esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 3 Materials and Methods
### Lighting Installation Rules  
Luminance requirements for different portal type tunnels are different; thus, the corresponding lighting installation rules and luminaire amounts of the lighting system are different, as illustrated in Figure 7. Table 6 lists the initial installation costs for tunnels. From this data, it can be observed that the initial installation costs increase with tunnel length. This is because the amount of equipment (e.g., LED luminaires, luminance controller, surveillance camera) increases with tunnel length.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multirow{2}{*}{**Tunnel Portal Type**} & \multicolumn{2}{c}{**Quantity**} \\ \cline{2-4}  & & **End-Wall** & **Bamboo Cutting** & **Cost Per Unit/5** \\ \hline \multirow{4}{*}{LED lamps} & 30 W & floor ((\(L-315\) m) \(\times\) 2/9) & 41.78 \\  & 50 W & 43 & 59 & 47.97 \\  & 80 W & 42 & 16 & 69.64 \\  & 140 W & 11 & 62 & 95.95 \\  & 240 W & 62 & 21 & 123.8 \\ Local optical transceiver & 1 & 1121.87 \\ Remote optical transceiver & cell (\(L/1000\) m) + 7 & 1060.08 \\ Server & 1 & 3961.74 \\ Local dimming controller & 1 & 1315.42 \\ Remote dimming controller & cell (\(L/1000\) m) + 7 & 1005.91 \\ Luminance meter & 1 & 1857.07 \\ Infrared sensor & 2 & 79.7 \\ Microwave detector & 2 & 162.03 \\ Surveillance camera & ceil (\(L/150\) m) & 558.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Details of initial installation costs for road tunnel lighting systems.  
Figure 7: Lamps’ installation rules for study tunnels.
## 3 Problem Statement and Input Data Representation Methods
### Common Forms of Input Representations  
With the rapid development of intelligent transportation systems, advanced deep learning techniques, particularly RNNs and their variants, for analyzing and predicting time series data have become a hot research area. RNNs, due to their unique feedback structure, demonstrate significant advantages in capturing long-term dependencies within time series data. However, the performance of RNNs in traffic prediction depends on their algorithms and is heavily influenced by the methods used to represent input data.  
Data representation methods play a pivotal role in model design and predictive performance. Different data representation formats, such as time series, grid sequences, or graph sequences, each possess distinct characteristics and advantages suitable for handling various types of traffic data. For example, time series methods emphasize the changes in traffic data over time, while grid sequences and graph sequences also concern spatial dependencies. Furthermore, applying sliding window techniques is particularly crucial for capturing time dependencies, as it allows for the inclusion of continuous sequences of historical data in the model, thereby enhancing prediction accuracy.  
With the advancements in big data and computing capabilities, an increasing number of studies are now exploring the impact of different data representation methods on the performance of RNNs in traffic prediction. From essential time series forecasting to complex spatiotemporal data analysis, researchers are striving to identify the most effective data representation methods to improve the accuracy and efficiency of traffic prediction. This section concentrates explicitly on the traffic prediction task's common input data representation methods. We aim to provide a comprehensive perspective to better utilize RNNs for traffic prediction by analyzing the latest research findings. Below, we will explore the relevant literature regarding three forms of input sequences: time series, matrix/grid-based sequences, and graph sequences. Table 1 summarizes these three types of forms of input representations.
## 3 Results and Discussion
### Real-Time Experimental Results
It is clear that the TSFSC has greater values for the three \(\mathcal{L}_{2}\) norms than those of the optimized approaches (except for the \(\mathcal{L}_{2}\) norm of the cart for the not optimized controller), showing a less performance and a higher control effort. This is mainly due to the fact that the controller has only two membership functions with one input, while the proposed approach has five for each of the four inputs. Nevertheless, it must be seen that although TSFSC has only two membership functions, it must be realized that this controller uses a linear approximator of the nonlinear dynamics, meanwhile the proposed approach does not use any dynamics of the system, and hence, fuzzy approximators must be used. These fuzzy approximators do not work well with few fuzzy rules. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & \(\mathcal{L}_{2}[\tilde{\theta}]\)**[deg]** & \(\mathcal{L}_{2}[\tilde{x}]\)**[m]** & \(\mathcal{L}_{2}[\tau]\)**[N]** \\ \hline NOT OPT. & 1.0360 & 0.0776 & 20.4555 \\ DE & 0.9765 & 0.0191 & 18.9581 \\ FFA & 0.9451 & 0.0193 & 19.3536 \\ PSO & 0.9371 & 0.0170 & 18.9549 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance indexes in real-time experimentation. Figure 14: Time response in real-time for the Takagi–Sugeno Fuzzy Servo Control (TSFSC) scheme. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(\mathcal{L}_{2}[\tilde{\theta}]\)**[deg]** & \(\mathcal{L}_{2}[\tilde{x}]\)**[m]** & \(\mathcal{L}_{2}[\tau]\)**[N]** \\ \hline TSFSC & 4.5969 & 0.0356 & 27.0273 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance index for T-S fuzzy servo controller with two MFs.
# GIS and BIM as Integrated Digital Environments for Modeling and Monitoring of Historic Buildings
###### Abstract  
Multidisciplinary data integration within an information system is considered a key point for rehabilitation projects. Information regarding the state of preservation and/or decision making, for sustainable restoration is prerequisite. In addition, achieving structural integrity of a historic building, especially one that has undergone many construction phases and restoration interventions, is a very elaborate task and should, therefore, involve the study of multidisciplinary information regarding historical, architectural, building material and geometric data. In this paper the elaboration of such data within 2D and 3D information systems is described. Through the process described herein, a methodology, including the acquisition, classification and management of various multisensory data, is displayed and applied within a geographic information system (GIS). Moreover, the multidisciplinary documentation process, aggregated with the surveying products, generates 3D heritage building information modeling (HBIM), including information regarding construction phases, pathology and current state of preservation of a building. The assessment of the applied methodology is performed concluding in a qualitative and a quantitative manner, in both 2D and 3D environments, providing information to facilitate the structural assessment of a historic building. Thus, in this work, the described methodology is presented, combining the multidisciplinary data with the development of GIS thematic maps and an HBIM. Representative results of the suggested methodology applied on the historic building of Villa Klonaridi, Athens, Greece are displayed.  
multidisciplinary documentation; geometric documentation; monitoring; HBIM; structural integrity; cracks; cultural heritage; building materials; decay; GIS thematic mapping +
Footnote †: journal: Applied Sciences
# Saturable Absorption Dynamics of Highly Stacked 2D Materials for Ultrafast Pulsed Laser Production
## 2 Saturable Absorption and Mode Locking  
Saturable absorption is one of nonlinear optical phenomena in which optical absorption decreases as incident light intensity increases, which can be explained by excited electron dynamics and [PERSON]'s blocking principle. Pauli blocking occurs when the transition states are filled, hence an incoming electron can no longer jump to them as a direct result of the Pauli exclusion principle. Consequently, saturable absorption takes place when a high intensity light comes into SAs. Higher is the incoming light intensity, more easily it can pass through SAs. In contrast, a low intensity light does not induce such a physical event and the incoming low intensity light would be absorbed into SAs and not allowed to pass through SAs.  
As such in most nonlinear optical processes, sufficiently high incident light is required to trigger saturable absorption. The physical parameters of SA are for how fast it dynamically responds, at what intensity it saturates and for what wavelengths it absorbs, which correspond to a recovery rate, saturation intensity and spectral range of application, respectively. A typical nonlinear optical absorption curve for characterization of saturable absorption is shown in Figure 1, in which several important parameters are presented. SAs show prominent intensity-dependent optical loss \(\alpha(I)\), exhibiting a curve shown below.  
\[\alpha(I)=\frac{\alpha_{o}}{1+I/I_{o}}+\alpha_{ns} \tag{1}\]  
where \(\alpha_{ns}\) is a non-saturation component of optical loss and \(\alpha_{o}\) is a total optical loss difference, and \(I_{o}\) is a saturable intensity.  
In laser optics, a high intensity light can occur when the modes of propagating lights in the cavity are accidentally locked in phase. As the saturable absorption process repeats by SAs, eventually lasers with a significantly high intensity would only survive in the cavity and they would correspond to highly mode-locked pulsed lasers, which is referred as passive mode-locking. In this context, ultrafast pulse lasers can be generated through a mode-locking process. At a mode-locking state, relative phases of laser modes in a cavity are locked and these modes are periodically constructive with one another, generating an intense pulsed laser. The duration of the pulsed laser is determined by the number of modes that are oscillating in phase and its time-bandwidth product is associated with the pulse shape.  
For example, a hyperbolic-secant-squared shape is often assumed for ultrashort fiber pulse lasers and its time-bandwidth product is 0.315. Passive mode-locking can be easily obtained using SAs and excellent SA performance enables the production of femtosecond pulsed laser in a compact photonic platform.
## 2 Materials and Methods  
### The SIR Model  
The epidemic process in the SIR model is schematically represented in Figure 1. As shown, a fraction of the subpopulation S turns to I with rate \(\alpha\) and another fraction of the subpopulation I turns to R with rate \(\beta\).  
The total size of the population N is given by:  
\[N=S+I+R \tag{1}\]  
Moreover, N is normalized. So, we set \(N=1\); therefore, the \(S\), \(I\)_and_\(R\) stand as a fraction of the population.  
Figure 1: Schematic representation of the SIR model.  
We denote:  
\[\frac{dS}{dt}=rate\ of\ Susceptible\ over\ time \tag{2}\]  
\[\frac{dI}{dt}=rate\ of\ Infected\ over\ time \tag{3}\]  
\[\frac{dR}{dt}=rate\ of\ Recovered\ (removed)over\ time \tag{4}\]  
According to Figure 1 and Equations (2)-(4), the system of ODEs describing the SIR model is:  
\[\begin{array}{c}\frac{dS}{dt}=-aSI\\ \frac{dR}{dt}=aSI-\beta I\\ \frac{dR}{dt}=\beta I\end{array} \tag{5}\]  
We can easily prove from the ODE that \(limS_{t\rightarrow\infty}limI_{t\rightarrow\infty}limR_{t\rightarrow\infty}\) exist. From Equation (5) we obtain  
\[\begin{array}{c}\frac{dS}{dt}=-aS\frac{\frac{\alpha}{\beta}}{\beta}\\ \frac{dS}{dR}=-\frac{aS}{\beta}\\ \frac{dS}{\beta}=-\frac{\alpha}{\beta}dR\\ \ln S=-\frac{\alpha}{\beta}R+c\\ S=S_{0}\exp(-\frac{\alpha}{\beta}R)\end{array} \tag{6}\]  
Since \(0<\) R \(<\) 1, susceptible individuals are  
\[\lim_{t\rightarrow\infty}S=\lim_{t\rightarrow\infty}S_{0}\exp(-\frac{\alpha }{\beta}R)\rightarrow\ S_{t\rightarrow\infty}>0 \tag{7}\]  
Equation (5) results in:  
\[\begin{array}{c}\frac{dS}{dt}=-\frac{aS}{aS-\beta}\\ \frac{dI}{dS}=-\frac{aS-\beta}{aS}\\ dI=-ds+\frac{\beta}{a}S\\ \int dI=\int-ds+\int\frac{\beta}{a}\frac{dS}{S}\\ I=-S+\frac{\beta}{a}\ln S+c\end{array} \tag{8}\]  
Equation (8) indicates that I at infinity will be 0 since it is assumed that all individuals will again fall within the S group.  
The SIR model has an equilibrium solution at  
\[\lim_{t\rightarrow\infty}(S(t),I(t),R(t)) \tag{9}\]  
The solution of the above system for S, I, and R is at time \(S\) (\(\infty\))  
\[S\left(\infty\right)=S(0)\times\exp(-\frac{a}{\beta}(1-S_{\infty})\to 0 \tag{10}\]  
\[R\left(\infty\right)=1-S(\infty)\to 1 \tag{11}\]  
\[I\left(\infty\right)=0 \tag{12}\]  
where \(S_{0}\) and \(I_{0}\) are the initial conditions and regarding inter-compartmental rates, a \(>\) b is assumed. The above solution also shows that moving to infinity a number of Susceptible and a number of Recovered individuals will be present.
## 3 Results and Discussion
### Stakeholder's Influence in Implementing CE Principles
#### 3.4.1 Owner  
The respondents ranked the owner as the most influential stakeholder in implementing CE principles, with the highest total points as shown in Table 8. The reasoning behind owner having the greatest influence was consistent across all interviewees. These respondents see the owner as the primary decision-maker who has the authority to set project goals, allocate financial resources, and drive the adoption of CE strategies. In this regard, CT2 commented that \"_The project owners have the ultimate control over a project, including setting the budget, scope, and priorities. Their commitment to CE principles is crucial because it directly influences the overall direction of the project. If the owner prioritizes sustainability and circular practices, these principles are likely to be embedded from the very start_\". Similarly, C1 heightened that \"_The owner is the one who will pay and invest in the project. If the owner is not convinced that the short-term or long-term benefits justify the investment, they will be hesitant to invest in  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Stakeholder**} & \multicolumn{8}{c}{**Participant Responses**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Rank**} \\ \cline{2-2} \cline{4-11}  & & & & & & & & & & & & & \\ \hline Owner & 3 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 29 & 1 \\ \hline Designer & 1 & 3 & 4 & 2 & 2 & 2 & 3 & 2 & 2 & 2 & 27 & 2 \\ \hline Consultant & 2 & 2 & 3 & 3 & 3 & 3 & 4 & 3 & 3 & 3 & 22 & 3 \\ \hline Manufacturer & 4 & 5 & 5 & 4 & 4 & 4 & 2 & 4 & 5 & 4 & 18 & 4 \\ \hline Contractor & 6 & 4 & 2 & 5 & 5 & 5 & 5 & 5 & 4 & 5 & 14 & 5 \\ \hline Sub-contractor & 7 & 6 & 6 & 7 & 6 & 7 & 7 & 7 & 6 & 6 & 5 & 6 \\ \hline Demolition contractor & 5 & 7 & 7 & 6 & 7 & 6 & 6 & 6 & 7 & 7 & 5 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ranking of the stakeholder’s influence in implementing CE principles.  
circular solutions. Circular solutions can be costly from an immediate perspective, as their benefits are realized over the long term. Therefore, the owner needs to be convinced of the value. To achieve this, stakeholders need to first persuade the owner. Once the owner is convinced of the benefits, they are more likely to invest in circular solutions.\"_ [PERSON] also discussed the significant role of the client by stating \"_As the client has the full authority over the project so, they should have more power to influence the adoption of CE principals by documenting all requirements in the project charter, business case and mapping all requirements in requirements documentations. If the client supports the adoption of CE at all levels of an organization, so sustainable practices can be achieved, as it will be a habit of all employees, which would foster an organization's commitment to CE principles\"._
# Intraoperative Use of Mixed Reality Technology in Median Neck and Branchial Cyst Excision
## 3 Results
### Case 3. Adjustable Navigation Frame  
In May of 2021, Patient K., 25 years old, was seeking medical advice in a maxillofacial surgery clinic and was diagnosed with branchial cyst. During inpatient preoperative treatment, the patient underwent MSCT of the neck, with intravenous contrast enhancement and cystography.  
The MSCT of the neck was performed in the patient's selected position, on the back with the head turned all the way to the left (to the healthy side).  
Similar to Case 1, Case 3 included a three-step MSCT protocol, with cystography and intravenous contrast enhancement (Figure 9).  
Operating Principle and Conclusion  
Due to the strictly specific lateral positioning of the patient, the use of the navigation frame from Case 2 was impossible.  
The developed anatomical navigation splint approach [24] allowed for the attachment of a splint with anatomical landmarks to any of the patient's body parts by means of an impression compound applied next to the area of interest prior to CT. In this case, the ear was chosen as a relatively immobile body part with a complex structure.  
Figure 8: Referencing the position of the patient’s head in the operating room using a navigation frame (**left**). Results of overlaying the 3D model of anatomical structures on the patient, as seen from the mixed reality glasses (**right**).  
Figure 9: The MSCT of the neck in the patient’s selected position, with intravenous contrast enhancement and cystography (**left**). Venous catheter inserted into the cyst and fixed to the skin, and a PLA splint fixed to a silicon impression of the patient’s ear auricle and auditory passage (**right**).  
The splint itself consisted of two parts: a reference frame and a fixing handle (Figure 10). The fixing handle had a cross-shaped base firmly installed into the impression compound. It also contained thin metal rods (10 mm \(\times\) 0.5 mm \(\times\) 0.5 mm), which served as radiopaque markers, allowing for further calibration of the coordinate systems (similar to the previous case).  
The key advantage of this method is the possibility to reference the splint to any part of the patient's body, as well as its quick installation. However, the chosen body part needs to have a complex anatomical shape and rigid bone structure.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 4 Discussion
### Model Efficiency  
Complexity and efficiency are extremely important to enhance the model's potential for real-time scene applications. Therefore, we compared the parameters, complexity, and speed metrics of each model on the UAVid dataset (1024 \(\times\) 1024). As shown in Table 8, the speed (114) of our model was second only to the UNetFormer and Fast-SCNN, and it also achieved the highest accuracy performance (mIoU = 68.2). Specifically, the accuracy of our model was improved by 6.8% compared with the fastest shallow CNN network, Fast-SCNN, and it also outperformed other Transformer-based networks. In addition, we replaced the shifted window attention mechanism with the global-local information attention module to maintain the information interaction of different windows, which can further improve the model's efficiency (Table 5). The efficient balance between model efficiency and accuracy proves the potential of our method for real-time scene applications,  
Figure 10: The heatmap visualization of different attention mechanisms. LNA: linear attention; MSA: multi-head self-attention; FLA: focused linear attention; SH: segmentation heads of our model.  
such as the ability to segment the remote sensing images in real time for environmental monitoring and land law enforcement inspections in urban areas.
## 6 Evaluation Metrics
### Error Metrics  
* **Mean Absolute Error (MAE)**: Measures the average absolute deviation between the PV panel's input power (\(P_{int}\)) and converter output power (\(P_{out}\)): \[\text{MAE}=\frac{1}{N}\sum_{t=1}^{N}|P_{in}(t)-P_{out}(t)|\] (17) where \(N\) is the number of samples. Lower MAE values indicate better steady-state accuracy.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
\begin{tabular}{c} GA- \\ optimized \(\Delta\) \\ \end{tabular} & \(V_{pv},I_{pv}\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Benchmarking configuration for MPPT algorithms.  
* **Integral Absolute Error (IAE)**: Evaluates the cumulative tracking error over time, penalizing persistent deviations: \[\text{IAE}=\int_{0}^{T}\left|P_{in}(t)-P_{out}(t)\right|dt\] (18) This metric is particularly sensitive to prolonged errors during transient conditions.
* **Mean Squared Error (MSE)**: Emphasizes larger errors through quadratic penalization: \[\text{MSE}=\frac{1}{N}\sum_{t=1}^{N}[P_{in}(t)-P_{out}(t)]^{2}\] (19)
* **Integral Squared Error (ISE)**: Combines temporal accumulation with quadratic error weighting: \[\text{ISE}=\int_{0}^{T}[P_{in}(t)-P_{out}(t)]^{2}\,dt\] (20) ISE is especially relevant for assessing performance under partial shading conditions where large power fluctuations occur.
# Resolution Aspects for Near-Field Projections
## 3 Results
### Dependency Analysis of Viewing Distance, Ambient Lighting and Projection Content  
Table 1 shows all results of the chi-square tests and the corresponding effect size. [PERSON]'s V is calculated to indicate the effect size of the difference and is categorized by [PERSON]'s effect size benchmark [43]. In the PGS, only one case is significant, where the symbol-based projection is significantly different, with a small effect when the observer evaluates the projection from either a 1 m or 5 m viewing distance. All other combinations regarding the PGS for text- or symbol-based projections are not significant. This leads to the result, without respect to the resolution's values, that in the PGS the viewing distance of the text-based projection can be accumulated to one group regarding the distribution of the participant's answers.  
Furthermore, all combinations are significant in the DDS with a small effect (\(V\leq 0.3\)) [43]. Consequently, all answer distributions in the DDS, are significantly different from the other with a small effect size. Likewise, in the comparison in the DDS, the ambient lighting influences the participants' answers, so all combinations for the compared distributions are significant.  
Moreover, comparing the distributions of the text- and symbol-based projections within the PGS, the applied chi-square tests are not significant. Hence, there is no difference in the participants' evaluation of the PGS for the projection content. This does not conclude in any difference in the perceived resolution [44]. In addition, the text- and symbol-based content at a 1 m viewing distance for the DDS is not significant, but the combination in the DDS of text- and symbol-based projections at 3 m or 5 m viewing distance are significant. Therefore, these two combinations regarding the projection content are significantly different and must be examined separately in further analysis.  
After all, the chi-square analysis features a significant dependency of the DDS independent of text- or symbol-based projection content on the viewing distances. Additionally, the ambient lighting plays a significant role in the subject's evaluation of the perceived resolution in all tested combinations. However, occasional significance is observed in the dependent variables of the PGS with symbol-based projection content (1 m \(\times\) 5 m), or the projection content comparison in the DDS for 3 m and 5 m. Overall, the chi-square test depicts the dependency of the compared distributions with small effect, therefore, the perceived resolution is evaluated and stated along with logistic regression in detail in the following section.