is illustrated in Figure 2b,\(c\). Comparative results of simulat  
Figure 1: Simplified mechanical model of prefabricated joints.  
Figure 2: Schematic diagrams of the test and numerical models: (**a**) test model; (**b**) numerical model; (**c**) spring model joints.  
shapes (vibration directions, frequencies, and errors) are presented in Table 1. The maximum frequency error of 3.8% demonstrated the modeling method's satisfactory accuracy for semirigid prefabricated RC frames, validating its applicability for subway vibration analysis within elastic ranges.
## 3 Results and Discussion
### Optimization of Input Variables and Validation of Results  
In this study, the optimal combinations of input variables viz. acetone %, time, LSR, and the number of extraction cycles have been determined by using the RSM-based desirability approach. This approach has been implemented to maximize the reduction of free gossypol and total gossypol. The desirability function (di) was formed by each response variable, as shown in Table 6. Its value must lie between 0 and 1. The desirability function is said to 0 when the response variable is completely unacceptable, whereas 1 represents that the response variable is acceptable. In this technique, the best desirability solutions were preferred among different obtained solutions. In this case, the optimal parameters were determined by using State-Ease Design Expert Software version 7.0. For optimization of the best levels of parameters, the minimum and maximum limits are shown in Table 6.  
As shown in Table 7, the optimized values for FGR and TGR were found at 86.38% acetone, time of extraction 30 min, LSR 10, and no. of extraction cycles 4. To validate optimized results, the experiments were performed thrice, and the average of three actual experiment responses was calculated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Goals** & **Minimum Limits** & **Maximum Limits** & **Desirability (di)** \\ \hline Acetone \% & In range & 60 & 100 & 1 \\ Time of extraction (min) & In range & 15 & 75 & 1 \\ LSR & In range & 2.5 & 12.5 & 1 \\ No. of extraction cycle & In range & 1 & 5 & 1 \\ Free gossypol reduction (\%) & Maximize & 59 & 99.5 & 0.995 \\ Total gossypol reduction (\%) & Maximize & 24 & 90 & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Response optimization of the input parameters based on desirability.  
Figure 10: (a) 3D surface plot no. of extraction cycles and time of extraction (b) 3D surface plot between no. of extraction cycles and LSR.  
The error between experimental results and prediction results were recorded and found very close to each other, as shown in Table 7. The validation results show that the developed model (RSM) for predicting free gossypol and total gossypol is accurate.
## 4 Experiments and Results
### Model Evaluation Metrics  
The evaluation metrics for the model primarily focus on classification accuracy across various tasks. The model's performance on these tasks is evaluated by calculating the average classification accuracy across all examples and tasks. The corresponding performance evaluation metrics include classification accuracy (which quantifies the proportion of correct predictions made by the model across all tasks) and calibration error (which measures the discrepancy between the model's predicted confidence and the actual accuracy).  
The evaluation metrics for the MMLU and C-eval dataset can be calculated using the following steps:  
\(D\) is the number of all disciplines; for each discipline \(d\in D\), \(Q_{d}\) is the set of questions for that discipline and \(|Q_{d}|\) is the number of questions; for each question \(q\in Q_{d}\), \(A_{q}\) is the correct answer and \(\overline{A_{q}}\) is the result predicted by the model.  
1. For each discipline \(d\), calculate the accuracy for that discipline \(A_{d}\) \[A_{d}=\frac{1}{|Q_{d}|}{\sum_{q\in Q_{d}}{\delta\big{(}\overline{A_{q}}\,=\,A_ {q}\big{)}}},\] (13) where \(\delta\big{(}\overline{A_{q}}=A_{q}\big{)}\) is 1 when \(\overline{A_{q}}=A_{q}\) otherwise 0.
2. Calculate the average accuracy across all disciplines A \[A=\frac{1}{|D|}{\sum_{d\in D}{A_{d}}}.\] (14)
3. Calculate weighted average accuracy \(A_{w}\) across all disciplines: \[A_{w}=\frac{\sum_{d\in D}{|Q_{d}|}{\cdot}A_{d}}{\sum_{d\in D}{|Q_{d}|}}\] (15)  
Evaluation metrics on NER tasks are calculated from the description in Section 3.2.
# The Importance of Modeling Path Choice Behavior in the Vehicle Routing Problem
## 2 Transport System Analysis
### Objectives and Constraints  
In a DSS, all components interact with each other, even with circular dependencies. From an operational point of view, the inputs are the objective and the constraints, and the outputs are the actions to be applied on the supply and demand transport subsystems.  
(Objectives) The VRP requires the definition of objectives to be achieved in some conflicting cases (some examples could be the minimum cost and time for operators and the maximum pedestrian areas for citizens in urban areas or the maximum revenue for operators and the lowest price for end users). The VRP, considering all the components involved, must be studied with a multicriteria approach considering different types of criteria (even mixed) in different areas:  
* (i.) Transport, (ii.) information and communication and (iii.) energy when considering SC;
* (a.) DSS and (b.) ICT when considering ITS;
* (I.) Economic, (II.) environmental and (III.) social criteria when considering sustainability;
* (A.) Decision-takers, (B.) analysts and (C.) users when considering the involved actors.  
Figure 2: Functional architecture in a decision support system.  
(Constraints) Constraints can be classified into external, technical and behavioral. The external constraints represent the quantities that must be respected and imposed for carrying out the activity, i.e., restrictions on circulation in limited traffic areas, regulatory constraints on the characteristics of vehicles that can circulate, time windows allowed for the pick-up and delivery of freight. The technical constraints represent the quantities that must be respected and internal to the problem, i.e., the number and type of vehicles available for the VRP, the contractual characteristics, the quantity of freight that must be picked up and/or delivered to each node. The behavioral constraints represent the choices of the system manager or the driver (pretrip and en route) during the journey between two nodes of the route, and they are conditioned by the state of the transport system. The first two constraints can almost always be linear constraints; the third constraint is always of a nonlinear type.
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 4 Results and Discussion
### Simulation Results  
#### 4.2.1 Time Delay Estimation Performance  
Figure 9 shows the time delay estimation performance of MCC-ETDE algorithm when the primary input has no communication signal. Therefore, all the input signals are the correlated impulsive noises. The time delay between the primary input and reference input is 10 \(T_{s}\). The Gaussian kernel size \(\sigma\) is 0.5 and the positive scalar \(\mu\) is 0.4. The MCC-ETDE algorithm can converge to the true time delay whin 1000 times of iterations (1 ms) and is suitable for real-time communication. When the pulses occur, the MCC-ETDE algorithm has bigger errors but will come back to the steady state very soon.  
Figure 8: Adaptive noise cancellation system.  
Figure 10 shows the time delay estimation performance of MCC-ETDE algorithm when the mixed SNR in the primary input is \(-23\) dB. When MCC-ETDE algorithm estimates the time delay between the noise of the primary input and that of the reference input, the communication signal becomes an interference signal. As shown in Figure 10, the communication signal has no major impact on the convergence speed but causes big steady-state error, which affects the noise cancellation performance.  
Figure 11 shows the time delay estimation variance of MCC-ETDE algorithm at different mixed SNR levels. The time delay estimation variance of MCC-ETDE algorithm increases when the mixed SNR increases. Therefore, in the communication process, the MCC-ETDE algorithm may not converge to the steady state because of the communication signal. We should obtain the time delay and relationship between the primary input and reference input among communication gap.  
Figure 10: Time delay estimation performance when mixed SNR (Signal-to-Noise Ratio) in the primary input is \(-23\) dB.  
Figure 9: Time delay estimation performance without communication signal.