esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 1 Introduction
* How is VNS commonly hybridized with other optimization methods to tackle complex logistics challenges, and which hybridization approaches have proven to be the most effective? * How are multiobjective logistics problems, which often involve trade-offs between different sustainability aspects, addressed when employing VNS? * Which aspects of sustainability are most frequently considered when applying VNS to solve logistics problems, and how are these aspects integrated into the optimization process? By answering these questions, this study will contribute to the growing body of knowledge regarding the use of VNS in sustainable logistics, integrating economic, environmental, and social dimensions of sustainability. Unlike existing reviews that primarily focus on specific VNS variants or isolated applications, our work takes a holistic approach, identifying trends, challenges, and opportunities across multiple facets of sustainable logistics, such as routing, scheduling, and supply chain design. The findings will provide valuable insights into the application of VNS in various logistics challenges and how it can be leveraged to enhance sustainability in logistics operations. Additionally, we highlight the growing importance of adaptive and hybrid VNS methods in addressing the dynamic and multiobjective nature of logistics problems. By systematically categorizing and analyzingthe literature, this study not only offers new insights into the current state of VNS research but also identifies critical gaps and provides actionable recommendations for future work. The remainder of this article is structured as follows. Section 2 provides a detailed description of the VNS methodology, including its key versions and extensions. Section 3 outlines the review methodology used in this study, detailing the steps taken to identify the relevant literature and analyze the results. Section 4 presents the findings of the literature review, focusing on the main applications, hybridizations, and sustainability considerations associated with VNS in sustainable logistics. Section 5 presents a discussion highlighting the research trends, challenges, and gaps identified in the reviewed literature and offers directions for future research. Section 6 presents a discussion on advantages, limitations, and decisions when implementing VNS for sustainable logistics. Finally, Section 7 concludes the article with a summary of key insights and their implications for both research and practice in sustainable logistics optimization.
# Computational Modeling of U-Shaped Seismic Dampers for Structural Damage Mitigation
## 2 Materials and Methods
### Model Selection  
The key geometric parameters of the U-shaped metallic damper include the projected lengths of the straight and curved sections, the radius of the curved section, the width, and the thickness (Figure 1a,b). The damper operates by moving the upper and lower straight bolted clamped sections in opposing directions, transitioning from elastic deformation to plastic deformation under cyclic loading. The mode of deformation is bending dominated by two plastic hinges, which enables the dissipation of the structure's kinetic energy during a seismic event. Since Nippon Steel proposed several types of USSD, UD-40 (Figure 1a) was selected as the model for experimentation because of its smaller size than the other models. For example, the displacement until failure is lower than that of UD50 [19]. The UD-40 damper differs from the other dampers mainly in its geometrical characteristics, with a greater thickness and both lower and variable widths (Figure 1b).  
Figure 1: (**a**) UD-40 model with boundary conditions applied. (**b**) Upper and front sight of UD-40 USSD with dimensions in mm. (**c**) Load protocol applied to the damper. (**d**) True stress–strain curve from experimental data reported for JIS-SM490 fitted with the bilinear hardening model.
## 2 Construction Method of EMR-SF along the Road Collection Data
### The Layered Grid Points in the LRBF  
In the LRBF method, the layered interpolation after layering the grid points enables the EMR sample point data to interact with each other. This ensures that the EMR intensities of points close to each other are similar. Usually, similar points will show aggregation in space. If the points are aggregated to a high degree in a certain distance scale range, it means that there are many aggregated points. A distance scale that makes the maximum aggregation degree is found, based on which the stratification of the grid points can ensure that more similar points are aggregated and improve the interpolation accuracy of LRBF.  
[PERSON]'s K function counts the number of points according to the search distance range of a certain radius, which is a point density distance function [26]. It can obtain the degree of aggregation of spatial data at different distance scales. The distance range corresponding to the maximum degree of aggregation of the sampled data can be obtained by using this function. This distance range is used as the stratified interval distance of LRBF. [PERSON]'s K function is defined as:  
\[K(d)=A\sum_{i=1}^{n}\sum_{j=1}^{n}\frac{I(d)}{n(n-1)} \tag{4}\]  
\[I(d)=\begin{cases}1,\text{ }if\text{ }r_{ij}<d\\ 0,\text{ }if\text{ }r_{ij}\geq d\end{cases} \tag{5}\]  
where \(d\) is the distance scale, \(A\) is the area of the study area, and \(n\) is the number of EMR sampling points. \(I\) is the number of EMR sampling points in the range of the distance scale \(d\), and \(r_{ij}\) is the distance between the \(i\)th point and the \(j\)th point.  
\[L(d)=\sqrt{\frac{K(d)}{\pi}}-d \tag{6}\]  
Assuming that the sample points are completely spatially random distributions, the expectation of \(L(d)\) is 0. When \(L(d)\) is less than 0, the EMR sampling data are discretely distributed. When \(L(d)\) is greater than 0, the EMR sampling data have an aggregated distribution. The distance scale \(d\) corresponds to the largest value of \(L(d)\) as the maximum aggregation distance, which represents the maximum spatial aggregation intensity of the EMR sampling data.  
The maximum aggregation distance \(d\) is taken as the layered interval distance of the LRBF. The boundary line of the EMR sampling data is taken as the standard, and the interval distance H is used to gradually divide the grid points into multiple layers inward (Figure 5).  
Figure 4: The influence range of the Gaussian basis function: (**a**) the 3D Gaussian basis function; (**b**) 3D top view of the Gaussian basis function.
## 4 Comparative Evaluation of Magnetic Gear Core Materials
### Technical Specifications and Numerical Comparisons  
#### 4.2.1 Material Properties  
In the literature, the performance of magnetic gear systems employing different pole piece materials has been compared, and it has been demonstrated that material selection has a direct impact on torque density, particularly in transverse flux magnetic gears. Studies have shown that soft magnetic composite (SMC) materials, due to their isotropic magnetic structure and high electrical resistivity, enable more-effective 3D magnetic flux guidance in transverse flux magnetic gears. As a result, low-loss and compact designs with higher torque density can be achieved [78].  
Scores for manufacturability and cost are rated on a scale from 0 to 100, where 100 represents the most favorable (easiest to manufacture or most economical) and 0 the least favorable.  
The technical specifications related to the performance of different magnetic core materials are summarized in Table 3. In this table, the fundamental parameters of electrical steel (M400 50A) (ThyssenKrupp AG, Essen, Germany), soft magnetic composite (Somaloy 700 HR 5P) (Hoganas, Sweden) and cobalt-based steel (Vacodur 50A) (Vacuumschmelze GmbH & Co. KG, Hanau, Germany) are systematically compared. The material data were compiled based on the literature and experimental sources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Electrical Steel** & **Iron Powder** & **Cobalt Steel** \\  & **(M400 50A)** & **(Somaloy 700 HR SP)** & **(Vacodur 50A)** \\ \hline
**Saturation Flux Density (T)** & 2.10 & 2.00 & 2.35 \\
**Relative Permeability (\(\mu\)m)** & 3000 & 600 & 10,000 \\
**Core Loss (50 Hz, W/kg)** & 5.5 & 18.0 & 7.5 \\
**Electrical Conductivity (S/m)** & \(1.96\times 10^{6}\) & \(1.66\times 10^{3}\) & \(2.38\times 10^{7}\) \\
**Thermal Conductivity (W/m:K)** & 28 & 21 & 32 \\
**Density (kg/m\({}^{3}\))** & 7650 & 7500 & 8120 \\
**Manufacturability (0–100)** & 59 & 22 & 36 \\
**Cost Score (0–100)** & 100 & 65 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measurable technical properties of the compared materials [79].
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
###### Abstract  
Trains used in urban mass passenger transit often have side entrance doors through which passengers can rapidly enter and exit the train. These doors are typically electrically powered and automated. Many incidents have occurred in which a passenger is trapped and injured while passing through the doors as they are closing. Existing solutions rely on sensitive-edge sensors and current signal peak detection in the time domain to detect door obstructions. However, these methods have notable limitations: sensors are expensive, and sensor failure can result in safety risks, while time-domain signal analysis is prone to noise, potentially leading to false peak detection. The proposed efficient and cost-effective method enhances safety by implementing the torque control of a DC motor which limits the door closing force to prevent potential injuries. In addition, it reduces reliance on traditional edge sensors, which are prone to failure and may result in undetected obstructions. By using a robust time-frequency domain approach, the system ensures more accurate detection, minimizing potential injury risks. An obstruction of the door causes a corresponding change in the motor current. These changes can be detected by using the discrete wavelet transform to decompose the current signal. The norm and peak of the current are used as obstacle detection features, and appropriate threshold values are obtained from a simulation. The simulation results were validated through an experiment. The proposed novel system effectively detects forces between 100 and 200 N (indicating the presence of an object) within 0.3 s and complies with EN14752 safety standard. It can also differentiate between soft and hard objects trapped in train doors.  
current sensor; obstacle detection; train door; wavelet transform +
Footnote †: journal: Applied sciences  
18 November 2024  
Revised: 20 December 2024  
Accepted: 21 December 2024  
Published: 24 December 2024
## 2 Materials and Methods  
### Chemicals and Reagents  
Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid), 2,2-azinobis-(3-ethylbenzothazoline-6-sulfonic acid)-diammonium salt (ABTS), 2,4,6-tripyridyl-s-triazine (TPTZ), p-dimethylaminocinnamaldehyde (DMAC), Folin-Ciocalteu reagent, phenolic standards (catechin, epicatechin, quercetin, gallic acid, vanillic acid, protocatechuic acid, syringe acid, \(p\)-coumaric acid, ferulic acid, caffeic acid, _trans_-sinapic acid, Phloridzin) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Other high purity analytical phenolic standards (procyanidin B1, B2 and A2, cyanidin 3-galactoside, cyanidin 3-glucoside, cyanidin 3-arabinoside, peonidin 3-glucose, myricetin-3-galactoside, myricetin-3-glucose, quercetin-3-galactoside, and quercetin-3-rhamoside) were from Extrasynthese (Genay, France). HPLC grade chemicals, e.g., formic acid, were from BDH Chemical Ltd., Co. (Poole, England) and acetonitrile from Romil Ltd., Co. (Cambridge, England). Potassium metabosulfite (PMS) was from Enartis (Trecate, Italy). All other chemicals used were of analytical grade. Type 1 water (Barnstead NANOpure(r) Diamond(tm), Thermo Scientific, Waltham, MA, USA) was used in experiments.  
For GC-MS, deuterated internal standards of high purity, including d\({}_{3}\)-ethyl butyrate, d\({}_{11}\)-ethyl hexanoate, d\({}_{15}\)-ethyl octanoate, d\({}_{3}\)-3-methylbutyl acetate, d\({}_{3}\)-n-hexyl acetate, d\({}_{3}\)-2-phenylethyll acetate, d\({}_{3}\)-linalool, d\({}_{3}\)-\(\alpha\)-Terpineol, d\({}_{2}\)-3-methyl-1-butyl alcohol, d\({}_{11}\)-n-hexyl-2,2,3,3,4,5,5,6,6-alcohol, d\({}_{5}\)-2-phenyl alcohol, d\({}_{12}\)-n-hexanol, d\({}_{11}\)-hexanoic acid were from CDN Isotopes (Pointe-Claire, Canada). High purity non-deuterated internal standards: DL-3-octanol was from Acros Organics (Geel, Belgium), 4-decanol from Lancaster (Morecambe, England), and 3-4, dimethylphenol from Aldrich (Castle Hill, Australia). Analytical volatile standards ethyl acetate, pentanal, methyl butyrate, ethyl methylbutyrate, ethyl 2-methylbutanoate, methyl hexanoate, 3-octanone, D-Limonene, eucalypt, methyl benzoate, methylbutyric acid, ethyl octanoate, ethyl benzoate, \(\beta\)-phenylethyl acetate, \(\beta\)-damascenone, 4-ethylguaiacol, ethyl cinnamate (_trans_), benzoic acid, and \(\beta\)-damascenone were purchased from Aldrich (Castle Hill, Australia). Other volatile standards were purchased from different suppliers as follow: benzyl alcohol from Riedel-de Haen (Lotte, Germany); isobutanol from Scharlau (Barcelona, Spain); isoamyl alcohol from Panreac (Barcelona, Spain); isoamyl acetate from Univar (Downers Grove, IL, USA); 4-ethylphenol from Lancaster (Morecambe, England); phenylethyl alcohol, ethyl butyrate, octanoic acid, decanoic acid, \(\alpha\)-terpineol, geraniol (trans), from Acros Organics (Geel, Belgium); ethyl hexanoate, ethyl decanoate, isobutyl acetate, hexanoic acid, from Fluka (Castle Hill, Australia).
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism  
In this section, the overall system architecture of the proposed status-byte-assisted RDMA transmission system is first described. Then, the status-byte-assisted transmission mechanism is illustrated in detail, covering the processes involved in data filling, sending, receiving, synchronization, and processing. Next, this section details the specificity of the status-byte-assisted RDMA transmission mechanism, which offers fine-grained control over memory blocks. Finally, it explains the multi-device shared QP strategy to efficiently handle data streams from multiple devices.
# Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network
## 4 Experiment
### Experimental Result  
To test our neural network model, DEAP dataset was used for emotion recognition. Table 1 compares the proposed 1D CNN model with previous representative models using the DEAP dataset. We have 75.3% classification accuracy for valence and 76.2% accuracy for arousal. Also, we show the time values of recognition determine how fast the emotion recognition is possible because we have tried to perceive emotions at short intervals. The proposed model was able to provide short term emotion recognition, successfully detecting human emotions in 1.1 s.  
This study proposed a short term emotion recognition method based on single PPG pulses. It is important to achieve high accuracy in a short period, hence we split the raw PPG signal into a single pulse that was input to a 1D CNN to extract features and classify emotions. The 1D CNN output was a binary classification indicating high or low arousal and valence.  
In Table 1, various deep learning models, including DBN, CNN, auto-encoding, and CapsNet have replaced shallow methods, providing superior performance because deep learning can automatically extract features containing information that hand-crafted (statistical) features cannot access. In particular, the proposed 1D CNN model was superior to derive emotion related features from 1D biological signals.  
The proposed 1D CNN method achieved short term emotion recognition, providing acceptably high accuracy compared with other methods for both arousal and valence, but only requiring 1.1 s which indicates the shortest interval of recognition. In particular, the proposed 1D CNN was more accurate and significantly faster than the latest EEG signal using CapsNet.  
PPG signals include considerable information regarding emotions and can be easily measured using simple wearable devices, and the raw PPG signal is sufficient to represent emotions.  
Consequently, fast emotion recognition could be implemented using the proposed 1D CNN architecture for single pulse PPG signals.
# Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
## 6 Implementation and Evaluation
### Functional Evaluation
#### 6.3.2 RESTlets  
RESTlets are application building blocks that may be defined once on devices and that can be instantiated a number of times to build (part of) IoT applications by interconnecting them with devices and each other using flexible bindings. This process can also be accomplished using any device connected to the Internet. To illustrate this, we consider the example of a simple LESS-THAN RESTlet in order to send notifications to the actuator shown above in case the temperature drops below 25 degrees. Figure 14 shows the Copper screenshot of this operation. Sending a POST request to the /r resource of the node selected to host the RESTlet, in this case [aaaa:1], creates the resource and its sub-resources. The payload shows the name of the RESTlet, LT indicating less than, which has one data input and one control input. The control input is initialized to 25. The default number of outputs is 1. Upon reception of the POST request the RESTlet is being instantiated and further referenced as /r/0. In addition, 3 sub-resources identified as /r/0/con/0, /r/0/in/0 and /r/0/out/0 are created representing the control input, data input and output, respectively.  
To achieve the desired result, the input /r/0/in/0 is bound to the sensor node and the output /r/0/out/0 is bound to the actuator. This way, sensor readings are being transmitted to the RESTlet and the output of the RESTlet triggers the actuator. For this, the binding functionality shown in the previous sub-section is applied twice. Subsequent updates of the control parameter can be easily performed by sending a PUT request to the /r/0/con/0 resource.  
This solution can be used to build complex IoT applications by distributing the RESTlets at different devices inside the LLN, at the LLN Gateway or even in the cloud. Irrespective of the complexity of the application or the location of the RESTlet nodes, we send a series of CoAP requests to the devices to program the application. To simplify the development even further, we can employ visual programming tools to simply drag-and-drop components to instantiate RESTlets and perform the binding.
# Analyzing Momentum Shifts in Tennis: A Machine-Learning Approach to Predicting Match Outcomes
## 3 Results
### The Normal Test of the Variation of Difference of Momentum Between the Two Players  
We introduce a new concept, \"the variation of difference in momentum between the two players\", denoted as \(\Delta{\it{n}}\). To evaluate its statistical properties, basic tests were conducted. For smaller datasets (typically fewer than 5000 samples), the Shapiro-Wilk test was applied, while the Kolmogorov-Smirnov test was utilized for larger datasets (generally exceeding 5000 samples) to assess the significance of \(\Delta{\it{n}}\).  
Table 8 presents the results of descriptive statistics and normality tests for the variation in momentum difference \(\Delta{\it{n}}\), including key measures such as the mean, median, standard deviation, skewness, and kurtosis. Based on the 7284 samples analyzed, the mean value is 0.087, while the median is \(-\)0.048, indicating a central tendency close to zero. The standard deviation is 12.288, reflecting moderate variability in the data. The distribution shows slight asymmetry, with a skewness of 0.143, but the kurtosis is markedly high at 20.152, suggesting a sharper peak compared to a normal distribution. The Shapiro-Wilk test yielded a statistic of 0.846 and a p-value of 0.000, rejecting the null hypothesis of normality at the 1% significance level. Similarly, the Kolmogorov-Smirnov test produced a statistic of 0.1, exceeding the critical value of 0.05, further supporting the rejection of normality. These results indicate that \(\Delta{\it{n}}\) does not follow a normal distribution, warranting further analysis and discussion.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Variation**} & **Sample** & \multirow{2}{*}{**Median**} & \multirow{2}{*}{**Mean**} & **Standard** & \multirow{2}{*}{**Skewness**} & \multirow{2}{*}{**Kurtosis**} & \multirow{2}{*}{**S-W Test**} & \multirow{2}{*}{**K-S Test**} \\  & **Size** & & & & & & & \\ \hline \(\Delta{\it{n}}\) & 7284 & \(-\)0.048 & 0.087 & 12.288 & 0.143 & 20.152 & 0.846 (0.000 *) & 0.1 \\ \hline \end{tabular}
\end{table}
Table 8: Statistical Tests for the Variation of Difference in Momentum (1%) under Different Sample Sizes.  
Figure 2: Momentum contrast of [PERSON] and [PERSON].  
The process of the normality test is visualized in the Quantile-Quantile (Q-Q) plot shown in Figure 3, which illustrates the distribution of \(\Delta\dot{m}\). The plot reveals that the sample distribution aligns closely with the normal distribution near the mean (0). However, as the values deviate significantly from the mean (absolute values \(\geq 3\)), noticeable discrepancies from the standard normal distribution emerge. This observation is consistent with the results of the Shapiro-Wilk and Kolmogorov-Smirnov tests, which suggest that the sample distribution is sharper than a standard normal distribution. Consequently, the true distribution of the variations in momentum difference requires further investigation.
## 3 System Design and the Proposed SEAC-Based Resource Allocation Methodology
### Network State Representation
#### 3.1.2 Forecast Indicator of the Possibility of an Object (_I-sf_)  
This indicator reflects the likelihood of aperiodic traffic within the vehicle's sensor field of view (FOV). Derived from Cooperative Perception Messages (CPMs), it helps in predicting potential traffic events and adjusting resource allocation.
## 4 Discussion
### Limitations and Future Discussion  
While this study provides valuable insights, it has several limitations. First, due to its cross-sectional design, it cannot account for training effects or physiological changes over time. Second, the positional roles of players were not analyzed, although these may significantly affect VO2 max and lactate-related parameters. Third, the study was conducted mid-season (March 2022), which may have influenced physiological status due to varying training loads and match exposure.  
Future studies should include a sample of players from a larger number of teams and adopt longitudinal and positional specific designs, potentially integrating real-time tracking (GPS, inertial sensors) and match analytics to correlate laboratory findings with in-game performance. In our opinion, a multi-dimensional approach that includes psychological, tactical, and biomechanical data would further refine our understanding of performance determinants in professional football. Also, future research could incorporate lower-tier teams and explore the differences among top-, mid-, and lower-tier teams, which would provide a more comprehensive understanding of potential variations.  
It is worth noting that, to date, only one other study has examined physiological predictors of match performance in Serbian elite football players [33], focusing on predictive modeling rather than group-based comparisons. This highlights the novelty and applied relevance of the present comparative approach.