esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art: Tolerancing Information Sharing throughout the Product Life Cycle
### Types of Tolerancing Information  
Based on Figure 1, it can be seen that different kinds of tolerancing information exist throughout the product life cycle. While the nominal product geometry and the specified tolerances are relevant during the design and manufacturing planning stages, part inspection processes add information about the observed part geometry and the process capability. Consequently, it is reasonable to divide the set of tolerancing information in specification information and verification information:  
* Specification information results as in- and output of functional tolerancing during design, i.e., nominal product geometry, functional product requirements, geometrical product requirements, tolerance types and specified tolerance values. More particularly, it is distinguished in ISO 21619 between functional specifications, which result from functional product requirements, manufacturing specifications, which are to satisfy requirements of related manufacturing processes, and verification specifications.
* Verification information is obtained from verification activities, such as the observed part geometry, observed tolerance values, and the measurement uncertainty related to the tolerance verification. Beside the information about the conformance of the individual part instances to the specifications and the actual part deviations, it comprises information about the measurement processes and their uncertainties.  
These two distinct categories of tolerancing information can also be enriched by manufacturing, inspection and assembly process data leading to insights on how the process parameters relate to the geometrical part or assembly characteristics.
# The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
## 9 Challenges and Research Directions  
As presented in this paper, transfer learning, if carried out successfully, can significantly improve the performance of AR data-driven modelling. In this Section, we summarise and discuss the challenges faced and the potential research opportunities and future directions of this investigation.  
TL-FmADLs was introduced as a transfer learning framework to automate machine-learning modelling. Although TL-FmADL was evaluated utilising InSync, one should note that the data collection was conducted in a simulated home environment over a scripted set of ADLs. To this end, TL-FmADL can develop further through an evaluation using data collected under natural conditions and an extended number of subjects, where subjects would perform their ADLs without guidelines, and the larger number of subjects could allow us to generalise the results better. This activity can address two issues. First, it could evaluate the classification capabilities of unpredicted human behaviour. Second, it could assess the latency and response time for the computation component, which could impact the performance of the modelling process.  
Moreover, one can observe that the _FrontDesk_ component seeks compatible machine-learning models to refer to as teachers. Although current experimentation was conducted utilising annotations to categorise each machine-learning model, a more robust approach can be planned. In particular, one can envision the formatting style proposed for the open data initiative (ODI) to represent data obtained from sensors [72]. ODI is an ontological model that includes a common protocol for data collection, a common format for data exchange, a data repository, and related tools to underpin research within the domain of activity recognition. ODI utilises the extensible event stream (XES) standard for event data storage [73] and an ontological model for the description of experimental metadata,which has been designed to address issues surrounding the management, storage, and exchange of sensing data [74]. In this regard, one can anticipate the availability of contextual information to characterise the environment in which the ADL is taking place, to optimise the framework's searching processes.
## 3 Computed Amplification Factors: Main Statistical Characteristics
### Means and Variability of AFs  
* For each profile set, we compute the \(n_{P}\times 14\times 11\)\(AF:\)\(AF(P_{k},\theta,S_{l},T_{l})\), the \(n_{P}\) average amplification factors (\(AF_{m}\)), to constitute, for each soil type, a database of \(3564\)\(AF_{m}(P_{k},\theta,T_{l},PGA(l_{0}))\) values and their associated variability \(\sigma_{AF}(P_{k},\theta,T_{l})\), the mean amplification factor \(AF_{0}(\theta,T_{l})\), and the associated initial variability \(\sigma_{0}(\theta,T_{i})\). The results are displayed in Figure 5(a),b for the clay and sand sets, respectively. The following main observations are derived:
* The peak period, i.e., the period with the peak amplification factor, covers a very broad range from 0.08 s to about 6-7 s for clay and sand soil profiles, which explains the richness of the database.
* As shown in Figures 4 and 5, the corresponding peak amplification ranges from less than 1.0 to 4.0 and up to 5.0 for low levels of \(PGA\) (0.01 g to 0.05 g). However, increasing the \(PGA\) level results in a decrease in the peak amplification factors to values of around 2.0 to 2.5 for a \(PGA\) ranging from 0.75 g to 1.05 g. The average amplification factors for clay are generally higher than those for sand at the mean (2 Hz \(\leq\)\(f\) < 5 Hz) and high (\(f\) \(\geq\) 5 Hz) frequency ranges.
* Some amplification factors exhibit a short period of de-amplification. A careful look at the corresponding soil profiles indicates that they correspond to deep soft soils, with low velocity, which act as seismic isolators.
* The overall average amplification factor (Figure 5(a),b) is higher than unity for periods greater than 0.5 s for clay soil profiles, but it is greater than 0.9 s for sand soil profiles. The lowest overall average amplification factor is observed in the 0.05 to  
Figure 6: Average amplification factors as functions of real period for each set of soil profiles, namely (**a**) clay soil profiles and (**b**) sand soil profiles. Thin blue lines correspond to every site profile (3564 results), thick red line is geometrical average over whole profile sets, and thick light blue lines are averages \(\pm\) one standard deviation.  
0.15 s period range for clay and sand soil profiles. In this period range, the overall average amplification factor is less than 0.7 and 0.6 for clay and sand, respectively. The overall average amplification factors are significantly smaller than the peak values for individual profiles, which highlights the need to identify relevant site parameters that may explain this site-to-site variability.
* The \"initial variability\" \(\sigma(\theta,T_{\mathrm{I}})\) associated with the average \(A\)Fs (Table 3) has a maximum value at low to intermediate periods (0.01 to 0.4 s), reaching up to 0.39 for clay and 0.47 for sand soil. It then gradually vanishes with the period's increase, reaching a value of around 0.065 at \(T=10\) s.
## 4 Discussion
It implies that the proposed method is even more compatible with superficial infections by fungi, yeast or Gram-positive bacteria due to their higher susceptibility to PEF [58; 59; 60]. However, further in vivo research is required to confirm this hypothesis. In terms of the immune response, we have shown that the PEF + AA treatment triggers significant changes in the levels of surface IgG, IgG1 and IgG2 antibodies. After the treatment, the response is comparable to the groups of mice without the infection (Figure 7, control group \"before\"). IgG1 is one of the most functional subclasses of antibodies, due to its superior affinity to FcyR, while IgG2 also plays an important role in targeting polysaccharides and is commonly induced during bacterial infections [61; 62]. Our data indicate that the reduction in both IgG1 and IgG2 is associated with reduced bacterial infection and an anti-inflammatory effect following the treatment, which shows no contrast with data presented by [PERSON] et al. [28]. As a limitation of our study, we could highlight the fact that it was a proof-of-concept work, where we aimed to characterize the synergistic PEF + AA treatment compared to AA-only treatment based on an available biotechnical protocol. However, as a matter for future work, it would be useful to characterize the effects of PEF alone and AA alone, and to determine the optimal conditions and concentrations for PEF + AA treatment. Moreover, the development of an in vivo wound model with the possibility to induce biofilms would significantly improve the accuracy of the model and the treatment planning. To conclude, nanosecond PEF + AA treatment shows promising results for the treatment of superficial infections, while it requires further optimization in terms of pulse parameters and the optimal concentration of AA. Acetic acid is routinely used in burn wound contexts since it does not contribute to the development of drug resistance in bacteria, while electroporation has been applied in clinics for decades to treat superficial and deep-seated cancer. Therefore, the transition of the proposed methodology to a clinical context is merely a matter of time due to the already established safety and treatment planning measures. The available clinical electrooperators can be employed. Conceptualization, V.N., I.G. and A.Z. ; methodology, V.N. and I.G. ; investigation, E.P., E.R. and V.M.-P.; resources, J.N. and I.G. ; writing--original draft preparation, V.N. and E.R. ; writing--review and editing, E.P., E.R., V.M.-P., A.Z. and J.N. ; visualization, V.N., E.P. and V.M.-P.; supervision, V.N. and I.G. All authors have read and agreed to the published version of the manuscript. This research was funded by Research Council of Lithuania grant number LAT-02/2016. The protocol was approved by the State Food and Veterinary Service (License Nr. G2-48). Not applicable. Data availability Statement: Data are available from the corresponding author V.N. on request. The authors declare no conflict of interest.
## 3 Research Methodology
### Global Model Aggregation  
Following the use of DNN approaches, several local models were trained. The weights of these models were then aggregated and consolidated worldwide using the Federated Averaging method. It is a collaborative method for merging model weights without directly accessing specific variables. The system employs federated learning, storing individual models on several devices. The training process is distributed, indicating that several servers are utilized. The local models that have undergone training possess weights. Federated Averaging has been employed to merge the data, resulting in a more robust and widely applicable global model.  
1. Federated Averaging: following the individual clients' training and updating of the model, the server combines the models and calculates the new global model using Equation below: \[\text{global model}\leftarrow\sum_{i=1}^{N}\left(\frac{N_{i}}{N}\times\text{ local model}_{i}\right)\] (6) This technique ensures that the global model mirrors the cooperative developments from all local models, upholding high efficiency while conserving data secrecy. In addition, the loss function can be reduced by handling the optimisation complexity given in the Equation below: \[p=\min_{w_{p}\in P}\frac{1}{p}\sum_{p=1}^{p}w_{p}^{\prime}\] (7)  
The aggregated global model influences the distributed character of IoT devices, compounding their insights without conciliating the distinct data secrecy of every device.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## Abbreviations  
\begin{tabular}{l l}
**Symbol** & **Description** \\ \(\mathbb{T}\) & Set of trains, \(\tau_{m}\), \(\tau_{n}\in\mathbb{T}\) \\ \(\mathbb{J}\) & Set of junctions, \(J_{m}\), \(J_{n}\in\mathbb{J}\) \\ \(\mathbb{S}\) & Set of stations, \(S_{m}\), \(S_{n}\in\mathbb{S}\) \\ \(\mathbb{R}\mathbb{J}\)\({}_{n}\) & Set of routes at junction \(J_{m}\), \(r_{m}^{J_{m}}\), \(r_{n}^{J_{m}}\in\mathbb{R}^{J_{m}}\) \\ \(t_{\tau_{m}}^{L}\) & The original timetable of train \(\tau_{m}\) at junction \(J_{m}\) \\ \(t_{\tau_{m}}^{L}\) & Decision variable: the rescheduled timetable of train \(\tau_{m}\) at junction \(J_{m}\) \\ \(t_{\tau_{m}}^{S_{m},dep}\) & The original departure time of train \(\tau_{m}\) at station \(S_{m}\) \\ \(t_{\tau_{m}}^{S_{m},arr}\) & The rescheduled departure time of train \(\tau_{m}\) at station \(S_{m}\) \\ \(t_{\tau_{m}}^{S_{m},arr}\) & The rescheduled arrival time of train \(\tau_{m}\) at station \(S_{m}\) \\ \(t_{\tau_{m}}^{S_{m}}\) & The passing time of train \(\tau_{m}\) at control boundary \\ \(t_{\tau_{m}}^{J_{m}}\) & The traveling time of train \(\tau_{m}\) from control boundary to junction \(J_{m}\) \\ \(t_{\tau_{m}}^{L}\) & The traveling time of train \(\tau_{m}\) from junction \(J_{m}\) to junction \(J_{n}\) \\ \(H_{\tau_{m},\tau_{n}}\) & The headway between train \(\tau_{i}\) and train \(\tau_{j}\) \\ \(D_{\tau_{m}}^{S_{m}}\) & The dwell time for train \(\tau_{m}\) at station \(S_{m}\) \\ \(C_{\tau_{m},\tau_{n}}^{S_{m}}\) & The minimum connection time between train \(\tau_{m}\) and \(\tau_{n}\) at station \(S_{m}\) \\ Decision variable: 1 if train \(\tau_{m}\) is assigned former than train \(\tau_{n}\) at junction \(J_{m}\), 0 otherwise \\ \(t_{\tau_{m},\tau_{n}}^{J_{m}}\) & Indicator variable: 1 if route \(r_{m}\) and \(r_{n}\) have conflict at junction \(J_{m}\), 0 otherwise \\ \(t_{\tau_{m}}^{S_{m}}\) & Indicator variable: 1 if train \(\tau_{m}\) has a scheduled dwell at station \(S_{m}\), 0 otherwise \\ \(t_{\tau_{m},\tau_{n}}^{J_{m}}\) & Indicator variable: 1 if train \(\tau_{m}\) and \(\tau_{n}\) have a connection at station \(S_{m}\), 0 otherwise \\ \(t_{\tau_{m},\tau_{n}}^{J_{m}}\) & Indicator variable: 1 if the passing sequence of train \(\tau_{m}\) and \(\tau_{n}\) has strong coupling at junction \(J_{m}\) and junction \(J_{n}\), 0 otherwise \\ \end{tabular}
## 2 Materials and Methods
### A-DInSAR Processing  
Available SAR data consisted of 113 IW-SLC SAR images acquired by Sentinel-1A/B satellite in C-Band (5.4 cm wavelength) during the period January 2018-February 2020 and descending orbit (Table 1). For A-DInSAR processing (Figure 2), 2 different approaches have been used: (i) PSIG chain (Persistent Scatterer Interferometry of the Geomatics Division), a robust software developed by CTTC [56; 57; 58] and (ii) GEP platform (Geohazard Exploitation Platform), an unsupervised service [59; 60] with P-SBAS algorithm implemented [1].  
Figure 2: Flow diagram of the procedure and tools used in this work.  
The general procedure of A-DInSAR processing consisted of: (i) co-registration of SAR images and generation of wrapped interferograms; (ii) estimation of residual topographic errors and line-of-sight (LOS) mean velocity over a network of PS, according to amplitude and coherence threshold criteria; (iii) interferogram phase unwrapping and assessment of deformation time series; (iv) estimation and filtering of the atmospheric component using a low pass spatial (LPS) filter in PSIG. In GEP, an atmospheric phase screen (APS) filter was included in the algorithm; (v) geocoding and implementation of LOS mean deformation velocity map (mm year\({}^{-1}\)) and deformation time series (mm) in a Geographical Information System (GIS). The main processing parameters of both approaches are shown in Table 1.  
With respect to the processing conducted in this research, the A-DInSAR dataset consisted of large regional processing of the whole of Central Asturias using both approaches. The reference points were located in the city of Oviedo, which was located to the South and outside our study area. The geographical location of the reference points is shown in Table 1. Afterward, in order to focus only on the coastal area, a buffer of 3 km from the coastline was created (Figure 1), and only the PS measured into this study area have been considered.  
Concerning measured PS, the sign indicated a movement towards the satellite if the value was positive; the value was negative if the movement was away from the satellite [61]. In this work, since the Sentinel-1 SAR images used were acquired in descending orbit, the highest sensitivity will be obtained when terrain orientations were 278 degrees with respect to N and a slope of 55\({}^{\circ}\) approximately.
## 1 Introduction
The study's findings showed a decrease in tensile strength of coir fiber-PP composites with increasing fiber content. The flexural and impact strength of the fabricated composites increased with increasing fiber loading. The highest flexural, tensile, and impact strength values recorded in this study were 57 MPa (25/75 wt %), 31.3 MPa (10/90 wt %), and 73 J/m (25/75 wt %), respectively. Moreover, the microstructure of the composite revealed fewer fiber agglomerations, microvoids, and stronger fiber-matrix interfacial adhesion. In another study, [PERSON] et al. [37] produced two natural-fiber-reinforced polymer composites to assess their interfacial adhesion characteristics and critical length. On one end, the researchers incorporated untreated coir fiber (30 vol%) in the matrices of epoxy resin, and on the other end, pineapple leaf fiber (PALF) (30 vol%). The findings showed that the critical length of coir fibers was 70% higher than those of PALF. However, the interfacial strength of coir fibers in the epoxy resin matrix compared to PALF was 3.5 times smaller. In terms of tensile strength, the Epoxy/30 vol% coir fiber composite gave a strength value of 28.7 \(\pm\) 11.0 MPa, while the Epoxy/30 vol% PALF composite gave 86.4 \(\pm\) 16.9 MPa. The study conducted by [PERSON] et al. [14] highlighted the need to integrate natural fibers in the matrices of metakaolin-based geopolymers for improved mechanical properties. The current study on coir-fiber-reinforced geopolymer composite will provide information on the performance of coir fiber as a reinforcing agent in the matrices of metakaolin-based geopolymer for sustainable building applications. Furthermore, since the trend in our today's world is the promotion of sustainable materials, the environmental pollution and sanitation problems resulting from the disposal of coconut husks will be alleviated. Moreover, the use of coir fibers in metakaolin-based geopolymers for building applications may serve as a benchmark for material scientists and engineers to develop other coir-fiber geopolymer composite products with improved properties for use in other sustainable applications.
## 3 Methodology
### Landslide Control Parameters
#### 3.1.1 Parameters Related to the Terrain  
Lithological units significantly influence the location of landslide phenomena [31], and lithological characterization is one of the most relevant aspects in compiling an inventory of terrain-associated control parameters. The lithological units used in the study were obtained from the geological mapping carried out by the Spanish Geological Survey [32].  
Other parameters in the control of landslide triggering include geomorphological factors, such as terrain morphology, slope, curvature, and roughness (Table 1).  
Information about the overall curvature of the terrain in a specific direction is obtained using three measures of curvature as follows: total, perpendicular, and transverse concavity/convexity. The total concavity/convexity helps identify areas prone to landslides. Perpendicular concavity/convexity describes the curvature in relation to a vertical line,  
Figure 2: Location map of the study area showing the different control parameters of the study.  
helping to understand how local terrain features influence slope stability. Transverse cavity/convexity refers to the lateral curvature, allowing the evaluation of how terrain geometry affects the propagation and direction of landslides.  
Roughness in landslide analysis refers to variations in the slope. It provides key information about local topographic characteristics and how changes in the slope can affect slope stability and its propagation. Areas with higher roughness tend to have irregularities in the terrain, which increases the likelihood of landslides. Moreover, roughness can influence the velocity and direction of failures.  
It is well known that all of these terrain factors significantly influence the distribution, typology, and mechanisms of slope movements [33]. Therefore, they were incorporated into the actual study using several parameters derived from the combined use of the ArcGIS software with the digital elevation model from the Spanish Geographic Institute [34].
## 2 Materials and Methods
### Design Phase
#### 2.2.2 Problem Definition
**Constraints:**  
* Any pair of operations (i\({}_{j}\), i\({}_{j^{\prime}}\in\mathrm{O_{i}}\)) with j \(<\) j\({}^{\prime}\), i\({}_{j^{\prime}}\) can only start to be transported after the transportation of i\({}_{j}\) has completed;
* Each operation (i\({}_{j}\in\mathrm{O_{i}}\), r \(\in\) {1,..., n}) must be processed on exactly one AGV out of the set of possible AGVs (A\({}_{i}\subseteq\) A) for that operation;
* Each AGV can transport only one operation at a time, and each operation can be transported by at most one machine at a time;
* The transportation time of an operation i\({}_{j}\in\mathrm{O_{i}}\) of a job J\({}_{i}\in\) J on an eligible AGV (A\({}_{k}\in\mathrm{A_{i}}\)) is represented by t\({}_{j}^{k}\in\) N\({}^{+}\);
* The set of eligible operations of an AGV (A\({}_{k}\in\) A) is E\({}_{k}\) = {i\({}_{j}\) | J\({}_{i}\in\) J\({}_{j}\)\(\in\) O\({}_{i}\), A\({}_{k}\in\) A\({}_{i}\)}. **Objective:**  
* C\({}_{j}\) is the completion time of an operation (i\({}_{j}\in\mathrm{O_{i}}\)) of job J\({}_{i}\in\) J in a schedule;
* C\({}_{i}\) is the completion time of job J\({}_{i}\in\) J. A job is considered to be completed once all its operations are completed, for instance, C\({}_{i}\) = C\({}_{i_{qi}}\) for all i \(\in\) {1,..., n}. The main objective is to minimize the makespan. \[\mathrm{C_{max}}=\max_{i\in\{1,\,\ldots,\,n\}}\mathrm{C_{i}} \tag{5}\]
# Parameter Study for the Ice Adhesion Centrifuge Test
## 3 Results  
The results of this study are displayed in the following subsections according to the main questions this study focused on. These include the durability determination of the test surfaces as a pre-requisite for the result interpretation (Section 3.1), detailed assessment of the gained data per scenario and material (Section 3.2), result comparison for different ice types (Section 3.3) and for different ice adhesion test parameter set-ups (Section 3.4), and finally, the interdependencies between the surface properties and ice adhesion test results (Section 3.5).  
Quantitative ice adhesion results were derived from tests with 100% adhesive failure of the ice. This was observed for the materials Standox, PUR C25, and PTFE-tape. Partial detachment of the ice or even no ice shedding occurred for the primer coating in all test set-ups. The ice adhesion to the primer surface was higher than the maximum shear strength of the test (at a maximum centrifuge speed of 10,000 rpm: >231 kPa for 2 g of ice and >347 kPa for 3 g ice). This was evaluated as a qualitative result only.  
For tests with static ice, one data point per test day and used sample was obtained. A maximum of 10 tests per day were conducted. For impact ice, a maximum of six samples per working day were tested in a twofold measurement. At least three independent test days per parameter set and material were conducted.