esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research on the Influence of Track Structure Stiffness Change on Track Deformation
### Vertical Stiffness under Block  
According to the calculation results of the simulation conditions of different vertical stiffness under block, the maximum values of the vertical displacement of the rail, the lateral displacement of the rail, and the gauge expansion are summarized in Table 10, the changes are shown in Figure 12.  
According to Table 10, when the vertical support stiffness under the block increases from 40 kN/mm to 160 kN/mm, the vertical displacement of the rail shows a decreasing trend, and the trend gradually slows down. When the vertical support stiffness under the block increases to a certain value, the vertical displacement of the rail is almost no longer reduced; the ranges of vertical displacement, lateral displacement and gauge expansion of the rail are 1.127\(\sim\)2.268 mm, 0.908\(\sim\)0.993 mm, 1.603\(\sim\)1.745 mm, and the relative change rates are 101.13%, 9.29%, 8.86%, respectively; the lateral and vertical displacement of the rail and the amplitude of the gauge expansion are all within the specified limits. When the vertical stiffness under the block is 40 kN/mm, the vertical displacement of the rail is close to the limit.  
According to the analysis in Figure 12, the vertical displacement of the rail gradually decreases with the stiffness of the vertical support under the block, and the lateral displacement and gauge expansion show a certain randomness, but the overall trend is increasing. In order to maintain the straightness of the vertical position of the track structure, the deformation of the rubber boots at the bottom of the support block are reduced, the safe service life of the rubber boots is extended, and the stiffness of the vertical support under the block is appropriately increased.
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 4 Same Cluster Queries
### An Algorithm Using Few SCQs  
We next describe a simple algorithm which uses much less SCQs, though a comparable number of high-low cost operations. The algorithm works for certain instances, and its performance is summarized in the following theorem.  
**Theorem 5**.: _For any \(\gamma\geq 2\) and for any positive integer \(n_{1}\), there exists an algorithm with the following performance guarantees. The algorithm computes the optimal \(k\)-clustering, with a probability of at least \(1-\frac{3}{n}\), on input any \(\gamma\)-perturbation-stable metric space \((X,d)\) which has \(\gamma\)-radius optimal clusters, and such that every cluster in the optimal solution has a size of at least \(n_{1}\). Moreover, the algorithm uses only the following number of exact same-cluster queries and high-low cost operations parameterized in \(p_{1}=\frac{n_{1}}{n}\):_  
1. _The algorithm uses_ \(O(\frac{k\log k}{p_{1}})\) _exact same-cluster queries, which is independent of_ \(n\) _for approximately balanced clusters, that is,_ \(n_{1}=\Theta(n/k)\)The additional high-low cost operations used by the algorithm is  
\[O\bigg{\langle}\frac{(\log n)^{2}}{p_{1}},(\frac{\log k}{p_{1}}+n)k\log n\bigg{\rangle}\.\]
# Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model
## 8 Mitigation of Carbon Emissions by Building Offshore Wind Farms  
The integration of renewable energies brings challenges on power system planning and operation. The variability of renewable power generation requires additional actions to balance the system. Those variable resources cause thermal power plants to turn on and off more often and thus increase the carbon emissions. Consequently, numerous advance techniques, such as accurate forecasts, fast dispatch, reserves management, demand response, and flexible generation sources, is necessary. A high penetration of renewable energy can mitigate carbon emissions [50]. Additionally, the future trend for developing multiple energy systems (MES) to improve energy efficiency and promote the integration of renewable energy in several areas is obvious. Research works in reference [51] demonstrated the carbon emission flow (CEF) in MES, emphasizing that carbon emission should be allocated along with the energy delivery and conversion. Reference [52] highlighted the importance of coordinating multiple energy systems in both district level and multi-regional level, and applied the CFD model to allocate the carbon emission. Similarly, several works [53; 54; 55] allocated the carbon footprint among generation resources, consumers, transmission loss and others to trace carbon emission. The above works reveal that the carbon-emission constraint would also have effects on the planning of offshore wind farms.
## 2 Methods
### Stamp/stpa  
STAMP is an accident causality model grounded in systems theory, created to analyze modern complex systems [31]. Unlike traditional models that view accidents as chains of component failure events, STAMP treats safety as an emergent property arising from the interactions among system components [39]. In STAMP, accidents occur when component interactions violate the system safety constraints. The key principles of STAMP are [31]:  
* Safety constraints define the boundaries of safe system behavior. Accidents result from inadequate control and violation of these constraints.
* Complex systems are viewed as hierarchical control structures, where each level enforces safety constraints on the level below it.
* Control loops consisting of controllers, actuators, sensors, and controlled processes are the building blocks of the hierarchical control structure.
* Controllers must have accurate process models reflecting the state of the controlled processes. Flawed process models and inadequate feedback can lead to accidents.
* Continuous adaptation through feedback is necessary to cope with changes and maintain safety.  
From the STAMP perspective, the goal of safety management is to control the behavior of system components to ensure that safety constraints are upheld. This approach treats safety not merely as a component reliability problem but as a matter of controlling the overall system behavior.  
By adopting a systems theoretical perspective, STAMP enables a more comprehensive analysis of hazards in complex socio-technical systems by considering both component failures and dysfunctional interactions. Consequently, in this study, STAMP is used as a basic framework for gaining a broader perspective on the safety of complex systems and for systematic hazard identification.  
STPA is a hazard analysis technique based on STAMP. It offers a step-by-step approach to identifying and addressing potential hazards in complex systems by examining the control structure and the enforcement of safety constraints. The main steps of STPA are (Figure 1) [40]:  
This structured approach enables a comprehensive analysis of system safety, from defining the analysis goals to identifying specific hazards and understanding the underlying causes of unsafe control actions. Through these steps, the STPA method systematically uncovers and addresses potential safety issues in complex systems, guiding the development of safer system designs and operations.  
Figure 1: General process of STPA.
## 1 Introduction
Furthermore, this opens new applications in emerging fields such as Internet of Things (IoT) [44; 43], brain-computer interfaces (BCI) [45], artificial intelligences [46], augmented/virtual reality environments, medical settings and completely different fields such as neuro-economics and neuro-marketing or customer satisfaction evaluation [47; 48]. In this review we focus, in particular, on the latest advancements and perspectives of the use of TG detection for TD-DO, applied mainly in, but not limited to, biophotonics. In Section 2, the main physical principles underlining photon diffusion in a diffusive medium and the time-gating approach will be discussed. In Section 3, the hardware for accomplishing the TG modality will be presented, focusing mainly on SPADs. In Section 4, few hints on the main applications and future perspectives of TG methods for diffuse optics in biophotonics and other fields will be addressed.
###### Abstract  
In order to cope with data with an irregular shape and uneven density, this paper proposes a two-phase clustering algorithm based on detecting the peaks of dimensional density and the degree of interaction between clusters (CPDD-ID). In the partitioning phase, the local densities of the data in all dimensions are calculated using kernel density estimation, the density curves are constructed based on the densities of all the data, and the peaks of the density curves are used as the benchmark to construct a Kd-Tree to search for the data points that are closest to each peak to partition the initial sub-clusters. Then, the intersection of the results of the initial sub-clusters obtained from all the dimensions is taken to obtain the final sub-clusters. The proposed partitioning strategy is able to accurately identify clusters with density differences and has significant effects in dealing with data with irregular shapes as well as uneven densities in this category. In addition, a new similarity measure based on the interaction degree between clusters is proposed in the merging stage. This method iteratively merges subclusters with maximum similarity by calculating the interaction degree of shared k-nearest neighbors between neighboring subclusters. The proposed similarity measure is effective in dealing with the problems of high overlap between clusters and ambiguous boundaries. The proposed algorithm is tested in detail on 10 synthetic datasets and 10 UCI real datasets and compared with existing state-of-the-art algorithms. The experimental results show that the CPDD-ID algorithm accurately identifies potential cluster structures and exhibits excellent performance in terms of both clustering accuracy.  
clustering; density peak; intersection; shared neighbor; similarity metric +
Footnote †: journal: Applied sciences
## 2 Basic Theory
### Skeleton Simulation in Multi-Light-Source Scenarios
#### 2.1.2 Silhouette Information Extraction
The auxiliary anchor points are marked with star symbols in Figure 2a. * The sub-curve between two feet joints contains the position of hip center \(Sp^{hip}\) at the local nadir position. * The sub-curves between the head position and two feet positions contain positions of two oxters at local nadir positions, respectively. (3) Localization of Remaining Major Joint Positions  
Based on the three global peaks and three auxiliary anchor points, the shadow contour is subdivided into six new sub-curves. To illustrate the extraction of remaining joint positions clearly, six sub-curves are marked as \(C_{sub}^{i}\). The indicated \(i\) ranges from 1 to 6 as shown in Figure 2a. \(C_{sub}^{1}\) to \(C_{sub}^{6}\) cover the shadow contour in a clockwise direction, initiating from the head position. * Sub-curves \(C_{sub}^{1}\) and \(C_{sub}^{6}\) cover the contour ranges of left arm and right arm. Thus the local peak positions of these two sub-curves are hand positions. Their local nadir positions are located between the cervical verteba position \(Sp_{neck}\) and two shoulders. * The local peak positions of \(C_{sub}^{2}\) and \(C_{sub}^{4}\) indicate the positions of two keens \(Sp^{keen_{left}}\) and \(Sp^{keen_{right}}\) in the shadow area. * Similarly, the nadir positions of \(C_{sub}^{3}\) and \(C_{sub}^{5}\) can assist the positioning of both keens \(Sp^{keen_{left}}\) and \(Sp^{keen_{right}}\). The major joint position localization procedure is illustrated in the three steps above and simplified in stage Equation (20). In order to simplify the subsequent presentations, function \(Loc\) is introduced to cover the localization procedure for major joint position set \(\left\{Sp^{joint}\right\}\) based on the human shadow contour \(Sh\) and the corresponding distance curve \(\overline{D}\). \[\left\{Sp^{joint}\right\}=Loc\left(Sh,\overline{D}\right) \tag{20}\]  
Figure 2: Silhouette information analyses and joint position extraction. (**a**) Sub-curve segmentation; (**b**) two-dimensional (2D) joint position extraction on the shadow area. Noticeably, the joint position localization procedure can also be adopted in the joint position extraction from a normal human pose contour. The human pose classification illustrated in Section 2.2.2 is based on the joint position extraction procedure illustrated in Equation (20).
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 1 Introduction  
### Context and Motivations  
Collaborative robots (cobots) are poised to revolutionize manufacturing by working alongside humans, interacting during shared tasks that combine human expertise with the precision and repeatability of robotic automation. Industry 5.0 emphasizes the integration of cobots into industrial workflows to increase productivity, safety, and operator well-being.  
The research work is carried out within the framework of the DYNAMICA project [1], which proposes novel robotized assembly paradigms for SMEs that aim to evaluate the impact of cobots on production and human energy expenditure, facilitate the teaching of new tasks, and define novel safety assessment strategies. This work focuses on the third objective and addresses the challenge of balancing human safety specifications with production requirements. While current state-of-the-art methods primarily focus on limitedaspects such as human detection or just robot speed scaling strategies (see Section 3), the proposed research introduces a comprehensive approach, which combines a robust and reactive human tracking module (see Section 4), an intention estimation algorithm (see Section 5), and a robot control module (see Section 6). The reactive perception setup consists of a few retro-reflective markers worn by each human operator who moves in the collaborative space, which are continuously identified by a reliable perception system. The speed scaling algorithm is based on real-time risk assessment and adapts the robot speed according to the actual behaviors of operators, so as to allow the robot speed to increase during low-risk situations while reducing it during more hazardous situations. To do that, the algorithm computes a minimum protective distance in real time, according to current safety regulations (see Section 2) and factoring in the prediction of the operator's intentions. The proposed approach ensures operator safety while optimizing production efficiency, aiming at improving industrial profitability in collaborative workcells. Furthermore, by gradually adjusting the robot's speed, the system also considers psychological factors, ensuring comfort for operators, whether working alone or with robots. Finally, robust experimental results demonstrate the applicability of the strategy, which improves the overall performance of the HRC task for industrial environments (see Section 7).
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Meaning** \\ \hline \(t\) & Current time \\ \(j\) & A job \\ \(u\) & A processor \\ \(r(j)\) or \(r_{j}\) & Release/arrival time of a job \(j\) \\ \(p(j)\) & Processing requirement (size) of a job \(j\) \\ \(m\) & Number of processors \\ \(lg_{u}\) & On a processor \(u\), the count of lagging jobs, at time \(t\) \\ \(\eta\) & Maximum speed of a processor using Opt \\ \(p\) & Power of a processor at speed \(s\) \\ \(s(t)\) or \(s\) & At time \(t\), speed of some processor \\ \(\alpha\) & A constant, commonly believed that its value is 2 or 3 \\ \(\Delta\) & A constant, its value depends on the value of \(\alpha\) \\ \(l\), \(S\) & A set of jobs and their schedule, respectively \\ \(p\)uk\((j,t)\), \(p\)uk\((j,t)\) and \(p\)uk\((j,t)\) & Remaining/pending work of a job \(j\) at time \(t\), using MBS and Opt, respectively \\ \(\tilde{F}(j)\) & Flow time of a job \(j\) \\ \(imp_{u}(t)\) or \(imp_{u}(t)\) & Importance/weight of a job \(j\), at time \(t\) on a processor \(u\) \\ \(imp_{u}(t)\) or \(imp_{u}\) and \(imp_{u}(t)\) or \(imp_{u}\) & Importance of all active jobs using MBS and Opt at time \(t\) on a processor \(u\), respectively \\ \(imp_{2}(t)\) or \(imp_{3}\) and \(imp_{3}(t)\) or \(imp_{3}\) & Total importance of lagging jobs, at time \(t\) on all \(m\) processors and on a processor \(u\), respectively \\ \(n_{u}(t)\) or \(n_{u}\) and \(n_{u}(t)\) or \(n_{u}\) & Total number of active jobs (NoAI) in MBS and Opt at time \(t\) on all \(m\) processors, respectively \\ \(n_{u}(t)\) or \(n_{u}\) and \(n_{u}(t)\) or \(n_{u}\) & NoAI in MBS and Opt at time \(t\) on a processor \(u\), respectively \\ \(s_{u}(t)\) or \(s_{u}\) and \(s_{u}(t)\) or \(s_{u}\) & Speed of a processor \(u\) for MBS and Opt at time \(t\), respectively \\ \(imp(t)\) & Total importance of all active jobs \(n_{u}\), at time \(t\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations used. The organization of the paper is as follows. In Section 2, some related non-clairvoyant algorithms are explained and their competitive values are compared to the proposed algorithm MBS. Section 3 presents the preliminary definition and information for the proposed work. In Section 4, the proposed algorithm, its flow chart and potential function analysis is presented. The processing of a set of jobs are simulated using MBS and the best identified algorithm to observe the working of MBS. Section 6 provides the conclusion and future scope of the work.
## 3 Methodology
### HybridLG Framework Construction
#### 3.3.4 Feature Fusion Network and Head Network  
In addition to the training methods for sub-models described above, a feature fusion network is required to combine their outputs, and a head network is required for final classification. The neck part of Figure 3 shows the fully connecting layer for fusing local and global information. Specifically, both ResNet and MobileViT-Plus have been trained to learn the parameters of their respective networks. The outputs of these networks are probabilities obtained by applying the ResNet and MobileViT network to the input images, classifying them into five different categories. In addition, the training of the fully connected layer in the neck part will facilitate the fusion of the outputs from ResNet and MobileViT-Plus. Since each sample's classification from local and global information may differ, finding a balanced classification boundary and learning more essential features is necessary. Thus, the weighted sum is an excellent choice. Inspired by the idea of weighted soft voting [51] from ensemble learning, we created a weighted voting mechanism for a feature fusion network. This fully connected layer, which uses identity mapping as the activation function and a bias vector to adjust the output offset of the layers, combines each sub-model with learnable parameters. The fully connected layer maps values to a vector, where each vector element represents a specific class's score. A higher score indicates that the model is more likely to predict the corresponding class.  
A softmax layer was utilized in the head part to output the probability of classification of each sample into each category (Figure 3). The category with the highest probability is the final prediction of the model. The formula for _Softmax_ is as follows, where \(i\) represents the \(i\)-th value in the vector and \(j\) represents the other values:  
\[Softmax(z_{i})=\frac{e^{z_{i}}}{\sum_{j}e^{z_{j}}} \tag{4}\]  
During training, the model uses the backpropagation algorithm to update the weight parameters of the fully connected and _Softmax_ layers. It minimises the loss function and improves the classification accuracy of the model.
## 2 Results and Discussions
### Bioactive Properties
The main primary amino groups in the medium are located at the N-terminal end of the polypeptide chain and at the epsilon amino group of the amino acid lysine. Hydrolysis of the polypeptide chain increases the number of amino groups in the medium. The main reasons for this increase may be the proteolytic activities that occur as a result of microbial activities (proteases hydrolyze polypeptide chains and increase the amount of free amino acids and peptides with primary amino groups), because _Aspergillus oryzae_ has the ability to produce proteases such as extracellular alkaline protease, aspartic protease, neutral protease II, carboxypeptidase (49). Although there is an increase in the amount of lysine amino acid in the medium after fermentation (hazelnut meal lysine 1.64%, fermented hazelnut meal lysine 2.01%), it is thought that the main effect on the result is more likely to be caused by the primary amino groups of free amino acids and peptides released as a result of protein hydrolysis. In this study, it was also observed that there were significant increases in the antioxidant activity values of the FHOC during the fermentation process. The fermentation process increased the ABTS cation radical scavenging activity of the meal by 3.8 times and DPPH radical scavenging activity by 4.9 times. Although there was a significant increase in hydroxyl radical scavenging capacity, this increase was quite limited compared to other antioxidant activities. In the literature, it was reported that there were significant increases in the antioxidant activities of the end products formed when different plant sources (wheat, oat, buckwheat, soybean, chickpea, rice bran, cocoa) were fermented with solid or liquid fermentation methods using various microorganisms (bacteria, yeast, mold) (99). Studies have shown that the DPPH radical scavenging activity of soybean meal fermented by SSF method using _Aspergillus oryzae_ increased 2.5 to 5.5 times after fermentation (51,104). Similarly, this activity increased approximately 3-fold with rice fermentation (97). [PERSON] and [PERSON] (2014) reported significant increases in DPPH radical and ABTS+ radical scavenging activities of fermented wheat, brown rice, corn, and oats in their study (98). After fermentation of flaxseed meal by solid culture fermentation using _Aspergillus oryzae_, the ABTS cation radical scavenging activity of the meal increased depending on the fermentation time (80). In a study in which hazelnut milk was fermented with kefir grains, the DPPH radical scavenging activity of the beverage increased (60%) after fermentation (105). Although this increase is quite low compared to the increase in our study, it is thought that the type of fermentation (dry, liquid), type of microorganism (bacteria, mold), and type of material (hazelnut meal, hazelnut kernel) have a decisive impact on the result.