esa-sceva/satcom-chunk-collection huggingface dataset:
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 6 SemKoRe Implementation
### Failure Data Collection
#### 6.2.2 Failure Ontology Instantiation  
We use the Model Driven Interfaces (MDI) to dynamically generate the user interfaces using the Failure ontology. This procedure has two advantages: One, the UIs allow instantiation of the Failure ontology by non-technical users without any knowledge about the Semantic Web or ontologies; and Two, the UIs enforce the constraints defined in the ontology model and ensure that all the inputs are valid. These UIs rely on the annotations defined in the ontology such as, _@rdfs:label_ and _@rdfs:comment_. The former is used as a human readable label of the input fields shown to the user, while the latter is displayed to explain the nature of the field and the expected input. We defined an additional annotation _@semkore:hidden_ to hide a field on the UI in case it should be defined automatically or exclusively by an expert.  
The ontology model incorporates two types of constraints: value and cardinality constraints, as described below.  
In the W3C OWL reference [28], a value constraint is used to enforce restrictions on the range of a property when applied to a particular class description. Value constraints can be applied to data properties, for which the value is a data literal, and object properties, for which the value is an individual. We handle each type of property differently:  
* Data properties: Users can input a value in the text box which will be validated to make sure that the data type is correct as per the defined constraints; and
* Object properties: A select box is provided with the list of all possible values. For example, for the object property _Machine__ _hasComponent_ _AllValuesFrom_ _Component_, will lead to a select box with the list of all available **Component** instances. With this approach, the probability of getting an invalid input is eliminated altogether.  
Only the _owl:someValuesFrom_ constraint was managed differently from the W3C standard [28] definition, as it defines a constraint that is applied to at least one value, which means that the property could have other values without any restrictions. Since our UIs are targeting non-expert users, and to guarantee the consistency of our model, we considered _owl:someValuesFrom_ as being similar to the _owl:allValuesFrom_ constraint in our implementation.  
Cardinality Constraints  
In the W3C OWL reference [28], a cardinality constraint restricts the (min, max, or exact) number of values a data or object property can have. To satisfy the cardinality, single/multiple input fields are generated for each property, allowing to the user to provide the correct number of values for each property.
## Preliminaries
### Security Assumption  
**Discrete Logarithm (DL) problem [35]:**\(G_{1}\) is a multiplicative cyclic group, \(g\) is a generator of \(G_{1}\). Given \((g,g^{a})\in G_{1}\), compute \(a\).  
**Definition 1** (DL Assumption): _For any probabilistic polynomial time (PPT) algorithm \(A\), the advantage for \(A\) to solve the DL problem in \(G_{1}\) is negligible, which can be defined as \(Adv_{A}^{\text{DL}}=Pr\Big{[}A(g,g^{a})=a:a\overset{R}{\leftarrow}Z_{p}^{*} \Big{]}\leq\varepsilon\)._  
**Computational Defie-Hellman (CDH) Problem [36]:**\(G_{1}\) is a multiplicative cyclic group, \(g\) is a generator of \(G_{1}\). Given \((g,g^{a},g^{b})\in G_{1}\), compute \(g^{ab}\).  
**Definition 2** (CDH Assumption): _For any PPT algorithm \(A\), the advantage for \(A\) to solve the CDH problem in \(G_{1}\) is negligible, which can be defined as \(Adv_{A}^{\text{CDH}}=Pr\Big{[}A(g,g^{a},g^{b})=g^{ab}:a,b\overset{R}{\leftarrow} Z_{p}^{*}\Big{]}\leq\varepsilon\)._  
The \(\varepsilon\) denotes a negligible value in the above definitions.  
**3. System Model and Security Model**  
In this section, we introduce the system model and security model of CL-PGSDP protocol.
# An Improved Contact Force Model of Polyhedral Elements for the Discrete Element Method
## 6 Conclusions  
By combining the experimental and numerical simulation approaches, this study explores the contact force between polyhedral elements under various contact modes, and the following main conclusions were drawn:  
1. The experimental method designed in this study can be employed to test the contact forces between polyhedral blocks under different modes. A contact stiffness correction coefficient was introduced into the Cundall model, leading to the construction of an improved contact force model suitable for polyhedral elements.
2. The improved contact force model was incorporated into the DEM. An analysis of simulated contact force peaks and force patterns revealed an improved accuracy of the model, and this would affect the motion behavior of polyhedral elements.
3. The DEM simulation methods, both before and after the improvement, were applied to packing experiments with cubic specimens. A comparative analysis of the results confirmed the improved accuracy and reliability of the improved contact force model in simulating the contact of polyhedral elements.  
This work presents an exploration into the study of contact forces involving polyhedral elements. The proposed improved contact force model optimizes the DEM simulation of polyhedral elements, and future research could further improve experimental techniques based on this study. Additionally, there is potential to develop contact models for more complexly shaped elements for engineering applications in subsequent studies.  
Conceptualization, Y.W.; Methodology, J.L.; Software, J.L.; Formal analysis, Y.W.; Resources, Y.W.; Data curation, Y.W.; Writing--review and editing, M.Z., Z.L., H.Z., F.Z., C.O. and P.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (no. 51174076 and no. 51874118).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest. The authors declare no potential conflicts of interest concerning the research, authorship, and/or publication of this article.  
Figure 19: Packing simulation of randomly shaped elements: (**a**) 10 cubes; (**b**) 20 cubes; (**c**) 30 cubes.
## 2 Materials and Methods
### Equipment  
The participants' movements were measured concurrently with an athlete tracking technology device consisting of 10-Hz GNSS (Vector S7, Catapult Innovations, Melbourne, Australia) and a 3D motion analysis system (Vicon, Oxford, UK) sampling at 100 Hz. The GNSS device was placed between the athlete's scapulae using the manufacturer's west with a tight fit to avoid unnecessary device movement. Data collection procedures were in accordance with the guidelines of [PERSON], [PERSON], [PERSON], and [PERSON] [2], with each participant having their own specific device. The study sample had an average (\(\pm\)SD) horizontal dilution of precision (HDOP) of 0.72 \(\pm\) 0.12 and 15 \(\pm\) 3 number of satellites. A 10-camera motion capture system (Vantage 16, 18 mm, Vicon) was used to provide the criterion real-time measurements of distance, velocity, and acceleration (Figure 1). This system has previously been used to determine the criterion measurements for athlete tracking systems [19,34]. To ensure stable marker recognition within the 15 \(\times\) 9 m measurement area, retro-reflective markers with a diameter of 26 mm were used. One retro-reflective marker was firmly attached to the participant on each of the following locations: (1) the outside of the manufacturer supplied vast, in correspondence with the middle of the GNSS device; (2) the acromion of each shoulder of the participant; (3) each anterior superior iliac spine; (4) fifth lumbar vertebra (Figure 2). The marker corresponding to the middle of the GNSS device was used for analysis; all other markers were used for gap filling where needed. Gaps in the data \(\leq\) 50 ms (5 samples) were filled using spline interpolation or rigid body fill, gaps \(\geq\) 50 ms were excluded from analysis. Out of all trials, 50 data samples (4 gaps) were excluded, which equalled 0.045% of data being excluded.
# Insights into PCDD/Fs and PAHs in Biomass Boilers Envisaging Risks of Ash Use as Fertilizers
###### Abstract  
Since ashes are a possible source of Persistent Organic Pollutants (POPs) contamination, their application in soils must be subject to more study and control. In this scope, feed residual forest biomasses and biomasses ashes, collected along one year in four biomass power stations, were characterized mainly for their polychlorinated dibenzo-p-dioxins and polychlorinated dibenzoforurans (PCDD/Fs) and Polycyclic Aromatic Hydrocarbons (PAHs) contents. The biomasses present concerning levels of Cl (0.04-0.28%) that may lead to PCDD/Fs formation. The biomasses also contain OCDD (29-260 ng/kg) and 1,2,3,4,6,7,8-HpCDD (35 ng/kg) that may contribute to increased Toxic Equivalents (TEQs) of ashes, possibly involving declorination and ash enrichment mechanisms. While the WHO\({}_{2005}\)-TEQs in bottom ashes (14-20 ng TEQ/kg) reaches the proposed limit (20 ng TEQ/kg) for ash use as fertilizers, in fly ashes (35-1139 ng TEQ/kg) the limit is exceeded. PAHs are below 0.02 mg/kg in bottom ashes and 1.5-2.5 mg/kg in fly ashes, complying with the proposed limit of 6 mg/kg. As bottom and fly ash streams may contain different ash flows, a clear definition of ash mixes is required. Correlations between unburned carbon (C), PAHs and PCDD/Fs were not found, which highlights the need for compulsory PCDD/Fs analysis in ashes, independently of their origin, burnout degree or levels of other contaminants. A sensitivity analysis was performed to evaluate the impact of handling non-detected values, which showed more impact for TEQs values close to the proposed regulatory limit of PCDD/Fs. These findings highlight the need to define reporting protocols of analytical results for risk assessments and conformity evaluation.  
Keywords:residual forest biomass; combustion; ash; fertilizers; PCDD/Fs; PAHs; TEQs; BaP-EQs; non-detected handling; regulatory limits +
Footnote †: journal: Applied Soft. 2020, 10, 4951; doi:10.3390/app10144951
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 3 Results
### Data Validation
#### 3.2.2 Equipment Validation  
The primary purpose of equipment validation is to ensure that distance measurements obtained using the distance measuring wheel meet the required standards of accuracy. The validation process involves performing three consecutive measurements over a straight 160 m section and recording the results. The average relative error between the three measured distances and the reference distance must not exceed 1% to be considered acceptable. Table 7 presents the validation results for the distance measuring wheel used in this study.
## 3 Results and Discussion
### Scientific Mapping
#### 3.2.4 Visualizing Products from Different Viewpoints and on a True Scale Stimulated Novel Insights  
The most frequently cited benefit of virtual-reality reviews of CAD on a screen is enhanced 3D visualization and manipulation. The conventional review process is often carried out on a computer with the use of CAD tools, on a flat screen. The visualization of CAD on a screen may not always satisfy all of the criteria for functional and ergonomic validations of complicated 3D models [16]. Virtual reality allows more novel modes of visualization and interaction to enhance engineering design reviews in this situation [16].  
Several studies reported that the opportunity to observe a product from different angles and with more detail generated innovative discoveries [55]. The authors of [41] observed that visualizing planes in a virtual reality environment gave participants a better understanding of the spatial relationships between product components, as well as the interaction space around the assembly line, allowing the design team to understand the operating clearances in real size. The authors [42] found that relevant information about transport routes or kinematic properties, which is either not modeled in the CAD data or is lost during conversion processes, played an important role in reviews and provided an observation of machine mechanics (e.g., motor-driven mechanisms). The authors of [41] observed that the possibility of viewing and interacting with the geometry of the product (a pump) and the surroundings (an assembly line) on a true scale benefited the team in understanding important viewability problems during a subassembly engagement. The team received kinesthetic and ergonomic information on operator movement via natural engagement with the Wii remote [41].  
On average, we found a consensus that this new visualization facilitated overall mistake detection. The authors of [16] found that, when compared to a standard CAD software approach on a flat computer screen, participants are more likely to spot faults in a 3D model inside an immersive virtual reality environment. In [41], the team uncovered design flaws and possible solutions that could not be detected or verified using conventional computer tools. Virtual-reality-enabled design review enables users to detect significantly more flaws in a 3D model than a CAD-software-based approach on a PC screen [16].  
The benefits of combining fully digital CAD models with physical components of hybrid prototypes, such as the cockpit gear utilized in the automotive industry, are also emphasized. With the addition of the physical model, designers can evaluate their designs both visually and tactiely. This adds a further physical dimension, allowing designers to not only \"see\" their designs, but also \"touch\" them, providing designers with simulatedinteraction solutions in the early stages of design [48]. In [19], it is argued that virtual reality's benefits contribute to the Industry 4.0 and cyber-physical system issue of linking the physical and digital worlds.
## 2 Material and Methods
### Enrichment Factors  
In order for the metal enrichment in house dust and possible natural or anthropogenic sources to be determined, enrichment factors (EF) were calculated using the Equation (1).  
\[EF=\frac{\left(C_{x}/C_{ref}\right)_{sample}}{\left(C_{x}/C_{ref}\right)_{ background}} \tag{1}\]  
where \(C_{x}/C_{ref}\): the ratio of concentrations of trace metal to the corresponding concentration of a reference metal in the sample and background. In the present study, Al was selected as reference metal, as it is considered that anthropogenic activity contributes the least to its presence. In general, as long as the enrichment factor is less than 10, the natural origin of the elements dominates, while for values greater than 10, their enrichment due to anthropogenic activity is important due to anthropogenic activity [15; 23], especially for metals for which the factor is estimated to be greater than 40.
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 2 Related Work
### Verifiable Random Function
#### 2.2.1 Indexed Verifiable Random Function (iVRF)  
The indexed verifiable random function (iVRF), introduced by [PERSON], is a quantum-resistant extension of traditional VRFs tailored for blockchain leader election and consensus protocols [12]; iVRFs add crucial features like forward security, ensuring that past outputs remain secure even if the secret key is compromised. This is achieved using cryptographic hash functions, making iVRFs both scalable and secure for blockchain applications.  
Traditional VRFs can encounter issues related to key refreshing, especially in blockchain environments, where long-term security is paramount. When using lattice-based post-quantum cryptographic schemes, more frequent key updates may be necessary to maintain security levels; iVRFs solve the issue of frequent key refreshes by using lattice-based hierarchical identity-based encryption (HIBE) schemes. This allows keys to be refreshed efficiently without replacing the entire key set, which is critical for long-term security in blockchain environments. In this system, a master secret key is used to derive round keys, which are each assigned a different identity (ID) and discarded after use. By employingthis mechanism, iVRFs effectively manage the challenges associated with frequent key renewals, enhancing the overall security and efficiency of blockchain applications, where such functionality is critical. This means that even if the current secret key is compromised, all previous outputs remain secure, thereby maintaining the long-term security and integrity of the blockchain system.  
In protocols like Algorand, iVRFs facilitate the random selection of block proposers and committee members while maintaining fairness and decentralization. The implementation of iVRFs demonstrates significant performance improvements over other quantum-safe VRF proposals, with faster evaluation and verification times; iVRFs strike a balance between proof size, computational efficiency, and communication overhead when compared to other post-quantum VRF models like lattice-based VRFs (LaVs) and ring-LWE-based VRFs [13; 14].
## 2 Methodology
### Observation Data Description
#### 2.3.4 Site-Level FPAR  
We use data from the Bartlett Experimental Forest flux tower site (44.06 N, 71.29 W, and 272 m elevation) in north central New Hampshire, USA. This measured data is available from the AmeriFlux Web page [49]. Half hour-averaged measurements from 1 January 2005 to 30 December 2006 were used in this study. The vegetation is primarily deciduous forest [28]. The flux tower is 25 m in height and set in a relatively flat area. Quantum sensors are placed above and below the vegetation canopy. Above-canopy sensors are located at the top of the tower, sensors facing upward measure the incident photosynthetic photon flux density (\(Q_{incident}\)) while sensors facing downward measure the photosynthetic photon flux density reflected from the canopy (\(Q_{reflected}\)). Six below-canopy sensors are placed in a circle (radius = 15 m) centered at the base of the tower [32]. They face upward and thereby measure the photon flux density transmitted through the canopies or gaps (\(Q_{transmitted}\)). Site-level FPAR is calculated by:  
\[\mathit{FPAR}=\frac{Q_{incident}-Q_{reflected}-Q_{transmitted}}{Q_{incident}} \tag{5}\]  
Multiple sites for various plant types are desired to rigorously evaluate the model's performance. However, due to the data availability, Bartlett Experimental Forest flux tower site was initially selected in this study to qualitatively diagnose model's performance in deciduous forest.
## 2 State of the Art
### Constraints
#### 2.1.3 Machine Flexibility Constraints  
In a modern manufacturing unit, machine flexibility is a very important feature that enables increasing the overall workshop capacities, reducing or eliminating the impact of bottleneck stages and balancing the capacities of the stages for the overall workshop. Such a production unit is characterized by several stages. Each stage is made up of a set of parallel machines. At some stages, the machines are duplicated and a job can be processed on any machine. A flowshop with parallel machines is also known as a multiprocessor flowshop, flexible flowshop, or hybrid flowshop. Machine flexibility has attracted much attention from researchers in recent years. There are several examples provided in the literature, including steelmaking [21; 22], industry [23], as well as the semiconductor industry [24; 25]. [PERSON] et al. [26] provide a survey on several real-world applications, ranging from the metal forming industry to the paper industry to the chemical industry. Some researchers address real-world problems in their papers.  
[PERSON] et al. [27] exhibit a local search approach to solve highly realistic flexible flow line scheduling with setups, buffer capacities, as well as blocking and breakdowns. Several heuristics are provided by [PERSON] [28] for the flowshop scheduling problem with multiple identical machines per stage, precedence constraints and time lags, and setups. [PERSON] and [PERSON] [29] provide a metaheuristic, in the form of a genetic algorithm, to a complex generalized flowshop scheduling problem that results from the addition of unrelated parallel machines at each stage, sequence-dependent setup times, as well as machine eligibility. [PERSON] et al. [30] investigate the problem of hybrid flexible flowshop (HFFS) with sequence-dependent setups, where the objective is to minimize the makespan. They put forward two advanced algorithms that effectively handle the flexible and setup features of this problem. [PERSON] [31] proposed an integer hybrid metaheuristic based on the principles of variable-neighborhood descent and TS for unrelated parallel machines problems with ready times and sequence- and machine-dependent setup times to minimize the weighted number of tardy jobs.  
While many papers in the literature have tackled various realistic considerations and constraints, to the best of our knowledge, there has been no effort to jointly address the set of realistic constraints incorporated in the problem formulation of our paper, which include sequence-dependent setups, machine flexibility, and resource calendar constraints.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Ablation Studies
#### 4.6.1 Effect of Multimodal Features  
We evaluated the recommendation performance of UPGCN by feeding each modal feature into the model sequentially. Specifically, we designed the following UPGCN variants:  
* \(\text{UPGCN}_{\text{w/o-vk}}\): In this variant, UPGCN eliminates the input of two modalities, thereby transforming into a general recommendation model;
* \(\text{UPGCN}_{\text{w/o-v}}\): This variant denotes that UPGCN utilizes exclusively the textual modality of the items;
* \(\text{UPGCN}_{\text{w/o-t}}\): This variant indicates that UPGCN has eliminated the textual modality of the item, thereby utilizing solely its visual modality.  
As is shown in Table 3, in all evaluation metrics (R@10, R@20, N@10, and N@20) and datasets (Baby, Sports, and Clothing), \(\text{UPGCN}_{\text{w/o-v}}\), which only utilizes textual features, achieved higher recommendation performance than \(\text{UPGCN}_{\text{w/o-t}}\), which only relies on visual features, indicating that the text modality contains more information. Furthermore, the two variants that integrate single-modal features demonstrate superior recommendation performance compared to \(\text{UPGCN}_{\text{w/o-vk}}\), which does not incorporate any modal features, thereby further substantiating the significant role of multimodal features. Additionally, the modal features derived from the texts and images of products in the Sports dataset are less informative compared to those obtained from the Baby and Clothing datasets. The evidence is that the average performance improvement of \(\text{UPGCN}_{\text{w/o-t}}\) in the Sports dataset is 7.23%, while it is 9.71% and 21.91% in the Baby and Clothing datasets, respectively. In the Clothing dataset, characterized by the highest level of data sparsity (as is shown in Table 1), the performance improvement achieved through the integration of multimodal features is particularly significant. This further suggests that incorporating multimodal features within recommendation systems can effectively alleviate the data sparsity issue.  
Finally, UPGCN utilizing multimodal features demonstrates higher recommendation performance compared to the other two variants employing single-modal features, indicating that the integration of multimodal features can significantly enhance UPGCN's recommendation performance; thus, our proposed fusion method is essential.