esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
By comparing seismic demands with structural capacities and validating the results against field observations, the study examines the role of regulatory compliance in the observed damage. The findings provide important insights into the effectiveness of past code provisions and the current vulnerability of the Turkish building stock. Buildings were modeled with 3, 4, 5, 6, 7, and 8 stories, reflecting the typical building heights in Turkey, to generalize the findings for the Turkish building stock. A total of 60 reinforced concrete building models were designed by producing 5 old and 5 new buildings for every building height (6 heights \(\times\) 5 structural models \(\times\) 2 different earthquake codes = 60 models). This study was carried out to assess the performance of Turkish buildings modeled with different story heights, different numbers of stories, different earthquake codes, and at different locations (stations 4611 Replicenter, 55.32 km, and 4615 Replicenter, 13.83 km), using a total of 46 (23 each) acceleration records. In the scope of the study, the horizontal strength and horizontal drift capacities of the buildings were assessed and compared with the obtained demands from 3D nonlinear time history analyses. The focus was mainly on the collapse conditions, representing scenarios where the occupants of the building would likely perish. As a result of 2760 analyses, findings consistent with previous field observations regarding the Kahramanmaras earthquake were identified. While older buildings experienced higher rates of collapse, the analysis also revealed that, although the rate was lower, some newer buildings also collapsed. Unlike other studies, this research was conducted to explain why certain buildings remained intact and to further validate field observations. The results confirm that buildings constructed according to the updated regulations (TEC1998) demonstrated significantly better performance compared to older buildings. However, the Kahramanmaras earthquake also revealed that many buildings were non compliant with the regulations, and these were the structures most likely to experience collapse. The Kahramanmaras earthquake highlighted the critical need to assess whether reinforced concrete buildings, particularly those located near fault lines, comply with current regulations.
## 4 Controller Design
### Nonlinear Disturbance Observer  
The uncertainties and disturbances in the system must be adequately compensated in order to achieve perfect tracking performance. A nonlinear disturbance observer is utilized to deal with this problem. The nonlinear disturbance observer is designed, as follows [32].  
\[\left\{\begin{array}{l}\dot{\theta}_{l2}=\sigma_{l2}+\eta_{l2}x_{l2}\\ \dot{\sigma}_{l2}=-\eta_{l2}\sigma_{l2}-\eta_{l2}(c_{1}x_{l3}+c_{2}x_{l2}+ \delta_{l2}+\eta_{l2}x_{l2})\end{array}\right. \tag{26}\]  
\[\left\{\begin{array}{l}\dot{\theta}_{l3}=\sigma_{l3}+\eta_{l3}x_{l3}\\ \dot{\sigma}_{l3}=-\eta_{l3}\sigma_{l3}-\eta_{l3}(c_{3}x_{l4}+c_{4}x_{l2}+ \eta_{l3}x_{l3})\end{array}\right. \tag{27}\]  
\[\left\{\begin{array}{l}\dot{\theta}_{o1}=\sigma_{o1}+\eta_{o1}x_{o1}\\ \dot{\sigma}_{o1}=-\eta_{o1}\sigma_{o1}-\eta_{o1}(d_{1}x_{o2}+\delta_{o1}+\eta _{o1}x_{o1})\end{array}\right. \tag{28}\]  
where \(\eta_{l2}\), \(\eta_{l3}\), and \(\eta_{o1}\) are the gains of the observer, which are related to the convergence speed of the NDO. \(\sigma_{l2}\), \(\sigma_{l3}\), and \(\sigma_{o1}\) are the internal variables.
## 4 Materials and Methods
### Machine Learning Segmentation Process
#### 4.3.2 Feature Extraction and Selection  
Training a binary classifier with many pixel features can be challenging due to high computational memory and speed requirements. Additionally, some features were observed to be highly correlated (Figure 9a) and redundant, leading to similar effects during  
Figure 8: Annotation of images into foreground and background patches for feature extraction. (**a**) Wheat image annotation and (**b**) Cowpea image annotation. The FG represents the foreground annotation, and the BG is the background annotation.  
training. To address these issues, a feature-selection method called decision-tree-based recursive feature elimination (DT-RFE) was applied. DT-RFE is a wrapper-type feature selection method that uses a backward feature illumination technique to remove features in order of importance. It works on the principle of decreasing the size of a feature set by recursively selecting the most important ones. The method works by first training the decision tree model with a set of features. A significance mark of each feature is obtained, and weights are assigned to each feature. Then, a ranking criterion of all features is computed by using the sum of the weights of each feature in the model [37]. The features are then sorted according to the final score (weighted sum), and the smallest ranking features are removed. The process repeats itself until the desired number of highly sensitive features are achieved [38; 39].  
In this study, 12 features with an importance score above a 0.04 threshold were selected as input features for training the classifier (Figure 9b). This threshold was selected based on a trial experiment with different feature combinations. It was observed that a combination of the first 12 selected channels (with a high importance score) resulted in high accuracy of classification, whilst additional channels had no impact on overall accuracy. Moreover, when fewer than 12 channels were used, the classification accuracy dropped.  
The important features selected for training the binary classifier are shown in Figure 9b. This includes the R and G (from RGB), H and S (from HSV), L and a (from Lab), v (from Luv), y (from ybr), u (form YUV), h, l, and s (from hls). These selected channels have properties such as high contrast, illumination invariant, and color differentiation that help in accurately separating plant pixels from non-plant pixels. For instance, the H and S channels separate the color information from intensity, reducing the effect of illumination and shadows on the robustness of the segmentation method. Additionally, the L and a components differentiate varying shades of color, with L indicating lightness, and a showing the red/green coordinate, which is dominant in the color makeup of plant pixels. Color transformation channels and their impact on image processing are highlighted in the work of [PERSON] and [PERSON] [40].
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Node
#### 4.1.3 Multiple Network Protocols  
An emulation container is created based on container images. The network protocol implementation software and other applications would be deployed among the container image in advance. According to different types of network protocol architecture, the  
Figure 3: Emulation node.  
emulation container can be divided into three types, namely the DTN node, TCP/IP node and custom node, as shown in Figure 4. The DTN node and TCP/IP node correspond to DTN and the TCP/IP network protocol architecture, respectively. Furthermore, custom node provides an extension method for new or modified protocol architectures in the proposed platform.
* DTN node. The DTN node is built by the ION-DTN container image. ION-DTN is a software which includes the implementations of LTP, BP, CGR routing protocol and some other DTN protocols.
* TCP/IP node. The TCP/IP node is generated by the Quagga container image. Quagga is a software which supports various routing protocols, such as BGP, OSPF, RIP and etc.
* Custom node. Various dependencies were installed in the custom container image previously to provide runtime environment for the software implementation of new or modified protocols and technologies. The virtual switch is also deployed in the custom container image. Based on virtual switch and flow tables, the custom node can support custom forwarding rules.
## 3 IPv6 Communications Requirements for M2M
### Routing  
In addition to assigning addresses to IP eHealth devices in a vehicular network, routing must be set up. Identifying and configuring routes in a system comprising a huge number of devices may quickly become a communication- and computation-intensive task, overcoming the capacity of the existing Internet. The concept of _default_ route (the route to be chosen from a routing table when no other route is matching a destination address) provides partial resolution to this problem; it is sufficient for the gateway to hold a single default route (the IP address of the next hop) instead of numerous routes towards too many specific destinations. Default route auto-configuration mechanisms exist basically under two distinct forms. The first is router advertisement (RA)-based (the use of stateless address auto-configuration), and the second is a dynamic routing protocol, such as OSPF [30]. Currently, these two mechanisms are the only Internet Engineering Task Force (IETF) mechanisms to assign a default route to an end node.  
Devices with limited CPU and memory capacities can benefit from the sole presence of a default route in their routing tables: it is sufficient to store only the default route in order to be able to reach any other node on the Internet. This is especially advantageous for machine-type communications.  
Whereas stateless address auto-configuration offers a default route to an end device, it does not offer a set of prefixes. Similarly, the prefix delegation part of the stateful address auto-configuration does offer a set of addresses to the gateway (in order to further deliver them to the IP eHealth devices), but does not offer a default route.  
For a limited capacity device (a constrained vehicular gateway or a constrained eHealth device), it is advantageous to use a lightweight auto-configuration protocol offering both parameters:  
* an IPv6 route to be used as a default route in the routing table of the gateway.
* a set of IPv6 addresses (addresses or prefixes) to be used for address auto-configuration on the IP eHealth devices on board the vehicle.
## 6 Discussion
### Limitations of the Proposed Method  
The soft spreading coding system combined with LDPC codes proposed in this paper loses channel information during the process of mapping the spreading sequence to symbols. This degrades its BER performance. To achieve an improved BER performance, the main correlation peak and the secondary correlation peak symbols of the M-ary correlator decoding could be combined and the input to the decoder could be adjusted to compensate for the partial loss of channel information. In addition, the performance of the method can be further verified in extreme environments through sea trial experiments.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 4 Discussions  
This study examines the dynamic effect of fire on the built environment and its developing interaction with evacuation in historical districts with ABM and SFM models. This study reveals that the location and progression of a fire significantly influence its spread, the extent of damage to the built environment, and its interaction with evacuation dynamics. While fires continuously destroy the built environment, their impact on buildings and roads is not synchronized--road obstruction occurs earlier than the expansion of burned building proportions and exhibits greater persistence. Changes in the built environment and evacuation routes affect evacuation behavior through different mechanisms at various stages of the evacuation process. Congestion and detours primarily result from the interaction between inappropriate evacuation strategies and changes in the built environment.  
Moreover, all temporally dynamic analyses deviate from static analysis results, especially in the first 25 min in evacuation duration-related indicators. Additionally, a mismatch exists between the evacuation destinations outside the historical district and the evacuation patterns within the district. Ultimately, 50% to 80% of evacuees in the historical district experience longer evacuation duration, with an average increase of 150%. These findings provide critical insights for understanding fire-induced evacuations in historical districts and formulating more effective evacuation policies.
## 4 Results and Discussion
### Comparative Analysis  
Table 5 displays the comparative findings between the proposed watermarking scheme and the zero-watermarking schemes for vector maps that have proven strong robustness against multiple attacks in recent years. Among them, the reference [34] is our previous similar study, which agrees with the construction of CDTN but extracts different feature information. In the table, '\(\surd\)' means that retrieving a valid watermarked image is possible,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
while '\(\times\)' means that doing so is impossible. In comparison to these representative studies, the present watermarking is only incapable of extracting the watermark image in larger scale points-adding attacks, whereas it is capable of extracting a valid watermark image in other attack patterns of varying degrees, demonstrating stronger robustness. In particular, most watermarking algorithms, such as [31; 32; 40], fail to extract watermark information in the face of larger-scale points-deletion and cropping attacks, while the proposed watermarking scheme shows a very strong robustness advantage. Due to the stability of the feature points and the geometric invariance of the CDTN, the proposed watermarking scheme can extract and verify the watermark with a small quantity of data, and this is also the primary reason why the proposed watermarking can extract the watermark image under the condition of different forms of coordinate points reduction. The algorithm described in [34] has a similar theoretical foundation to the proposed technique, notably with respect to the use of CDTN to extract feature information, and hence exhibits a very similar robustness performance in the comparison findings. However, Ref. [34] is unable to withstand the transformation of the projection coordinate system, whereas the technique presented in this study has been enhanced in this regard. The algorithm in [23] also mines the spatial angle values of vector maps as feature information based on feature points, which can complete the construction of zero watermark with a small amount of data. The results show that the method has very comprehensive robustness, especially in terms of coordinate point attacks, which is better than the algorithm in this paper. However, the scheme is difficult to resist coordinate system changes, while the proposed algorithm is greater in this regard. In general, the proposed zero-watermarking algorithm offers a more comprehensive performance in terms of robustness than the majority of current studies.
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
## 4 Improved Tri-Satellite Localization Method
The output location result is used as the initial value, and the single moment iteration method is used to obtain the precise value of the positioning result for the measurement result at each moment. The flow chart of the proposed tri-satellite interference localization is shown in Figure 3.  
Figure 3: The flow chart of the tri-satellite interference localization.
## 2 Development of Sensor Module
### ToF Distance Sensor Selection  
In this study, for the specific application of obstacle detection in unfamiliar indoor environments, the VL53L8 CX (STMicroelectronics, Bengaluru, Karnataka) was selected as the primary ToF distance sensor due to its multi-zone ranging capability, which addresses the limitations of conventional single-point ToF distance sensors. Unlike its predecessors (e.g., VL53L0X), the VL53L8 CX supports up to 64 independent ranging zones (8 \(\times\) 8 grid array), enabling higher-resolution spatial mapping of obstacles within a 45 \(\times\) 45-degree FoV and a fast response time (up to 60 Hz, corresponding to approximately 16.7 ms per frame) [26; 27; 28], characteristics that are crucial for real-time applications like collision avoidance in dynamic and unpredictable environments. This enables the sensor to perceive not just distance in a single direction but to construct a simplified spatial representation of its environment. This multi-zone detection enhances spatial awareness, allowing the drone to better recognize obstacles from various angles and make informed decisions to avoid collisions. This miniature ToF distance sensor offers an optical combination of accuracy, reliability, and ease of integration, making it an effective component for improving drone  
Figure 1: ToF distance sensor’s ranging mechanism.  
navigation and safety. Its multi-zone detection, low power consumption, wide field of view, and built-in signal processing contribute to its effectiveness in collision avoidance applications, ensuring drones can operate safely and efficiently in diverse environments; Table 1 summarizes the sensor's specifications.  
One of the most advantageous features of this sensor is its wide field of view of 45 degrees both horizontally and vertically. The sensor can return either 16 distance values (in 4 \(\times\) 4 resolution mode) or 64 distance values (in 8 \(\times\) 8 resolution mode). Figure 2 illustrates how these modes capture depth information in a real-world environment. Each number in the grid represents the index of a zone in the measurement data. The VL53L8 CX's combination of multi-zone detection, wide field of view, high accuracy, low power consumption, and compact form factor makes it a suitable sensor for real-time obstacle detection and avoidance in confined indoor environments.
# Map Matching Based on Seq2 Seq with Topology Information
## 2 Related Work
### Dataset Preparation  
The original data for this article was obtained from the OpenStreetMap download of the road network in Yibin City, which was used as the data source. Using QGIS software, special road sections were removed from the network, leaving only the main roads in Yibin City, as shown in the diagram below. The dataset includes 6429 GPS road points and 619 roads. The coordinates of the bottom left corner node are left_bottom = [104.5783146, 28.7090704], and the coordinates of the top right corner node are right_top = [104.6833462, 28.7962751]. The total area is 99,507,385 m\({}^{2}\).  
Based on this information, a directed graph G(V,E) was generated using the NetworkX data processing package for complex graphic network analysis in Python, and all nodes and edges were calculated, totaling 5810.  
For ease of description, this article divides the map into a grid of 1000 \(\times\) 1000, with a precision of 9.95 m \(\times\) 9.95 m, and assigns a grid code from 1 to 100,000. In actual environments, the grid precision can be adjusted according to demand.
## 4 Discussion
### Subjective Assessment
#### 4.1.4 Analogy of Our Proposal among Test Images A, B, and C  
To evaluate the shadow detection performance of our proposed MPSI-based approach in different scenes, we compared the appearances of resulting images shown in Figures 5h, 6h and 7h with corresponding reference images shown in Figures 5b, 6b and 7b. In these resulting images, shadow pixels were well distinguished from vegetation pixels whether the test image included a simple scene of almost all vegetation (test image A shown in Figure 5a) or a complex scene of diverse land cover types (test image B shown in Figure 6a and C shown in Figure 7a). Also, outlines of shadow regions were well delineated, even though a small amount of shadow pixels were still omitted. Comparing the appearances of the resulting images of test images A, B, and C with our proposed method shown in Figures 5h, 6h and 7h, respectively, we validated that the shadow regions were better recognized for test image C than in A and B. Also, the resulting image shown in Figure 7h for test image C highlights that our proposed approach works well for images with complex scenes including diverse land cover types. When comparing the performances of our shadow detection approach for test image A and B, we found that vegetation pixels and small shadow regions were well detected in both the test images, even though some dark buildings in test image B were misclassified as shadows to some extent due to their similarity to shadows. Accordingly, based on the subjective assessment, we conclude that our proposed MPSI-based shadow detection approach shows an improved ability in terms of mitigating the shadow omission and vegetation misclassification problems compared with other comparable standard shadow detection methods investigated in this paper.
## 3 Results
### Surface Characterization Results
#### 3.1.2 Hydrothermal Treatment  
Fourier transform infrared spectroscopy (FTIR) was performed to confirm the removal of residual SO\({}_{3}\)H groups post-hydrothermal treatment. Areas of interest on the FTIR spectrum are the broad OH band shown around 3400 cm\({}^{-1}\) and a peak between 1060 cm\({}^{-1}\) and 1040 cm\({}^{-1}\), which indicates an SO\({}_{3}\)H functional group. The OH band is predominantly seen on the non-hydrothermally treated specimens in Figure 5. The decrease in OH-absorbance on hydrothermally treated specimens is further seen in Figure 6a. Figure 6b is an enlarged area around 1050 cm\({}^{-1}\), where the absorbance increase is seen for the sulfonated specimens, indicating the presence of the SO\({}_{3}\)H group.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen:** & **S1** & **S2** & **S3** \\ \hline Total Pore Count & 4714 & 5486 & 6255 \\ Pore Count (15–100 \(\upmu\)m) & 1712 & 1700 & 2312 \\ Pore Count (\textgreater{}100–400 \(\upmu\)m) & 483 & 663 & 1048 \\ Pore Surface Coverage (\%) & 10.5 & 15.0 & 17.5 \\ Mean Pore Size (\(\upmu\)m) & 95.4 \(\pm\) 86.1 & 99.66 \(\pm\) 11.20 & 101.8 \(\pm\) 101.3 \\ \hline \end{tabular}
\end{table}
Table 1: Pore counts and total pore coverage on the independent replicates.  
Figure 5: FTIR spectrum for independent replicates before and after hydrothermal treatments. Arrows indicate the OH band around 3400 cm\({}^{-1}\) and the SO\({}_{3}\)H at 1500 cm\({}^{-1}\).  
Figure 6: (**a**) Enlarged portion of the FTIR spectra in the 3800 cm\({}^{-1}\) to 3400 cm\({}^{-1}\) range to show the broad peaks for OH- as indicated by the red box. (**b**) Enlarged 1100 cm\({}^{-1}\) to 1000 cm\({}^{-1}\) portion of the FTIR spectra to show the peaks for SO\({}_{3}\) groups as indicated by the red box.
## 4 Results
### Comparative Analysis with Traditional Methods
#### 4.8.4 Implementation Considerations and Practical Performance  
Real-world testing at the Singtel Serangoon TEPL building demonstrated superior performance in queue management. The LSTM system achieved average wait times of 45 s, closely matching actual measured times of 48 s, while queueing theory predicted 92 s. Resource utilization showed similar advantages, with LSTM achieving 85% optimal utilization compared to 72% for queueing theory and 65% for building codes.  
User satisfaction surveys (\(n=500\)) confirmed these improvements. Wait time satisfaction increased by 35.48% (from 3.1/5 to 4.2/5), facility availability improved by 33.33% (from 3.3/5 to 4.4/5), and overall experience ratings rose by 34.38% (from 3.2/5 to 4.3/5).  
These comprehensive comparisons demonstrate that despite higher initial investment requirements, our LSTM-based approach provides superior performance across multiple metrics and scenarios, resulting in significant long-term benefits for both user satisfaction and resource optimization.