esa-sceva/satcom-chunk-collection huggingface dataset:
# User Experience of Older People While Using Digital Health Technologies: A Systematic Review
## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline \hline
**Components** & **Description** \\ \hline \multirow{4}{*}{User Interface} & This is the front-end component accessible through \\  & web or mobile applications that allows users to \\  & interact with the digital health system. \\  & This component includes various sensors and devices \\  & that collect health-related data, such as wearable \\  & fitness trackers, medical devices, and IoT sensors. \\  & It manages user accounts, preferences, and personal \\  & information securely. \\  & Ensures secure access to the system by verifying user \\  & identities and granting appropriate permissions. \\  & Stores the collected health data securely, often in \\  & compliance with healthcare data privacy regulations. \\  & This component processes and analyzes health data to \\  & derive insights, identify patterns, and provide \\  & personalized recommendations. \\  & Facilitates real-time communication between \\  & healthcare providers and users through video calls, \\  & chat, or other telehealth services. \\  & Utilizes machine learning and AI algorithms for tasks \\  & like disease diagnosis, rick prediction, and treatment \\  & recommendation. \\  & Stores and manages users’ electronic health records, \\  & including medical history, diagnoses, medications, \\  & and treatments. \\  & Sends alerts, reminders, and notifications to users for \\  & medication, appointments, or other \\  & health-related activities. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General system components of digital healthcare technologies. Given this background, the present study aimed to review and introduce the questionnaires frequently used to assess the UX of older people when using digital healthcare technologies and to explore the contents of original or customized questionnaires. The outcomes of this study are expected to provide guidance for designing and implementing future research endeavors. Moreover, these findings are anticipated to be useful for people in the industry to assess the UX of older people. This, in turn, will significantly contribute to the development of user-centric healthcare technologies.
## 3 Proposed Method
### Pruning Routing Tables  
In the context of route link metric values, pruning is a process of eliminating links that have quality values greater than or equal to a given threshold. The threshold can be determined in different ways, such as calculating the median values of all available links to the destination and discarding links with values above or below this threshold. This is done to minimise the number of links to be considered, as well as to reduce the computational complexity of the AHP algorithm. In this article, pruning was carried out by selecting the best four values of each link metric and applying the decision making algorithm to calculate the best quality route to a destination.
# Answer Set Programming for Regular Inference
## 1 Introduction
Their work, in turn, was based on the idea of transformation from DFA identification into graph coloring, which was proposed by [PERSON] and [PERSON] [13]. [PERSON] et al. [14] proposed BFS-based symmetry breaking predicates, instead of the original max-clique predicates, which improved the translation-to-SAT technique. The improvement was demonstrated with the experiments on randomly generated input data. The core idea is as follows. Consider a graph \(G\), the vertices of which are the states of an initial automaton and there are edges between vertices that cannot be merged. Finding minimum-size DFA is equivalent to a graph coloring with a minimum number of colors. The graph coloring constraints, on the other hand, can be efficiently encoded into SAT according to [PERSON] [15]. In a more recent approach, satisfiability modulo theories (SMT) are explored. Suppose that \(A=(\Sigma,Q=\{0,1,\ldots,K-1\},s=0,F,\delta)\) is a target automaton and \(P\) is the set of all prefixes of \(S_{+}\cup S_{-}\). An SMT encoding proposed by [PERSON] et al. [16] uses four functions: \(\delta\colon Q\times\Sigma\to Q\), \(m\colon P\to Q\), \(\lambda^{A}\colon Q\to\{\bot,\top\}\), \(\lambda^{T}\colon S_{+}\cup S_{-}\to\{\bot,\top\}\), where \(\{\bot,\top\}\) represents logical \(\{\text{false},\text{true}\}\), and the following five constraints:  
\[m(\varepsilon)=0,\]  
\[x\in S_{+}\iff\lambda^{T}(x)=\top,\]  
\[\forall xa\in P\colon x\in\Sigma^{*},\,a\in\Sigma\quad\delta(m(x),a)=m(xa),\]  
\[\forall x\in S_{+}\cup S_{-}\quad\lambda^{A}(m(x))=\lambda^{T}(x),\]  
\[\forall q\in Q\quad\forall a\in\Sigma\quad\bigvee_{r\in Q}\delta(q,a)=r.\]  
They implemented the encodings using Z3 Py, the Python front-end of an efficient SMT solver Z3. This paper is organized into five sections. In Section 2, we present necessary definitions and facts originating from automata, formal languages, and declarative problem-solving. Section 3 describes our inference algorithm based on solving an AnsProlog program. Section 4 shows the experimental results of our approach and describes in detail all reference methods. Concluding comments are made in Section 5.
## 2 Backgrounds and Methods  
### Background: Rotary Position Embedding  
In transformer models, positional information needs to be provided in some way, which is typically achieved through positional encoding. The positional encoding used in models such as GPT-NeoX and LLaMA is the Rotary Position Embedding (RoPE). Given a position index \(m\in[0,c)\) and an embedding vector \(x:=[x_{0},x_{1},\ldots,x_{d-1}]^{T}\), where \(d\) is the dimension of the attention heads, RoPE defines the complex function as Equation (1):  
\[\mathbf{f}(\mathbf{x},m)=[(x_{0}+ix_{1})e^{im\theta_{0}},(x_{2}+ix_{3})e^{im \theta_{1}},\ldots,(x_{d-2}+ix_{d-1})e^{im\theta_{d/2-1}}]^{T} \tag{1}\]  
where \(i:=\sqrt{-1}\) denotes the imaginary unit and \(\theta_{j}=\mathit{base}^{-2j/d}\), where in RoPE, _base_ is typically set to 10,000. With RoPE, the calculation of self-attention scores is performed as Equation (2):  
\[a(m,n) = \mathrm{Re}\langle\mathbf{f}(\mathbf{q},m),\mathbf{f}(\mathbf{k},n)\rangle \tag{2}\] \[= \mathrm{Re}\bigg{[}\sum_{j=0}^{d/2-1}(q_{2j}+q_{2j+1})(k_{2j}-k_ {2j+1})e^{(m-n)\theta_{j}}\bigg{]}\] \[= \sum_{j=0}^{d/2-1}(q_{2j}k_{2j}+q_{2j+1}k_{2j+1})\cos((m-n)\theta _{j})+(q_{2j}k_{2j+1}-q_{2j+1}k_{2j})\sin((m-n)\theta_{j})\] \[=: a(m-n)\]  
Figure 1: Schematic representation of the angles learned by high-frequency and low-frequency dimensions during pre-training in SBA-RoPE. The subplots on the (**left**) and (**middle**) depict low-frequency and moderate-frequency dimensions, respectively, which did not cover the entire range of 0 to \(2\pi\) during pre-training, thus necessitating interpolation rather than extrapolation. The subplot on the (**right**) represents high-frequency dimensions, which thoroughly learned the entire range of 0 to \(2\pi\) during pre-training, enabling extrapolation.  
The value of \(a(m,n)\) depends only on the relative position \(m-n\), where \(\mathbf{q}\) and \(\mathbf{k}\) are query and key vectors. In Cartesian coordinates, RoPE can be written as Equation (3):  
\[\mathbf{f}(\mathbf{x}_{m},m,\theta_{j})=\begin{pmatrix}\cos{m\theta_{1}}&-\sin{ nm\theta_{1}}&0&0&\cdots&0&0\\ \sin{nm\theta_{1}}&\cos{m\theta_{1}}&0&0&\cdots&0&0\\ 0&0&\cos{m\theta_{2}}&-\sin{nm\theta_{2}}&\cdots&0&0\\ 0&0&\sin{m\theta_{2}}&\cos{m\theta_{2}}&\cdots&0&0\\ 0&0&0&0&\cdots&\cos{m\theta_{d/2-1}}&-\sin{nm\theta_{d/2-1}}\\ 0&0&0&0&\cdots&\sin{nm\theta_{d/2-1}}&\cos{m\theta_{d/2-1}}\end{pmatrix} \mathbf{x}_{m} \tag{3}\]  
It should be noted that this transformation of RoPE is equivalent to rotating \([x_{2i},x_{2(i+1)}]\) (where \(i\in[0,d/2-1]\)) in complex space. This is why this type of positional encoding is named Rotary Position Encoding, with the corresponding rotation angles being the focus of extrapolation research on out-of-distribution in longer texts.
## 2 Background
### Amazon Web Services
#### 2.3.1 AWS IoT Core  
AWS IoT core is the AWS platform for the internet of things. It allows devices to connect to the cloud and other AWS services. The architecture of AWS IoT core is described in Figure 1. The central component of the architecture is the message broker, which dispatches messages to and from things (i.e., IoT devices) and other AWS services and user applications. AWS IoT core supports MQTT and MQTT over Websockets, but clients can also publish messages on the broker using HTTP. A rules engine is available to automatically process messages and to generate triggers. Each IoT device has an associated \"shadow\" to keep track of its status, and shadows are stored in a corresponding registry. Various security and identity mechanisms are employed to guarantee secure communication between the broker and devices, including TLS data encryption [58] and secure authentication with X.509 certificates [59].
## 3 Methodology
### Model Configuration
#### 3.4.4 Bottlenecked Connection  
The original UNet architecture did not feature any direct connection between the input and the final layer, opting instead for a concatenation of the activation of the third layer and the activation of the last deconvolution layer [23]. Adding an even less processed pass-through could lead to better spatial awareness and improved performance. In this work, a bottlenecked connection is included, aiming at forcing the model to synthetize the input information by using a bottleneck within the residual block, which halves the number of features in the convolutional path of the block.
## 2 Materials and Methods
### Theory of Filtering over Vertical Horizontal (HOVE) Angular Grid
#### 2.3.3 Finding the Adjacent Points for Iterative Wedge Filtering  
The key point and the great difficulty of wedge filtering are finding the most meaningful neighbours for each point. Within a particular search area of the HOVE grid, all points are determinate which are behind the point to be examined. Therefore, all points with a smaller distance to the recording centre are excluded. Among the remaining points, the right and left sides are searched for the point (Figure 6) which has the lowest value according to the Equation (7):  
\[min_{l,re}=\Delta_{vert}+(1+Abs\ (\Delta_{horz}))^{fakt}+divi\times\Delta_{len} \tag{7}\]  
\(min_{l,re}\) is the minimum value; \(\Delta_{vert}\) is the difference of the vertical angle (from laser scanner); \(\Delta_{horz}\) is the difference of the horizontal angle (from laser scanner); _fakt_ is the factor for \(\Delta_{horz}\); _divi_ is the average HOVE raster resolution; and \(\Delta_{len}\) is the difference of length (from laser scanner).  
If in the HOVE grid the adjacent point is above the point to be examined (positive vertical difference), the formula value increases. If the neighbouring point lies below the point to be examined (negative vertical difference), the formula value decreases. By the factor, the influence ratio of the horizontal and vertical difference can be weighted. The distance to the point behind is also taken into account by the length difference, whereby the multiplication with the average raster resolution ensures the size adaption to the angle differences.  
Figure 6: Adjacent points for iterative wedge filtering.
# Color Brightness Recognition of Extremely Severe Amblyopia Children in an Indoor Environment
## 3 Results
### Recognition Results under Different Lighting and Distance Conditions
#### 3.1.7 Experimental Summary  
The study's findings within the natural light group revealed no significant differences among the six brightness levels of red and purple (\(p>0.05\)), whereas notable variations were observed for yellow, blue, and green. In the artificial light group, all six brightness levels of red, yellow, blue, and green exhibited significant differences, mirroring the patterns seen with purple in the natural light group. In the natural light group, lower brightness levels of red, yellow, blue, and green were associated with higher scores, whereas higher brightness levels received lower scores. Purple displayed a unique pattern in the natural light group, with brightness level 5 attaining the maximum score and brightness level 6 the minimum score. No significant differences were found in the weighted scores when comparing the natural light and artificial light groups. Collectively, these findings suggest that indoor lighting conditions exert a negligible influence on the visually impaired children's capacity to discern color brightness.  
In general, there were significant differences in brightness levels among the red, yellow, blue, and green hues for both the 3 m and 5 m groups. As the scores for the low-brightness group increased, the scores for the high-brightness group decreased, with this trend being particularly enhanced in the 5 m group. The purple hue did not exhibit significant differences in the 3 m group, but it did in the 5 m group. This suggests that the observation distance has a considerable impact on brightness recognition in children with visual impairments.
# Robust Control of an Inverted Pendulum System Based on Policy Iteration in Reinforcement Learning
## 6 Numerical Simulation Results
### Example 2  
Let us consider the nonlinear IPS (28). According to Lemma 5, system (28) can be rewritten as  
\[\dot{x}=\bar{A}x+Bu+BG(z,x) \tag{47}\]  
The optimal control problem for the IPS is as follows: for nominal system (30), we find an optimal control \(u\) such that the performance index (31) achieves a minimum.  
According to Lemma 6, we obtain  
\[\mid G(z,x)\mid=\mid-zx_{2}[cos(x_{1}x_{2}+x_{3}x_{4})+\frac{0.5x_{1}+2x_{3}-4 x_{4}}{x_{2}}]\mid\leq\mid x_{2}+0.5x_{1}+2x_{3}-4x_{4}\mid=f_{max}(x)\]  
then  
\[f_{max}^{2}(x)=(x_{2}+0.5x_{1}+2x_{3}-4x_{4})^{2}=x^{T}\begin{bmatrix}0.25&0.5& 1&-2\\ 0.5&1&2&-4\\ 1&2&4&-8\\ -2&-4&-8&16\end{bmatrix}x\]  
According to performance index (31), the weight matrix \(M\) is selected as  
\[M=\begin{bmatrix}1.25&0.5&1&-2\\ 0.5&2&2&-4\\ 1&2&5&-8\\ -2&-4&-8&17\end{bmatrix}\]  
Figure 7: Trajectory of closed-loop linearized system.  
Based on Algorithm 2, we give the initial control policy  
\[u_{0}=[1.0900\quad 4.1230\quad-24.8908\quad-6.7726]x\]  
The initial state of the system is selected as \(x_{0}=[0\quad 1\quad 1\quad 1]^{T}\). Algorithm 2 converges after six iterations, and the \(S_{d}\) matrix and control gain \(K_{d}\) converge to the following optimal solutions.  
\[S_{d}=\begin{bmatrix}2.4325&2.4398&-6.2874&-1.3888\\ 2.4398&5.0469&-14.0539&-3.0045\\ -6.2874&-14.0539&130.4535&18.9735\\ -1.3888&-3.0045&18.9735&4.2715\end{bmatrix} \tag{48}\]  
and  
\[K_{d}=[1.1180\quad 2.6229\quad-32.3006\quad-7.4126] \tag{49}\]  
The evolution of the control signal \(u\) is presented in Figure 8. Figure 9 presents the convergence process of the \(S_{d}\) matrix.  
We also selected \(z=0.1\), \(0.4\), \(0.7\), \(1.0\). Figure 10 presents the closed-loop trajectory of system (28). Figure 10a-d represent the closed-loop system trajectories for uncertain parameters \(z=0.1\), \(0.4\), \(0.7\), and \(1.0\), respectively. It is easy to observe that the system is stable, which means that the controller is valid.  
Figure 8: Control signal \(u\) of the nonlinear system.  
Figure 9: S-matrix iterative process of the nonlinear system.
## 3 Results and Discussion
### Effect of Spring Factor of HDR Dampers on First Modal Damping Ratio  
To investigate the influence of two HDR dampers on improving cable damping, various properties of damping devices such as \(K_{j}\)-spring stiffness factor, \(\lambda_{j}\)-the loss factors, and the attached locations of dampers have been analyzed. For each HDR- damper parameter, the stay cable damping ratio is solved from the system of Equations (7) and (8). According to previous studies, the modal damping ratios do not depend much on the vibration modes for the stay cable installed HDR damper without rotational constraint at cable ends [7,10]. Therefore, the following sections present and discuss the effects of two HDR damper's parameters on only the first modal damping ratio of stay cable.  
Figure 2: Model algorithm.  
As discussed in Section 2, the spring factor of the damping device can be characterized by non-dimension coefficients \(\Phi_{1}=K_{\rm{i}}L/\pi\).S [13]. Where, \(L\), is cable length and \(S\), is cable tension, those are fixed for each stay cable. To figure out the relationship between \(\Phi_{1}\) and \(K_{\rm{i}}\), a stay cable with tension force (\(S\)) of 3200 KN and \(L\) of 100 m are considered in this investigation. Figures 4 and 5 illustrate the changes of 1 st modal damping ratio (\(\xi_{1}\)) with respect to the \(\Phi_{1}\) and \(\Phi_{2}\) corresponding to \(l_{1}/L\) and \(l_{2}/L\) equal to 0.03. In Figure 4, the \(\Phi_{1}\) varies from 0 to 60, while the \(\Phi_{2}\) values are fixed at 1, 2, 5, 10, 40, respectively. For each non-dimensional coefficient \(\Phi_{2}\), the 1 st modal damping ratio (\(\xi_{1}\)) increases with the increase of \(\Phi_{1}\) until reaching the maximum value. Then, the damping ratio (\(\xi_{1}\)) decreases gradually, and the curve becomes flat. Therefore, for each value of \(\Phi_{2}\), we can find an optimum value of \(\Phi_{1}\) having the highest damping ratio. The similar results are illustrated in Figure 5. Consequently, the optimum parameters for two HDR dampers in the current study are \(\Phi_{1}=1\) and \(\Phi_{2}=1\) corresponding to \(K_{1}=100\) and \(K_{2}=100\), respectively. The maximum 1 st modal damping ratio (\(\xi_{\rm{1 max}}\)) may reach up to 0.0284. The detail of the results is summarized in Tables 2 and 3.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture
### Azure Resource Provision
#### 4.2.5 Managed Identity  
Azure Managed Identities eliminate the need for embedding secrets in application code. Managed Identity is another key module that embodies security-by-design principles. Rather than relying on hard-coded secrets or application credentials, the project provisions Azure User-Assigned Managed Identities. These identities enable services to authenticate securely with Azure resources, supporting the principle of least privilege and aligning with zero-trust architectures. By isolating identity provisioning in a dedicated module, the design promotes reuse and consistent access management across all deployed environments. The Container App is provisioned with a user-assigned Managed Identity, allowing it to achieve the following:  
* Secure authentication to Azure services such as PostgreSQL and Azure AI Search;
* Bicep definition, including creating a Managed Identity at deployment, ensuring secure practices by default;
* Managed identity roles to secure access to resources in the environment on the subscription level.  
The infrastructure provisioning includes a dedicated module for Azure User-Assigned Managed Identity (UAMI), which enables secure authentication to Azure services without storing credentials. Identity is assigned appropriate roles on a subscription level, following the principle of least privilege. The full Bicep implementation for creating the managed identity and assigning its roles (e.g., Contributor, Search Index Data Contributor, AcrPull) is provided in Appendix E.
## 4 Application of Operating Model to the Simulator
### Configuration of the Operating Model in the Simulator  
Figure 7 shows the architecture of the TBM simulator. Once the simulation starts, the trainee can adjust any operational action. The mechanical responses continuously change following a change in operational actions. The interlock activation or warning alarm messages are displayed depending on the state of the excavation simulation. Both trainee and trainer can monitor all operating parameters on visual display panels. The instructor can create an unexpected situation by manipulating some mechanical responses in the instructor panel regardless of the operation actions taken by the trainee. The trainer can advise the trainee on dealing with any situation encountered during simulation.  
There are four main panel types for the operating: operating panels (Figure 8a), display panels (Figure 8b), a configuration panel (Figure 9a), and a warning alarm panel (Figure 9b). Figure 8a shows one of the display panels. This panel allows operators to monitor almost all of the operating parameters in one view plotted as a bar graph. At the top left corner of the panel, lengths of shield jacks are indicated; at the bottom left, the control pressure (= average earth pressure), cutterhead torque, and screw jack conveyor jack pressure are displayed; at the top right, shield jack speeds and the opening ratio of screw conveyor gate are indicated; at the bottom right, three operational actions associated with speeds and thrust are confirmed; in the middle, mechanical responses related to temperature are displayed. Figure 8b shows the main operating panels where the operator can press and click the various buttons for adjustment and monitor some of the main operating parameters. On the left, there are buttons for activating main operations; the number of shield jacks can be selected, and three operational actions (cutterhead rotation speed, shield jack speed, and screw conveyor speed) adjusted. Most operating parameters are displayed on the right.  
Figure 7: Architecture of the operating model for the TBM simulator.  
Figure 9a shows the configuration panel. Effective interlock systems are confirmed on the left, with each able to be activated independently if the system is set to _monitor_. On the right, upper, and lower limits of earth pressure can be determined. Figure 9b shows the simulator warning alarm panel, which displays information about alarm systems (Table 6). When a specific operating parameter has an abnormal value, the warning alarm light is turned on and displayed in a box at the bottom of the panel. Among the four columns, the left two are the first stage for _warning_, and the right two are the second stage for _error_. As an extra training tool, the instructor panel allows the trainer to introduce unexpected situations by manipulating any of the mechanical responses, regardless of the trainee's operation actions (Figure 10).  
Figure 8: (**a**) Display and (**b**) main operating panels in the TBM simulator.