esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Cluster Analysis  
Cluster Analysis is a way to categorize the observed samples or the variables of the data into each cluster. That is, objects which have not been categorized are gathered in the same group according to their degrees of similarity. This approach means that those which are in the same cluster possess a high degree of homogeneity, and samples in different clusters possess a high degree of heterogeneity. A large portion of the applications of cluster analysis belong to the category of exploratory research, which can be utilized for data simplification or for generating the segmentation of research targets.  
There are two major types of segmentation by the cluster analysis: hierarchical and non-hierarchical approaches. There is also the two-step approach, which combines these two types of methods. The hierarchical approach connects the individuals according to a specific distance that has been measured or the similarity of individuals, but the segmentation count is not known beforehand. In addition, the non-hierarchical approach determines the segmentation count of the samples beforehand based on the results of previous studies or the researchers' subjective cognition.  
There are many different ways for the cluster analysis to measure the similarity of research samples, including the methods of nearest neighbor, furthest neighbor, average linkage, centroid clustering, median clustering, and Ward's method. In addition, the measurements between clusters can also be divided into distance measures and association measures. The approach of distance measures is to present the similarity of research samples quantitatively, while the input of the association measures is qualitative data.  
In this research, the part-worth utilities sequence for each subject on different aesthetic principles, which was obtained by conjoint analysis, will be viewed as the quantitative distance data input to conduct the cluster analysis. Since the count of aesthetic tastes groups will be divided into is still unknown, hierarchical cluster analysis has been adopted. In addition, the measurement of within-groups linkage, which is less impacted by extreme values, has been adopted in this research.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 1 Introduction
However, this approach requires both frequency-domain and time-domain simulation to generate training data for the ANN leading to inefficiency in a complex structure. These approaches require both frequency-domain and time-domain simulation to acquire the training data. Although the ANN model can directly predict the time-domain parameters, both frequency-domain and time-domain simulations in these approaches consume significant effort. Additionally, the frequency-domain is the most time-consuming stage in the design procedure. Moreover, it is a crucial task to analyze and optimize the channel properties in this domain. Hence, this paper proposes a low-power design methodology based on ANN to overcome these shortcomings. The proposed methodology leverages the ANN model to produce reliable data in the frequency-domain quickly. This model significantly improves the design efficiency in the frequency-domain. In addition, the method utilizes the ANN model to determine the optimal structure with the objective function. The optimal structure and condition that are found from the proposed approach provides the highest margin in the eye diagram for transmitter supply voltage reduction. Hence, the designer can reduce the voltage swing at the transmitter circuit while the signal quality is still guaranteed. The rest of the paper is organized as follows: Section 2 describes the important parameters in the printed circuit board (PCB) interconnect design. Subsequently, Section 3 introduces the artificial neural network and the proposed approach. Following that, Section 4 illustrates the approach efficiency in typical structures including microstrip line and stripline. Finally, Section 5 summarizes the conclusions about the applicability of the proposed approach.
# Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers
## 5 Identified Gaps and Directions for Future Research
### Studies on Implementation Patterns of I4.0 and LSS Integration Are Rare  
Except for the work in [23], our review did not find any research addressing implementation patterns for I4.0 and LSS integration. Future research in this direction could  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**I4.0 Technologies**} & \multicolumn{1}{c}{(\%)} \\ \hline Just-in-Time (JIT) & 49\% \\ Value-Stream Mapping (VSM) & 39\% \\ Kanban & 31\% \\ Total Productive Maintenance (TPM) & 30\% \\ Flow Continuous & 24\% \\ Low Setup & 24\% \\ Pull & 20\% \\ Involved Customers & 20\% \\ Kaizen/Continuous Improvement & 20\% \\ Poka-Yoke & 20\% \\ Statistical Process Control (SPC) & 19\% \\ S & 19\% \\ Developing Suppliers & 18\% \\ Involved Employees & 15\% \\ Jidoka & 14\% \\ Adon & 14\% \\ Define-Measure–Analyse–Improve–Control (DMAIC) & 12\% \\ Heijunka & 11\% \\ Supplier Feedback & 9\% \\ Total Quality Management (TQM) & 9\% \\ Visual management & 9\% \\ Failure Modes Effect Analysis (FMEA) & 7\% \\
5 Whys & 5\% \\ Human Resource Management (HRM) & 4\% \\ Controlled processes & 4\% \\ Design of Experiments (DOE) & 4\% \\ Critical to Quality (CTQ) & 4\% \\ Plan–Do-Check-Act -PDCA) & 4\% \\ Cellular Manufacturing & 4\% \\ Constant Work in Process (CONWIP) & 1\% \\ Suppliers–Inputs–Process–Outputs–Customers (SIPOC) & 1\% \\ Quality Function Deployment (QFD) & 1\% \\ Gemba & 1\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: LSS techniques and tools that integrate with I4.0, according to reviewed articles.  
address patterns in companies that have significantly progressed in the horizontal integration of I4.0 as part of the value chain, that is, towards the collaboration/cooperation of cyber-physical systems involving LSS tools and techniques. Similarly, it could address companies successfully deploying an end-to-end engineering approach in the value chain and the potential synergies of this with DMADV and DMAIC. Furthermore, given the difficulties associated with the vast volumes of data collected through I4.0 technologies, future studies on implementation patterns could also cover algorithms for data analyses currently used by companies in the context of integration between I4.0 and SS and practical uses of the information.
## 2 Results
### Stress Conditions Impair E. coli BA1250 Growth  
To study the influence of stress factors on TA system activation, whether and how these factors could influence BA1250 growth were initially evaluated. Four types of stress factors were selected according to the possible conditions that a hybrid strain would encounter in the host: nutritional deprivation, osmotic, oxidative, and acid environment. The experimental procedure for the evaluation of stress factors' influence on bacterial growth is described in Figure 1A, and it was designed to collect samples both in the logarithmic and stationary phases.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Gene** & **Function** & **Type** & **Number of Copies** & **Location a** \\ \hline _cptB_ & Antitoxin & & 1 & Chromosome \\ _cptA_ & Toxin & IV & 1 & Chromosome \\ _yceU_ & Antitoxin & & 2 & Chromosome \\ _yceV_ & Toxin & IV & 1 & Chromosome \\ _yceU_ & Antitoxin & & 2 & Chromosome \\ _ykH_ & Toxin & IV & 1 & Chromosome \\ _glnoS_ & Antitoxin & & 1 & Chromosome \\ _glnoT_ & Toxin & V & 1 & Chromosome \\ \hline \end{tabular}  
* Predicted location determined by plasmid peeler (SCAPP) and PlasFlow.  
\end{table}
Table 1: _Cont._  
Figure 1: **(A)** Experimental design for bacterial growth under stress. The illustration depicts how the experiments were designed for cultivation under stress conditions, the media employed for bacterial growth, and the bacterial cultivation phase in which samples were collected for qPCR analyses. Created with BioRender.com. Count of BA1250 Colony Forming Units (CFUs/mL) under different culture conditions in the **(B)** logarithmic and **(C)** stationary phase. Statistical analysis was performed using the non-parametric \(t\)-test, compared to bacteria growth in the LB medium. *_p_-value < 0.02; **_p_-value < 0.002.  
Of the conditions studied, there was a significant influence on the growth dynamic between the control condition (LB medium) and osmotic stress, leading to a diminished number of CFUs both in logarithmic and stationary phases (Figure 1B,C). Both acid and oxidative stress factors impacted in a growth-phase-dependent manner, since acid stress altered bacterial growth in the logarithmic phase, leading to a lower number of CFUs, and oxidative stress had no influence. On the other hand, oxidative stress led to a considerable decrease in CFUs in the stationary phase, and acid stress conditions had no disturbance. Surprisingly, there was no significant influence of nutritional deprivation on the number of detected CFUs at either of the studied phases.
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 2 Materials and Methods
### The Prediction Step of the TC RTK/INS Integration
#### 2.2.2 The Covariance Matrix of the Process Noise  
The INS process noise is related to the IMU measurement model. We refer to Kalibr to set the IMU noise model [72]. The model contains two kinds of errors: an additive noise term that fluctuates very quickly, also known as \"white noise,\" and a slowly varying sensor bias, also known as \"the random walk.\" We use \(\sigma\) with different subscripts to denote the noise density of corresponding sensor errors, and the covariance matrix of the INS process noise can be formulated as Equation (9) shows:  
\[\mathbf{Q}_{\text{INS}}=\begin{bmatrix}\sigma_{1,\text{gyro}}\mathbf{I}_{3\times 3}&\mathbf{O}_{3 \times 3}&\mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}\\ \mathbf{O}_{3\times 3}&\sigma_{1,\text{acc}}\mathbf{I}_{3\times 3}&\mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&\mathbf{O}_{3 \times 3}\\ \mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&\mathbf{O}_{3 \times 3}\\ \mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&\sigma_{2,\text{gyro}}\mathbf{I}_{3 \times 3}&\mathbf{O}_{3\times 3}\\ \mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&\sigma_{2,\text{acc}}\mathbf{I}_{3 \times 3}\end{bmatrix}, \tag{9}\]  
where \(\sigma\) with subscripts \"1\" and \"2\" denote the strength of the white noise and the random walk, respectively; \(\mathbf{O}\) with different subscripts denote zero matrices with different dimensions. These parameters can be obtained from the datasheet and the Allan Variance analysis [73].  
GINAV models the SD ambiguity as a random walk process for GNSS process noise [71]. In our implementation, we directly set the parameters in the SD ambiguity noise covariance matrix to zero as RTKLIB does [74]. In this case, the covariance matrix of TC integration can be formulated as shown in Equation (10):  
\[\mathbf{Q}=\begin{bmatrix}\mathbf{Q}_{\text{INS}}&\mathbf{O}_{15\times m}\\ \mathbf{O}_{m\times 15}&\mathbf{O}_{m\times m}\end{bmatrix}, \tag{10}\]  
where \(m\) refers to the dimension of the SD ambiguity, which means the number of available satellites is \(m\).
# Anonymous Access System with Limited Number of Uses in a Trustless Environment
## 2 Literature Review
After the evaluation of some of the research based on electronic ticketing services [13; 14; 15; 16; 18; 19; 20; 21; 22; 24; 25; 26] found in the literature, the conclusion is that most of these are focused on granting access to events; solving problems on ticket reselling in the secondary market [14; 16; 26], privacy protection [15; 16; 23; 24], but protection and scaling [13; 14], while some also envisage anonymity [16; 23; 25]. Other research has been oriented towards the transport industry re-usability [19; 20; 21; 22]. Despite their strengths, existing solutions lack a crucial combination: anonymity, multi-use capability, and safeguards against malicious service providers (\(\mathcal{SP}\)) potentially engaging in replay attacks.
## 2 Materials and Methodology  
### Materials  
In this study, 6 recycled aggregates were selected and collected directly from different sources [14]: recycling plants, in situ demolitions, and concrete industry wastes (Table 1). Additionally, a reference concrete aggregate was also produced in the laboratory to be compared with the recycled aggregates. The composition of the control concrete aggregate (CA-L) is presented in Table 2. The binder used was the cement type CEM II/A-L 42.5 R. This cement is composed of 80% to 94% of clinker, in addition to limestone and other minor constituents [22]. CA-L comes from cubic concrete specimens of strength class C30/37. These specimens were kept under water to prevent natural carbonation between production and testing. At about three months of age, these cubes were dried at 40 \({}^{\circ}\)C until constant mass, and after that ground in a laboratory crusher to be analysed before and after carbonation. The aggregates' characterization is presented in the study by [PERSON] et al. [14]. Table 3 presents the results of the XRD analysis of the RAs. The three mixed recycled aggregates collected directly from recycling plants (MRA-RP1, MRA-RP2, and MRA-RP3) primarily reveal the presence of quartz, muscovite, microcline, albite, and calcite. No portlandite was detected within the limits of the XRD analysis, suggesting a low carbonation potential for these mixed recycled aggregates. In contrast, the XRD analysis of recycled concrete aggregates (RCAs) collected from recycling plants showed a notable presence of portlandite, attributed to the higher cement content (Table 4). Other significant mineral compounds identified in the RCA included calcite, quartz, microcline, ettringite, gypsum, muscovite, hydrocalumite, kaolinite, and albite. Overall, the RCA was predominantly composed of calcite and quartz, along with microcline and albite [23], which are not likely to carbonate. Also, the XRD analysis of CA-L revealed the existence of portlandite and other major mineralogical compounds, namely gypsum, quartz, calcite, and microcline. \begin{table}
\begin{tabular}{l l l l} \hline
**Aggregate Type** & **Designation** & **Origin (Collected at)** & **Location** \\ \hline \multirow{3}{*}{Mixed recycled aggregate (MRA)} & MRA-RP1 & Recycling plant 1 & Pero Pinheiro, Portugal \\ \cline{2-3}  & MRA-RP2 & Recycling plant 2 & Figueira da Foz, Portugal \\ \cline{2-3}  & MRA-RP3 & Recycling plant 3 & Seixal, Portugal \\ \hline \multirow{3}{*}{Recycled concrete aggregate (RCA)} & RCA-IR1 & Concrete producer & Alhanda, Portugal \\ \cline{2-3}  & RCA-RP1 & Recycling plant 1 & Pero Pinheiro, Portugal \\ \cline{2-3}  & RCA-RP2 & Recycling plant 4 & Bucelas, Portugal \\ \hline Control concrete aggregate (CA) & CA-L & Concrete produced in laboratory & Lisbon, Portugal \\ \hline \end{tabular}
\end{table} 
Table 1: Recycled aggregates studied and origin (taken from [14]).
### The Rising Importance of Including Non-Functional Requirements in Preliminary Design  
Studies within aerospace [12; 14], space [15] and construction equipment [16; 17] observed how functional requirements (e.g., thrust) often represent the concerns of external stakeholders (such as satellite operators), who typically have expectations of the product once in use. Internal stakeholders such as company owners and top management have also specific expectations, usually related to cost, risk of development, strategy and production. Internal stakeholder needs are often leading to 'non-functional requirements' or 'ilities', which are difficult to capture and explicitly expressed (compared to functional requirements and unit cost). As a result, non-functional requirements and 'ilities' are often considered in later phases of the project, which leads to technical solutions that are costlier for the manufacturers, as well as prone to scheduling delays [14].  
Figure 1: The increased production volumes expected for new space business scenarios is changing the way design trade-offs have to be considered.  
The need to trade functional and non-functional requirements simultaneously has resulted in a number of methodological approaches. Tradespace exploration (TE; [13]) models such characteristics as the utility of a system--aggregated adopting multi-attribute utility theory [18]--against lifecycle costs. In TE, design options are assessed in terms of utility and lifecycle costs, which also allow us to compare alternatives in terms of 'ilities' (e.g., flexibility, changeability and scalability). Value-driven design (VDD), [19] stresses the benefit of aggregating lifecycle costs and utility within the same monetary metric of value, because it provides a practical and convenient means to compare alternatives on targeted business cases. At the same time, [PERSON] recognized the difficulty of computing such a metric since many industry structures are complex, with competing customers, competing manufacturers, and competing lower-tier suppliers. For these reasons, [PERSON] proposed the use of a financial metric--surplus value (SV) [14]--to provide a simplified equation to a net present value analysis (NPV) [20], typically used by economists a basis for businesses investment decisions.
# Soybean Germination Response to Algae Extract and a Static Magnetic Field Treatment
## 1 Introduction  
Soybeans are among the most important grain legumes worldwide and their cultivation is gaining popularity, which results from the possibility of its versatile use in different industries [1]. Because of its valuable attributes, soybean accounts for a significant amount the world's vegetable oil, animal fodder, and food for human consumption [2, 3]. As this plant is very popular, new methods of plant growth-promotion/stimulation are still being sought. Stimulation of seeds using different priming methods as a way to increase their vigor for fast and strong plant development, optimized harvest efficiency, and quality of stimulation has caught the interest of many scientists [4, 5, 6, 7, 8, 9]. One of these methods is stimulation with algal extracts. Scientist have checked the influence of brown, green, and red macroalgae at different concentrations on soybean seed germination [4, 5, 7, 10, 11, 12, 13, 14] and growth parameters [15, 16, 17]. [PERSON] et al. checked also the impact of biofertlizer based on extract from the brown macroalgae _Ascophyllium nodosum_[18]. The effects of macroalgal extracts on soybean seeds, obtained by many scientists, are presented in Table 1.  
Quite strong global climatic fluctuations are responsible for the decline in crop productivity. Chemicals (fertilizers, plant protection products, growth promoters, etc.) andgenetic treatments are commonly used to improve yield. Many scientific studies have found that the influence of magnetic field (MF) on plants is an effective method of reducing diseases and increasing the tolerance of these plants to increasingly unfavorable natural environments [7; 9; 19; 20; 21; 22; 23; 24; 25; 26]. Under unfavorable conditions of abiotic stress, such as drought or soil contamination with heavy metals, MFs mitigate the effects of these stresses by increasing the number of antioxidants and, thus, reducing oxidative stress in plants [6; 27; 28; 29]. On the other hand, MF treatments reduce the plant disease index due to modulation of calcium signaling pathways, as well as prolines and polyamines [30; 31]. Studying the effects of magnetic fields on biological structures, including plants, is not entirely new. The first works in this area date back to the second half of the 19 th century [32]. The first documented work in this area is the publication of [PERSON] (1876), which presented the results of experiments on the influence of magnetic fields on plant development [33]. In
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 8 Internet-of-Things Trust Models
### QoS-Social-Driven Trust Models
Also, their model integrates the social relationships and context of interactions in the trust computation. The trust accuracy in this model can be affected by recommendations from dishonest nodes that assign higher trust values to their group of allies. [PERSON] and [PERSON] [76] proposed an event-driven trust model for IoT and they consider QoS and social trust attributes for trust computation. However, a context-aware issue has not been considered in their model. An access service recommendation model is proposed by [PERSON] et al. [81] for effective service composition and resistance against trust-related attacks. In their model, they consider both QoS trust metrics including energy status and quality reputation and social trust metrics based on social similarities. However, this study doesn't consider the contextual and dynamic nature of trust. Another study for trust management in SOA-based IoT application to service composition is proposed by [PERSON] et al. [82]. In their model, they utilized service quality as a QoS attribute to rate a service provider and social attributes to rate a recommender. Aggregating self-observations is done by utilizing Bayesian inference, while aggregating recommendations is done by utilizing social similarity weighted sum. Although the authors used QoS to rate service provider and social attributes to rate recommenders, only service quality attribute is used in trust formation. [PERSON] et al. [11] proposed a contextual SIoT trust model including independent and dependent metrics. Independent metrics include advertised and expected QoS while dependent metrics include social similarity friendship, social similarity relations, social similarity community, and contextual feedback of trust. Also, they proposed a mutual context-aware trustworthy service evaluation model to assess service providers and consumers trustworthiness. Authors in [83] applied only reputation and recommendation metrics that presented in [65] to propose their distributed trust model. Reputation metric in this study is redefined to be the opinion of other IoT nodes in the network and recommendation metric is redefined to be the opinions of social contacts. However, many other trust metrics are not considered. [PERSON] et al. [64] proposed a centralized trust model for SIoT based on reputation by utilizing reputation server to store reputation information. Two parameters are used to model the trust in this study; QoS trust attribute called credit to get a service and social trust attribute called reputation to measure node's trustworthiness. However, this study only considers the owner relationship and neglects other important social relationships.