esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The Si wafer was a two-inch P-type Si (100) substrate for the deposition of SiN\({}_{\mathrm{x}}\), cleaned with diluted hydrofluoric acid for 2 min to remove native oxides. After cleaning, the substrate was loaded in the RPALD chamber. At first, SiN\({}_{\mathrm{x}}\) films were deposited by RPALD with CSN-2 as the precursor and N\({}_{\mathrm{2}}\) plasma as the reactant (two-step). Precursors were heated at 60 \({}^{\circ}\)C using a heating jacket to obtain sufficient vapor pressure and Ar (50 sccm) was used as the carrier gas in the line during the precursor dosing and purging step. The delivery lines were heated to 70 \({}^{\circ}\)C to prevent precursor condensation. N\({}_{\mathrm{2}}\) plasma was generated by inductively coupled plasma (ICP) generated at a radio frequency of 13.56 MHz (Figure 1a). Deposition temperature was set in a wide range from 100 \({}^{\circ}\)C to 600 \({}^{\circ}\)C and the plasma power was fixed at 100 W. The recipe was selected as a standard which is composed of five stages (Figure 1b): CSN-2 dose time (3 s), precursor purge time (30 s), N\({}_{\mathrm{2}}\) reactant gas injection time (4 s), N\({}_{\mathrm{2}}\) plasma exposure time (20 s), and purge time (30 s). As shown in Figure 1c, we have developed the three-step process mentioned above, which adds H\({}_{\mathrm{2}}\) gas injection (4 s) and an H\({}_{\mathrm{2}}\) plasma exposure step (10, 20, and 30 s time variables) between the precursor purge and N\({}_{\mathrm{2}}\) gas injection step. In the previous ALD cycle, the precursor delivery lines were purged with 700 sccm of Ar to remove byproducts and residual gas. A base pressure in the reactor chamber of \(\sim\)10\({}^{\circ}\) totr was obtained using a turbo-molecular pump.  
The thickness and refractive index of the deposited SiN\({}_{\mathrm{x}}\) films were measured by spectroscopic ellipsometry (SE) using a Nano-View SE MG-1000 operated at an incident angle of 70\({}^{\circ}\) (1.5-5.0 eV). To investigate the surface morphology of SiN\({}_{\mathrm{x}}\) thin films, atomic force microscope (AFM; Park Systems, XE-7) was used. The chemical compositions of the SiN\({}_{\mathrm{x}}\) films were investigated with auger electron spectroscopy (AES). The chemical bonding state was determined by X-ray photoelectron spectroscopy (XPS) using a PHI 700 Xi with Mg K\(\alpha\) X-ray source (E = 1.254 keV). Film wet etch rates were evaluated in a diluted HF solution (H\({}_{\mathrm{2}}\)O / HF = 100:1). Film thickness and step coverage were examined by transmission electron microscopy (TEM).  
Figure 1: Schematic images of the (**a**) remote plasma atomic layer deposition (RPALD) system and process sequence (**b**) without H\({}_{\mathrm{2}}\) plasma and (**c**) with H\({}_{\mathrm{2}}\) plasma.
## 3 The ADVOCATE Framework  
The ADVOCATE framework [12; 13; 14] aims to meet the main requirements of the GDPR with regards to obtaining and managing data subjects' consents. According to the GDPR, consents are data subject's wishes that must be freely given, specific and unambiguous. Moreover, prior to obtaining data subjects' consents, data controllers must appropriately inform them in a transparent manner about (a) personal data they are willing to manage and the sources thereof, (b) the purposes, time periods and legal basis of the processing, (c) the entities that will process it, and (d) recipients or categories of data recipients. ADVOCATE aims to provide users with the means to manage their consents and shape their personal data disposal rules/policies.  
An ADVOCATE cloud service approach capable of handling personal data collected by IoT sensors deployed in smart cities and e-health environments is illustrated in Figure 1.  
In order to provide immutable versioning control, identifying the latest consents as well as periods that specific consents were valid in an undisputable manner, the ADVOCATE platform uses blockchain technology with smart contracts. This procedure is illustrated in Figure 3.  
An interaction of core ADVOCATE components during the introduction of new consents, is presented in Figure 2.  
Figure 1: ADVOCATE framework conceptual architecture.  
Figure 3: The steps followed by the consent notary component to ensure integrity, non-repudiation, and versioning of given contents.  
Figure 2: Interaction of ADVOCATE components during the introduction of new consents.  
Finally, the intelligence policies analysis component of the ADVOCATE framework is responsible for performing the necessary analysis for the user's data disposal policy, via an Intelligent Policies Analysis Mechanism (IPAM), to detect conflicting or contradictory rules/policies of a user's consents.  
The implementation of IPAM, that is presented in this work, is based on an advanced simulation of the problem of personal data leakage from smart entertainment devices. The problem is modeled using Fuzzy Cognitive Maps (FCMs), which is a technique that belongs to the neuro-fuzzy systems and can be used to solve decision-making problems and model and simulate complex systems [26], FCMs provide causal acquisition and representation of knowledge, and they can support the causal knowledge reasoning process. The identification process of conflicting rules is accomplished using Extreme Learning Machines (ELMs), which is a very fast and efficient type of Single-Hidden Layer Feed-Forward Neural Network (SHLFFNN) where the parameters of hidden nodes can be randomly assigned and never updated or can be inherited from their ancestors without being changed.
# Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques
## 4 The Study
### Experiments
#### 4.1.2 Dice Menu for Navigation  
To compare traditional design elements with a more immersive counterpart for navigating menu elements (RQ 2), we have prepared a setting with three different kinds of hand menus (cf. Figure 6). A menu floating in the space in front of the user allows them to switch the active hand menu. The different variations share a common position on the user's lower arm. Each particular hand menu is described in detail below.  
The rotating-cube menu is a special 3D hand menu we have designed and implemented prototypically. Its main design element is a three-dimensional cube. The cube floats on top of the user's hand and can be rotated around its y-axis using different hand positions. In our industrial setting, the controller can navigate through warning messages, for example. While rotating, the warning messages are continuously exchanged on the side of the cube facing away from the controller. This allows the cube to display more than four elements in its function as a navigation element. To show the cube, the application needs to recognize that the user holds his or her hand flat, and to rotate the cube, the user has to lift her or his thumb.  
The scrollable list menu mostly uses premade elements from Microsoft's Mixed Reality Toolkit, e.g., a _GridObjectCollection_ arranges the buttons automatically in the needed structure and a _ScrollingObjectionCollection_ handles the user input. When the user points a finger at the list, she or he can move the list up and down by moving the finger.  
The click menu displays content blockwise using a tile-based layout. The user can navigate to the next or previous content block by using a forward or a backward button, respectively. It is technically the simplest solution.  
In this scenario, the test person is asked to navigate through a set of warning messages and select two given ones in succession. In Figure 6 the hand menus are shown in front of a robotic arm in the SmartFactoryOWL. The warning messages can refer to this industrial demonstrator. The test person has to repeat the selection of the given warning messages three times using the previously described menus, each containing the same content.  
In order to answer RQ 2, we asked the following question to the test persons:  
* _Which menu was the most user-friendly to you?_  
Figure 6: View of the three variations for our hand menu evaluation. Upper left: click menu; lower left: scroll menu; right: cube menu.
# PMG--Pyramidal Multi-Granular Matching for Text-Based Person Re-Identification
## 4 Experiment Details
### Experiment Setup
#### 4.1.2 Dataset and Evaluation Metrics  
To evaluate the performance of the proposed PMG method, we conduct extensive experiments on two text-based person Re-ID datasets: CUHK-PEDES [10] and RSTPRedi [27]. CUHK-PEDES is currently the most popular dataset used for text-to-image person Re-ID and has become the de facto benchmark for text-based person retrieval tasks. Compared with CUHK-PEDES, the newly conducted RSTPRedi is more adaptable to real application scenarios in that images were caught by different cameras with complex scene transformations and backgrounds in various periods of time [27]. (1) CUHK-PEDES: We follow the official data split paradigm [10] for the training, validating, and testing of PMG. Specifically, the training set consists of 34,054 images representing 11,003 individuals, along with 68,126 query sentences. The validation set comprises 3078 images, 1000 individuals and 6158 textual descriptions. The testing set contains 3074 images, 1000 individuals and 6156 descriptions. (2) RSTPRedi: The RSTPRedi dataset used in this study consists of 20,505 images and 41,010 descriptions showcasing a total of 4101 individuals. Following the data split approach in [27], the whole dataset is divided such that 3701 identities are assigned to the training set, 200 identities to the validation set, and 200 identities to the testing set. For each identity in all sets, 5 images along with 10 annotated textual descriptions are attached to represent the corresponding identity.  
We evaluate the performance using Top-1, Top-5, and Top-10 accuracies, which are the most popular and crucial metrics in person Re-ID. Given a query sentence, all images in the test set are ranked by their similarities with the query text, and Top-k images with the highest scores are selected as candidates. If the Top-k candidate images contain at least one image of the targeted pedestrian, the query is called to be successful. Then, the Top-k accuracy is calculated as the ratio of successful queries to the total number of test queries. Figure 4 shows an example of Top-5 text-based person Re-ID results.
# Adaptive Piecewise Poly-Sinc Methods for Ordinary Differential Equations
## 2 Background
### Poly-Sinc Approximation
#### 2.1.2 Conformal Mappings and Function Space  
We introduce some notations related to Sinc methods [23; 24; 30]. Let \(\varphi\,:\,D\to D_{d}\) be a conformal map that maps a simply connected region \(D\subset\mathbb{C}\) onto the strip  
\[D_{d}=\{z\in\mathbb{C}:|\operatorname{Im}(z)|<d\},\]  
where \(d\) is a given positive number. The region \(D\) has a boundary \(\partial D\), and let \(a\) and \(b\) be two distinct points on \(\partial D\). Let \(\psi=\varphi^{-1},\psi\,:\,D_{d}\to D\) be the inverse conformal map. Let \(\Gamma\) be an arc defined by  
\[\Gamma=\{z\in[a,b]\,:\,z=\psi(x),\,x\in\mathbb{R}\},\]  
where \(a=\psi(-\infty)\) and \(b=\psi(\infty)\). For real finite numbers \(a\), \(b\), and \(\Gamma\subseteq\mathbb{R}\), \(\varphi(x)=\ln((x-a)/(b-x))\) and \(x_{k}=\psi(kh)=(a+be^{kh})/(1+e^{kh})\) are the Sinc points with spacing \(h(d,\beta_{\rm s})=\left(\frac{\tau d}{\beta_{\rm s}N}\right)^{1/2},\,\beta_ {\rm s}>0\)[30; 40]. Since points can be also generated for semi-infinite or infinite intervals. For a comprehensive list of conformal maps, see [24; 30].  
We briefly discuss the function space for \(y\). Let \(\rho=e^{\varphi}\), \(\alpha_{\rm s}\) be an arbitrary positive integer number, and \(\mathbb{L}_{\alpha_{\rm s},\beta_{\rm s}}(D)\) be the family of all functions that are analytic in \(D=\varphi^{-1}(D_{d})\) such that for all \(z\in D\), we have  
\[|y(z)|\leq C\frac{|\rho(z)|^{\alpha_{\rm s}}}{[1+|\rho(z)|]^{\alpha_{\rm s}+ \beta_{\rm s}}}.\]  
We next set the restrictions on \(\alpha_{\rm s},\beta_{\rm s}\), and \(d\) such that \(0<\alpha_{\rm s}\leq 1\), \(0<\beta_{\rm s}\leq 1\), and \(0<d<\pi\). Let \(\mathbb{M}_{\alpha_{\rm s},\beta_{\rm s}}(D)\) be the set of all functions \(g\) defined on \(D\) that have finite limits \(g(a)=\lim_{z\to a}g(z)\) and \(g(b)=\lim_{z\to b}g(z)\), where the limits are taken from within \(D\), and such that \(y\in\mathbb{L}_{\alpha_{\rm s},\beta_{\rm s}}(D)\), where  
\[y=g-\frac{g(a)+\rho\,g(b)}{1+\rho}.\]The transformation guarantees that \(y\) vanishes at the endpoints of \((a,b)\). We assume that \(y\) is analytic and uniformly bounded by \(\mathcal{B}(y)\), i.e., \(|y(x)|\leq\mathcal{B}(y)\), in the larger region  
\[D_{2}=D\cup_{t\in(a,b)}B(t,r),\]  
where \(r>0\) and \(B(t,r)=\{z\in\mathbb{C}:|z-t|<r\}\).
## 3 Results and Discussion
### Mathematical Modelling  
Based on the equations described in Section 2.2, the kinetic constants \(\mu_{MAX}\) and \(X_{MAX}\) of the logistic model were determined both for the fermentation process conducted without the pH control and under controlled conditions. Table 2 shows the values obtained in comparison with the experimental ones, while Figure 6 shows the trends of the bacterial growth rates experimentally evaluated. Finally, Figure 7 shows the comparison between the experimental trends and those estimated using the logistic model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Kinetic Constants**} & \multicolumn{2}{c}{**Without pH Control**} & \multicolumn{2}{c}{**Under pH Control**} \\ \cline{2-5}  & **Experimental** & **Estimated** & **Experimental** & **Estimated** \\ \hline \(\mu_{MAX}\), h\({}^{-1}\) & 0.4738 & 0.4905 & 0.3787 & 0.3025 \\ \(X_{MAX}\), CFU/mL & \(3.43\times 10^{8}\) & \(5.20\times 10^{8}\) & \(2.26\times 10^{8}\) & \(2.06\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic constants of the Verhulst (or logistic) model.  
Figure 5: ANOM results for the bacterial growth (**left side**) and lactic acid production (**right side**).  
Figure 6: Bacterial growth rate as a function of time and pH control.  
As can be seen from Figures 6 and 7, the logistic model is able to accurately describe the bacterial growth curve during the exponential phase only, with an \(R^{2}\) of about 0.97 for both conditions studied. On the other hand, the data reported in Table 2 suggest that the mathematical model overestimates the maximum concentration of biomass regardless of the pH control, while there is a different trend for the maximum growth rate, which is overestimated without pH control and underestimated under pH control.  
Despite the good predictive capabilities shown in the exponential phase, the model loses effectiveness outside this range. This may be due to the small experimental data set. It is therefore necessary to resort to more accurate methods that consider the substrate concentration and/or the inhibition effect due to fermentation saturation, or, as described in the following section, to expert methods capable of managing this lack of information and propose innovative solutions which allow the combination of parameters that are not commonly used in traditional kinetic models.
## 4 Results and Discussion
#### Discussion--Table Summary and Research Gap
One major limitation is the fragmented treatment of cost components, with most studies focusing on either construction or environmental costs, rather than integrating all cost categories into a unified LCCA framework. This limits the ability to holistically assess the long-term economic and sustainability-based impacts of different methods. Additionally, most existing analyses are context-specific and fail to account for variability across project conditions, such as pipe diameters, geographic locations, and soil types. While trenchless methods like CIPPRT have been proven to be effective in urban settings, their performance in rural or environmentally sensitive areas remains underexplored. Furthermore, there is limited empirical validation of these analytical tools in real-world scenarios, and their scalability for large-scale projects has not been rigorously tested. Addressing these gaps requires the development of comprehensive LCCA frameworks and their validation under diverse conditions to improve the reliability of cost predictions and decision-making. Another significant gap lies in the application of machine learning to enhance predictive modeling for LCCA. Although machine learning has been used in other engineering domains to predict costs and optimize decision-making, its integration into the comparative analysis of trenchless technologies and traditional methods is minimal. Studies often lack robust frameworks to evaluate the potential of ML in incorporating dynamic variables, such as inflation rates, material degradation, and evolving environmental regulations, into the LCCA. Despite promising advancements, the research reveals gaps in applying ML comprehensively across diverse geographic contexts and large-scale projects. Further studies are needed to validate these models' scalability and ensure their integration into standardized LCCA methodologies for sewer rehabilitation projects.
## 3 Results
### Influence of the Radial Interference  
To quantify the influence of the radial interference (8) and of the type of interference fit, three different values of the interference closure were selected for the reference interference fit 200H7/s6 (\(\delta\) = 38 \(\upmu\)m, \(\delta\) = 56 \(\upmu\)m, and \(\delta\) = 75 \(\upmu\)m) and, on the other hand, two additional interference fits recommended by the ISO standard for fits and tolerances were chosen causing moderate press (200H7/v6) and severe press (200H7/x6). Previous studies demonstrated that these fits generate different levels of plasticity (local and generalized) in conventional hubs manufactured with a steel AISI 1085 [27]. To reveal the influence, those interferences fit, they were simulated considering two different contact ring thicknesses (\(\gamma\) = 0.125 and \(\gamma\) = 0.250), both included within the recommended range discussed in the previous section. This way, FE simulations reveal the stress state whose axial distributions of radial, tangential, and von Mises stress at the interface are shown in Figure 11 for each case of study.  
Figure 11: Axial distributions at the interface of: (**a**) radial stress, (**b**) tangential stress, and (**c**) von Mises stress for diverse levels of radial interference (\(\gamma\) = 0.125).  
As can be expected, the stress state is higher as the radial interference (8) is increased, without causing changes in the shape of the stress distributions. For the case of the moderate press fit (200H7/v6), the stress concentration in a conventional hub (without a contact ring) is so high that it causes a plastic zone at the edge of the hub, as was revealed in previous studies [27]. However, in the case of an interference fit using a contact ring, the reduction in the stress concentration is high enough for making the plastic strains not appear anywhere in the hub, neither at the interface of the shaft/contact ring nor the interference contact of the ring/hub. In the case of the severe fit (200H7/x6), a similar effect can be observed. In this case, generalized plasticity is generated at the interface of the shaft/hub for the conventional hub case [27], whereas, in the case of using a contact ring, again, the reduction in the stress state is high enough to make plastic strains not appear anywhere in the hub since the von Mises stress is always lower than the material yield strength. It must be taken into account that the existence of plastic strains in the interface of the shaft/hub can compromise the effective performance of the fit since this can cause vibrations or modify the contact pressure and hence the maximum transmitted torque.
## 2 Related Work
The authors in [14] presented an encryption-based data sharing system using blockchain. The system maintained the integrity, privacy, and non-repudiation of data and achieved better encryption. However, it does not perform well for decryption. The authors in [15] proposed a Data security Sharing and Storage system based on Consortium Blockchain (DSSCB). The system achieved efficiency; however, authentication and security sharing do not perform better in real time evaluation. The authors in [16] proposed an Electronic Health Records (EHRs) system to address issue of sensitive medical data leakage and to ensure secure data access. Therefore, the real-world implementation of the system is not provided. The authors in [18] presented a bubble of trust mechanism to provide trustworthiness and confidentiality of data, identification and authentication of devices. The system is efficient in terms of cost; however, the communication between nodes in a bubble is not controlled. Furthermore, compromised devices are not eliminated. The authors in [19] presented a blockchain-based identity management system that works on a key management protocol based on Self Certified public Key-Based System (SCKBS). The system ensures several security requirements such as: authentication, confidentiality, and auditability. However, access mechanisms are not used efficiently. The authors in [20] presented a blockchain-based smart toy data exchange system to provide decentralized, secure, trusted, fair, and reliable data exchange. The system is efficient and ensures transparency and data confidentiality. Therefore, the data delivery time that is increased due to logs and high throughput are not handled. The authors in [21] presented an access control system based on eXtensible Access Control Markup Language (XACML) policies. The system ensures auditability of resources through generalized access control. However, privacy and auditability are not provided properly. Furthermore, permissioned blockchain used in the system does not work well. The authors in [22] proposed a blockchain-based mutual authentication system for Industry 4.0. The system aimed to provide efficient and secure access control. However, real implementation of the system in smart factory is not presented. Furthermore, the authors in [23; 24] addressed security issues in the networks. To overcome the issues, the authors proposed a blockchain-based trusted system for nodes' routing and recovery. The insecurity and untrustworthiness of data is also identified by the authors in [25; 26; 27; 28; 29]. To overcome the issues, blockchain-based systems are presented. The systems also provide the efficient use of devices data. Additionally, the authors in [30; 31; 32] presented a blockchain-based system to achieve trustfulness and authentication of data in the networks. The related work is summarized in Table 2.
## 5 Experimental Results
### Experimental Setups for Data  
To conduct the experiments, we divided the dataset into smaller sets for training, validation, and testing, and the details are presented in Table 2. Specifically, the number of ratings for the training set, validation set, and testing set of each dataset are as follows:  
* For the MovieLens dataset, there are 62,211 ratings for training, 3110 ratings for validation, and 12,442 ratings for testing;
* For the Toys and Games dataset, there are 54,482 ratings for training, 2724 ratings for validation, and 10,896 ratings for testing;
* For the Electronic dataset, there are 64,378 ratings for training, 3218 ratings for validation, and 12,876 ratings for testing;
* For the Video and Games dataset, there are 79,016 ratings for training, 3950 ratings for validation, and 15,803 ratings for testing.