esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of the Rotational Speed Estimation Principle Using GAD
### Speed Estimation Result Comparisons
Both the estimated average rotational speed and instantaneous rotational speed of the proposed method closely match those calculated by the encoder. It seems that most of the relative errors in the estimated average rotational speeds are within \(\pm\)0.5%, and most of the relative errors for the estimated instantaneous rotational speeds are within \(\pm\)5%. Furthermore, the effectiveness of the proposed GAD method is also verified under some stable rotational speed conditions. The rotation speeds are preset at 310 r/min, 410 r/min, 520 r/min and 620 r/min. The estimation results are listed in Table 1 and boxplots of the estimation errors are shown in Figure 14, outliers are plotted by red dots. It is found that both the estimation results of the average and instantaneous speed achieve a concentrate distribution. The estimated average speed errors are limited to \(\pm\)1%, while the estimated instantaneous speed errors remain within \(\pm\)5%, indicating the high accuracy of the proposed GAD method for order tracking.
# Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools
## 8 Process Simulation  
At micro-scale analysis, metal AM involves the complex physical phenomena of rapid cooling [86]. This model represents a multi-scale thermal-transient analysis on which every scan-laser hatching modifies the thermal response of bottom layers with melting-solidification cycles [87]. A constant heat transfer in the building direction to prevent high-stress concentrations is aimed. In this frame, numerical simulation plays an important role by predicting stress concentrations, associated deformations and high plastic strain regions. The last characteristic may lead to crack failures, detachment from the support plate, excessive geometric distortions or high anisotropic microstructure.  
The main advantage of the thermal analysis is optimizing the part design, building orientation and support generation to prevent high-stress concentrations. In addition, geometric compensation could be applied on the design to print pre-deformed part to obtain the nominal geometric tolerance after support removal [88]. The main drawback of this approach is that it becomes challenging to model due to the multi-scale behavior and the excessive computational time.  
Recently, software platforms such as MSC or Ansys provide supplementary modules to perform AM printing simulation with great accuracy and significant reduction of calculation time [85]. This model is based on the inherent strain approach that was first developed in academia for welding large components. The main characteristic is that thermo-mechanical simulation is replaced with a quasi-static FEM simulation where distortions are induced by user defined inherent strains [89]. The method starts with a course voxel discretization domain defined by the part with its support structure. The size of voxel elements is a multiple of real layer thickness, in this way every voxel layer is simulated as a manufactured layer. The material deposition modeling is addressed by a FE activation strategy, where new layers are activated with the corresponding inherent strain depending on hatching strategy and building time.  
Initial inherent strains can be obtained via simulation [89] and empirical methods [90]. The first method is based on the reduce order approach, which describes a thermo-mechanical simulation applied to a small-scale volume representation [91]. The last approach used by MSC Simufact Additive involves the manufacturing of sampled cantilever specimens to measure the maximum deflection after cutting from the building plate. Other input parameters include beam width, speed, power and material properties. To calibrate the model, the software uses an iterative procedure to find an adequate inherent strain related to maximum deflections for the sample and the selected printing strategy. After convergence, the calibration process is finished and this characteristic strain is fed as input to layer-by-layer deposition FEM static distortion analysis. Crucially, the simulation must be run under the same printing conditions defined on the calibration process.
# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 5 Results
### Success Rate
#### 5.1.1 NoDepot vs. DepotSt  
Figure 8 shows 100 paired comparisons of DepotSt and NoDepot in their evacuation success. Each point in the scatter plot corresponds to a simulated emergency event. The coordinates of each point shows the evacuation success of the two compared evacuation strategies simulated under that emergency event. Points on the red line indicate the two evacuation strategies have saved equal number of evacuees in those emergency events. The histogram shows the distribution of evacuation success for the 100 emergency events.  
Comparing NoDepot and DepotSt, for 87 events (i.e., 87% of the total 100 events) both strategies have saved the same number of evacuees, while for the remaining thirteen events (i.e., 13% of the total 100 events) the number of successful evacuees shows differences (Figure 8). For these thirteen events, DepotSt on average saved 3.4 more people than NoDepot. Specifically, in eleven of these events (84.6% of the thirteen events showing differences in the number of successful evacuees) DepotSt saved more people (4.4 people per event on average) than NoDepot, and only in the other two events (15.4% of the thirteen events showing differences in the number of successful evacuees), DepotSt saved less people (two people per event on average) (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Number of Cases** & **Proportion** & **More Saved Per Case** \\ \hline DepotSt’s Advantageous & 11 & 84.6\% & 4.4 \\ NoDepot’s Advantageous & 2 & 15.4\% & 2 \\ \hline \end{tabular}
\end{table}
Table 1: The advantages of DepotSt and NoDepot in terms of success rate when the numbers of the successful evacuees for DepotSt and NoDepot differ.
## 3 Results  
The presence of RF impairments in the received signal and their subsequent mitigation to obtain an estimate of the transmitted signal had a profound impact on the performance of our energy detection system, particularly in the determination of the threshold. Therefore, our initial evaluation focuses on assessing the performance of the two neural networks responsible for mitigating these RF impairments. Following the evaluation of the neural networks involved in RF impairment mitigation, we proceed to present the evaluation metrics for energy detection.
We show that the calculated value of the FM-noise SD agreed very well with the many independent measurements of the excess FM-noise in DBR-style lasers [15; 16; 18; 19; 20]. We used our analysis to find the laser parameters, both material and physical, that can be used to minimize excess FM-noise. Most of the dependent factors are deep-rooted in nature and thus unchangeable, though we found that while the shot noise increased proportionally to the injected current, the FM-noise increased proportionally to the square of the refractive index dependence on carrier density. Hence, it is possible to lower the excess FM-noise by choosing a laser material with smaller differential refractive index dependence on carrier density while maintaining the overall tuning capability. Our methods show how to numerically reproduce these random instantaneous frequency fluctuations, allowing us to create a system simulator to rapidly test signal processing concepts in order to use these lasers in high-capacity optical communication systems.
# Architectural Design for Digital Twin Networks
## 5 Design and Implementation Strategies
### Visualization  
Visualization is a crucial aspect of DTN systems, fulfilling the need for network visibility technology to accurately and intuitively display data and model outcomes. This technology not only mirrors the real-time interaction between the physical network and its twin but also aids in comprehending the internal network structure. Despite its importance, visualization is often underrepresented in research literature or omitted altogether, as many studies focus more on the technical and analytical aspects of DTNs.  
Platforms, such as Kibana [42], Grafana [43], Power BI [44], or Splunk [45], can be employed to enhance development speed by providing robust, ready-made visualization tools. However, if greater control is needed and the budget allows for in-house tool development, this approach becomes the most suitable solution. In-house solutions can leverage technologies like NetworkX [46], Vis.js [47], D3.js [48], and other frameworks, offering flexibility to implement custom visualizations in any programming language. For instance, Figure 5 shows a screenshot of our internal visualization tool, which utilizes Grafana along with custom plugins developed using D3.js. Ultimately, the choice of visualization tools and underlying query tools, as well as their design, should align with the specific goals of the DTN and the needs of its users, whether for detailed network analysis or general monitoring.  
Our architecture is fully decoupled, which means that adapting the visualization layer to other tools primarily involves tweaking the interfaces between components. The complexity of this adaptation naturally depends on the learning curve and flexibility of the chosen visualization tool. Although Grafana is used in our implementation and extended, similar integration could be achieved with other platforms, depending on user preference and expertise.
# A SAR-Based Index for Landscape Changes in African Savannas
## Materials and Methods  
### Study Area  
The study area is located in the center of the Sila region of Eastern Chad (Figure 1, upper right map). It hosts a total population of about 470,000 inhabitants, resulting in a moderately low population density of 12.5 inhabitants per square kilometer [72]. It includes the town of Goz Beida and the refugee camp of Djabal which was opened to Sudanese refugees in 2004 as a consequence of the large influx of people seeking shelter from the Darfur crisis [73]. Djabal is one of numerous refugee settlements (both temporary and permanent) along the 500 km long Sudanese border within Chad's territory which were established for people fleeing from civil war and environmental degradation [74; 75].  
The region lies at the southern border of the Sahel and is regularly affected by droughs [76] but receives more than 600 mm of annual precipitation on average, allowing for small-scaled agricultural use [77]. A decrease of summer rains and an increase of temperatures by 0.8\({}^{\circ}\) Celsius for the past 25 years have been reported, leading to both stronger dependency on crop yields and higher pressure on usable land [78]. Precipitation is mainly limited to the rainy season between June and September while the rest of the year is considered as highly arid. Climate can therefore be described as BSh (Hot semi-arid climate), referring to the Koppen-Geiger scheme.  
The area is dominated by Precambrian bedrock and Tertiary sediments as it lies at the transition between the geological Sud province and the Nubian Shield within the Chad Basin ([PERSON], 2017). Both camp Djabal and Goz Beida are located at around 575 m above sea level but they are framed by the Hadjer Arkop massif in the south and northwest reaching up to 900 m ([PERSON], 2017). Due to its location at the transition from desert to savannas most of the study area is covered by edaphic grassland, rupicolous shrubs, and scattered dry forest. Generally, tree cover is sparse in this area and mainly consists of single deciduous trees of the Combretaceae family ([PERSON], 2017). The ridges of the Hadjer Arkop massif are sparsely covered by various Boswellia trees and shrubs ([PERSON], 2017). Wadi Aouada, ranging from West to North across the study area, is accompanied by smaller semi-deciduous riparian forests (Figure 1, main map). Agricultural use of this land lies at around 30% and mainly concentrates around the few settlements. It is composed of small-scaled semi-permanent cultivation and bush fallow.  
Goz Beida lies along an important transport axis of Eastern Chad which connects the large cities of the North (Fada) with the ones in the South (Sarh). A Dutch military base (Ciara) under the mandate of the European Union was temporarily established between 2008 and 2009 as an additional peace force in the region (Boswellia, 2017). Today, only the Goz Beida airport remains at this location.
## 3 Methods and Materials
### Methods
#### 3.2.3 Spatial Autocorrelation  
In the spatial planar analysis, the distance methods used are Manhattan or Euclidean. In the analysis of spatial autocorrelation, the choice of conceptualization of the spatial association among crash events could be based on knowledge of the interactions among the features to be analyzed. There are several ways to conceptualize the spatial association between events, including fixed distance, inverse distance, inverse squared distance, K-nearest neighbors, the zone of indifference, the space-time window method and contiguity edges and corners. The result of the crash hot spot from global spatial autocorrelation and Getis Ord \(Gi^{\star}\) can be improved by applying Anselin Local _Moran's I_ to the aggregated traffic crashes [55]. The selection of appropriate spatial relationships among features for the use of local spatial autocorrelation analysis may help to reflect the distributional and spatial situation of definite target features [56]. The fixed distance approach was used in this study to conceptualize the spatial association among events.  
In this study, the incremental spatial autocorrelation tool of ArcGIS 10.5 was used to estimate the fluctuating value of spatial autocorrelation as a distance threshold. In this tool, Global _Moran's I_ index approach is employed to quantify the distance bandwidth (i.e., a distance in which maximum crash events are clustered [57]) across the entire area. The equations of _Moran's I_ index (I), the expected value (_E[I]_), variance (_V[I]_) and Z-score (Z_Score) are presented in Equation (6) to Equation (9) respectively:  
\[I=\frac{n}{\sum_{i=1}^{n}\sum_{i=1}^{n}w_{i,j}}*\frac{\sum_{i=1}^{n}\sum_{i=1}^ {n}w_{i,j}\big{(}x_{i}-\overline{X}\big{)}\big{(}x_{j}-\overline{X}\big{)}}{ \sum_{i=1}^{n}\big{(}x_{i}-\overline{X}\big{)}^{2}} \tag{6}\]  
\[E[I]=\frac{-1}{(n-1)} \tag{7}\]  
\[V[I]=E\big{[}I^{2}\big{]}-E[I]^{2} \tag{8}\]  
\[Z\_Score=\frac{I-E[I]}{\sqrt{V[I]}} \tag{9}\]  
where \(x_{i}\) is an attribute value of target feature at location \(i\), \(x_{j}\) is an attribute value of neighboring feature at location \(j\), \(w_{ij}\) is the spatial weight between features at location \(i\) and \(j\).  
A statistically significant Z-score (peaked) shows a critical distance threshold at which spatial autocorrelations are highly clustered. The first peak was used as the distance threshold in this study because it tends to best describe the spatial variation analysis and is recommended for use when there are multiple peaks [58].
###### Abstract  
The efficient extraction, storage, and visualization of geometric and semantic information is a key foundation for the operation of the building information modeling (BIM) platform. This study aims to develop a lightweight BIM system and optimize the system's performance according to the specific characteristics of steel structures. This study proposes several novel techniques for extracting and decoupling the geometric and semantic information of components from industry foundation class (IFC) files. A redundancy removal approach combining the principal content analysis (PCA) algorithm and the Hausdorff-based comparison algorithm is proposed to identify standardized steel components, and a lightweight visualization method on Web3D for redundant instances is also presented. A loading mechanism of the level of detail (LOD) model based on a mesh simplification algorithm is presented to optimize the display efficiency. The developed system is evaluated by three steel structural models. Using the redundancy removal approach, the number of instances is decreased by 96.46% in less than 30 s and over 30 FPS (frame per second) is kept when rendering. Using the LOD loading mechanism, 95.38% of vertices and 98.46% of patches are eliminated under 50 mm precision. The experiment results indicate that users can quickly load large BIM models and fetch sufficient information from the website.  
BIM; IFC; lightweight; visualization; steel structure Article
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 3 Design Overview
### Main Network Elements
According to Equation (2), the multicast source acquires the minimum fair shared rate \(R_{fair-shared}\),  
\[R_{fair-shared}=min\,\left(\frac{C_{i}}{N_{i}(t)},\ C_{access}\right),\ i\in N \tag{2}\]  
where \(N\) is the set of MTNs.
## 3 Results and Discussion
### Mechanical Properties of Self-Compacting SFRC  
The mechanical properties of hardened self-compacting SFRC were measured by the tests in accordance with the China code JG/T472 and CECS13 [25; 27]. Cubes with a dimension of 150 mm were made to determine the cubic compressive strength (\(f_{\rm ctu}\)) and  
Figure 5: Arrangement of strain gauges bonded on surface of pervious frames.  
Figure 6: Workability of fresh self-compacting SFRC.  
the splitting tensile strength (\(f_{\text{ffs}}\)), cylinders with a 150 mm diameter and 300 mm height were made to measure the axial compressive strength (\(f_{\text{fc}}\)) and modulus of elasticity (\(E_{\text{c}}\)), and beams of 100 mm \(\times\) 100 mm \(\times\) 400 mm were made to determine the flexural strength (\(f_{\text{ff}}\)). Each trial had three specimens, and a total of sixty specimens were prepared. After forming in steel molds for 24 h, the specimens were demolded and placed in the standard curing room for another 27 days. Then, the specimens were taken out of the curing room to wipe up the surface moisture and tested for corresponding mechanical properties. Test results are presented in Figure 7.  
Compared with self-compacting concrete SCF00 without steel fiber, as shown in Figure 7, the cube compressive strength of the self-compacting SFRC increased by 6.3% and 14.3% when the volume fraction of steel fiber was 0.4% and 0.8%, and decreased by 3.1% when the volume fraction of steel fiber was 1.2%. The axial compressive strength increased by 44.7%, 46.6% and 11.0%, respectively corresponding to the three volume fractions of steel fiber. The splitting tensile strength increased by 23.5%, 38.2%, and 51.0%, and the flexural strength increased by 42.8%, 47.6%, and 85.7%, respectively. The elastic modulus increased by 8.9% and 0.8% when the volume fraction of steel fiber was 0.4% and 0.8%, and decreased by 4.1% when the volume fraction of steel fiber was 1.2%. This once again demonstrates that the reinforcement effect of steel fiber is mainly on the mechanical properties which directly associates with the tensile performance of self-compacting SFRC [16; 18; 19]. Due to the trend of steel fiber orientation along the flow direction of a fresh mix, the flexural strength of self-compacting SFRC presents a higher increment with the oriented steel fibers parallel to the tensile direction at the tensile zone of beam specimens. Meanwhile, due to the confinement of steel fibers to the transversal deformation of the cylinder under axial compression, the axial compressive strength of self-compacting SFRC presented an increase with the presence of steel fibers. Relatively, less transversal deformation occurred on cubic specimens under compression, and less axial deformation of cylinders under compression was detected. This indicates a less influence of steel fiber on the cubic compression strength and the modulus of elasticity of self-compacting SFRC.  
According to the test results of the self-compacting SFRC, SCF08 was selected for the production of the pervious frames. That is, the volume fraction of 0.8% is rational for self-compacting SFRC.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 7 Discussion
### Model Performance (H4)  
Our results strongly refute H4, which hypothesized that the specialized, finetuned Llama3-Med42 model would outperform the general-purpose models. Llama3-Med42 proved entirely unsuitable for the FHIR conversion task, consistently prioritizing diagnostic suggestions over accurate data mapping, resulting in no valid FHIR bundles. This highlights the importance of selecting models appropriate for the specific task. Additionally, Gemini generally outperformed Mistral in terms of precision and hallucination rate, particularly with zero-shot prompts. While Mistral did retain more Spanish text, this did not translate to improved FHIR conversion and often introduced standardization challenges.  
Standardizing electronic health record (EHR) data using formats like FHIR offers significant potential for enhancing clinical data interoperability, enabling high-throughput computation, and promoting meaningful use. To advance FHIR-based EHR data modeling, this study evaluated the capacity of LLMs to extract, standardize, and integrate information from unstructured clinical narratives into FHIR resources. We believe that LLM-driven modeling of unstructured EHR data can play a crucial role in achieving advanced semantic interoperability across disparate EHR systems. It is important to emphasize that the FHIR format is highly restricted and complex, with clearly defined standards. Our results indicate that LLMs, when used with appropriate prompting strategies, can contribute significantly to this goal. This indicates that the main challenge lies in the conversion of the extracted information into a valid FHIR structure, rather than the initial information identification itself.