esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Datasets  
We conducted a validation of LSH-GEFR's performance across three distinct datasets, with the data volume for each dataset outlined in Table 1.  
The OALS2.0 dataset. The OALS2.0 dataset contains 15,890 life stories from 195 older adults. We collected life stories from various nursing homes, like Laoximen and Waitan in Shanghai and Jing'an in Hefei. Each older adult was interviewed four to five times, following an interview outline. These interviews aimed to explore their education, work experiences, family life, needs for elderly care services, and significant life events. We recruited volunteers aged 65 and above with good communication skills to conduct these interviews. Our dataset consists of voluntarily shared life stories. Following the wishes of older adults to make their life stories public, only 16 stories from nursing home residents met our criteria. To broaden our dataset, we extended the life stories to online sources, looking for stories from older adults aged 65 and above.  
The Twitter dataset. The dataset comprises 17,420 events collected from Twitter. We selected five topics from the US Trending list on Twitter, removed duplicates, and added them to the topic list. These topics were then used as keywords to craw1 500 tweets related  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **The Volume of Data** & **The Volume of Topic** \\ \hline OALS2.0 & 15,890 & 195 \\ Twitter dataset & 17,420 & 80 \\ CNN dataset & 46,855 & 61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The size of the datasets.  
to the topic. We filtered out tweets containing less than 20 words. As the majority of the tweets did not have a time element, we saved the posting time of the tweet as the time element of the tweet event.  
The news of the CNN dataset. We used trending topics as keywords to simulate browser behavior and search on CNN. From this search, we crawled 40 news items related to the topic, with only articles being retrieved and sorted by their relevance to the topic. The posting time of the news article was saved as the time element of the news event. The resulting dataset contains 46,855 English news documents spanning 61 topics.
# Map API-Based Evacuation Route Guidance System for Floods
## 2 Research Methodology
### Map API Comparison and Selection  
A map API was used to retrieve routes for pedestrians. Google Maps and OpenStreetMap are available in Korea. However, they were excluded because they could not accurately visualize the pedestrian route search. Therefore, based on a comparative analysis of Naver Map, Kakao Map, and T-Map, the T-Map API was selected as the map API for this study. Naver Map supports a walking route search in mobile applications, but is not supported in the computer version; therefore, a walking route search was not provided in the testbed area. Kakao Map identifies the most accurate route, but cannot be used outside the function provided by the API. T-Map does not output as accurately as Kakao Map, but supports the walking route search function API and can be used for development. Hence, the T-Map API was used in this study.
## 3 Materials and Methods
### Image-Based Transformation Techniques
#### 3.3.3 Recurrence Plot  
As a nonlinear transformation, a Recurrence Plot (RP) visualizes the recurrence of states in a time series [38]. Given a sequence of student behavior data, the RP calculates when certain states reappear over time, creating a symmetric matrix where each point represents a recurrence between two data points. High-density clusters on the plot indicate frequent recurrences, while isolated points or gaps suggest unique or one-time events. In this study, the RP captures the complex temporal dependencies and repetitive patterns within the behavioral data. By transforming the data into a recurrence matrix, the RP provides deep learning models with a robust view of recurring behavioral sequences, making it particularly effective in distinguishing consistent academic behaviors that contribute to success or failure.
# Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks
## 3 Proposed Scheme
### Initial PAN Construction
If it receives the RP request frame, it relays the received RP request frame to the PAN coordinator or the higher level coordinator. In the proposed scheme, the BI of every coordinator  
Figure 8: The superframe configuration process by a coordinator. Figure 7: The format of the proposed RP response command frame. in the WPAN is equal to the BI of the PAN coordinator. However, the SD of the coordinator can be adjusted by the number of devices and the lower level coordinators. If the inactive period is insufficient to allocate the SD to the lower level coordinator, the PAN coordinator can adjust its own BI. In the proposed scheme, because all coordinators transmit the beacon frame to their children coordinators in the RP, and the RP is allocated in the CFP, all device can receive the beacon frames from their parent coordinators without the contention. Therefore, the beacon frame transmitted by the PAN coordinator can deliver to all devices in the network without any collision. Also, in the proposed scheme, all devices only synchronize with their parent coordinator and are not affected by other coordinators. Thus, all coordinators can perform the distributed beacon scheduling and can communicate with their children devices in the next SD of their parent coordinator. If the PAN coordinator needs the additional resource and adjusts the BI, every coordinator adjusts their own superframes. Otherwise, the coordinator does not allocate the SD to the lower level coordinator. Figure 9 shows the relay function activation process of the coordinator. In Figure 9, when the coordinator powers on, its higher layer transmits a MAC layer management entity (MLME)-SET.request message to its MAC layer. Table 1 shows parameters of the proposed MLME-SET.request message. As shown in Table 1, to turn on/off the relay function, we add the RelayFunction parameter in the MLME-SET.request message and define its type to the Boolean type. If the value of the RelayFunction parameter is set to 'TRUE', the coordinator activates the relay function. Otherwise, the ZigBee protocol performs a routing protocol as the usual method. When the MAC layer receives the MLME-SET.request message from the higher layer, the coordinator activates the relay function. And then, the MAC layer transmits the MLME-SET.confirm message to the upper layer and constructs the relay table.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 4 Multidimensional Projection Approaches and Domains
### Local Convex Hull  
The Local Convex Hull (LoCH) [6] is a local technique built to project data embedded into multidimensional sparse space through three steps. In the first step, LoCH generates \(\sqrt{n}\) data clusters and finds their medoids (similar to LSP control points seen in Section 4.8), and these medoids are used to calculate distances among clusters. For each \(x_{i}\) data instance, the projection creates a _k_-neighborhood, considering the data inside the \(s\) closest clusters of \(x_{i}\).  
In the second step, the clusters' medoids are projected with Force Scheme [26]. According to [PERSON] et al. [6] testing, Force Scheme preserves global distance relationships despite the high-precision of its results.  
In the final step, the remaining \(x_{i}\) instances are projected, applying an interpolation that considers the previous projected points and the \(x_{i}\) distance to the convex hull of the neighborhood generated in the first step. Based on this idea, the \(x_{i}\) position in the projected space is determined by the linear combination \(\hat{y_{i}}=\sum\alpha_{i}y_{j}\); then,  
\[\alpha_{j}=\frac{1}{\delta(x_{i_{i}},x_{j})\cdot\sum_{x_{k}\in N_{i}}\frac{1}{ \delta(x_{i_{i}},x_{k})}},\text{being}\begin{cases}N_{i}&x_{i}\text{ neighborhood}\\ \alpha_{j}\geqslant 0\\ \sum\alpha_{j}=1\\ \delta&\text{distance function in the original space.}\end{cases} \tag{30}\]  
With this formulation, the distance relationships are preserved; however, it does not guarantee that \(\hat{y_{i}}\) is placed inside the correct convex hull. Therefore, the initial \(\hat{y_{i}}\) is iteratively moved toward \(\hat{y_{i}}\):  
\[\hat{y}=\hat{y}+\gamma_{i}\frac{\overrightarrow{v}}{\|\overrightarrow{v}\|}, \text{being}\begin{cases}\gamma_{i}=\frac{1}{k_{i}}\sum_{x_{j}\in N_{i}}\Bigl{(} \tau_{j}+\sqrt{\delta(x_{i},x_{j})^{2}+\tau_{j}^{2}-\|\overrightarrow{u_{j} }\|^{2}}\Bigr{)}\\ \tau_{j}=\frac{\overrightarrow{v}}{\|\overrightarrow{v}\|}\cdot \overrightarrow{u_{j}}\\ \overrightarrow{u_{j}}\end{cases} \tag{31}\]  
In this context, \(\gamma_{i}\) is a preservation factor of the original distances between \(x_{i}\) and its neighborhood \(N_{i}\). This process needs almost \(\sqrt{n}\) iterations to approximate \(y_{i}\) and the convex hull [6]. LoCH is simple to implement and presents a low computational complexity, namely \(O(n\sqrt{n})\). Sadly, LoCH characteristics (well suited to multidimensional sparse datasets) limit its application in a large range of applications.
## 3 Architecture
### Forecasting Component  
Figure 5 shows the forecasting component of MIRD. This component takes as input the preprocessed datasets corresponding to the last seven days and a set of forecasting models serialized as Pickle files. This set includes a model for each combination of PQM-PQ variable to forecast, which yields nearly 4000 different univariate forecasting models. The forecasting component uses the machine-learning library SciKit Learn. This component outputs the predicted values for each combination PQM-PQ variable, at a 1-h resolution, for the next week. MIRD executes the forecasting component weekly; however, the system administrator can change the execution frequency to as often as 1-day.  
Figure 4: Data-preprocessing component of MIRD. Eight-variable time series at 1-h resolution are preprocessed based on the parameters in the JSON archive. Preprocessed time series are passed as input to the forecasting component and archived to the Parquet-based persistence layer.
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space
### Increasing Number of Space Actors  
The new structure of the space system in the 21 st century is characterized by an increasing number of states that demonstrate interest in, capability for, and influence over, power relationships in the space sector. This phenomenon gives rise to a multifaceted and multipolar space system structure, characterized by the diversity of space actors involved, and their respective interests.  
As discussed in Section 4, space powers have been systematically investing in the development of economic, civilian, and military capabilities in space. However, an additional group of 80 countries, comprising emerging, middle, and small powers, is currently emerging in the space sector and has expressed a clear interest in participating in the global dynamics of the space system.
## 6 Simulation Results
### Robustness against Common Signal-Processing Effects
#### 6.3.4 Robustness to Signal Filtering  
Because of the noise that can affect a signal during its transmission, the bio-signal can be filtered for noise removal before being used for diagnostic purposes. In this test, the robustness of the proposed scheme against signal filtering was checked. For this, a test signal named \"118e06\" of ECG type (Figure 20) was used. This signal was then filtered by the \"Median filter\" of orders 5 and 10. This record was also filtered by the \"Average filter\" with a window of size 10. Next, the BER value was calculated for each filtered signal. From the results displayed in Figure 20, we can observe that the BER values remained close to  
Figure 19: Original MMG signal and its compressed versions with the corresponding BER values.  
zero. This finding means that the ECG signal copyrights were validated even if this signal was filtered by various filters.
## 6 Results
### Augmentation Types and Saliency  
To examine hypothesis one, we used a RMANOVA with augmentation type and saliency as within-subjects variables and computer vision accuracy as a between-subjects variable to determine their effects on jerk. Tests of sphericity were not violated for the augmentation type (\(p=0.45\)) variable and augmentation/saliency interaction (\(p=0.38\)), but were violated for the saliency variable (\(p=0.02\)). Huynh-Feldt corrections were used when sphericity could not be assumed.  
The main effects were present for augmentation type [\(F\) (3,78) = 4.10, \(p=0.009\), \(\eta^{2}_{partial}=0.136\)], but did not reach significance for saliency type (\(p=0.987\)), nor did the augmentation-saliency interaction (\(p=0.876\)), or the interaction of augmentation-saliency-CV accuracy interaction (\(p=0.406\)). As shown in Figure 3a, post hoc comparisons showed that a decrease in jerk magnitude was significantly associated with the use of augmentations with pairwise comparisons showing significant differences between no augmentations (\(M=8.20\) deg/s\({}^{3}\)) and the bounding box (\(p=0.003\); \(M=7.41\) deg/s\({}^{3}\)), corner-bound augmentation (\(p=0.002\); \(M=7.33\) deg/s\({}^{3}\)), but not for the outline augmentation (\(M=7.68\) deg/\({}^{3}\)) and no augmentation pair (\(p=0.52\)). Pairwise comparisons between the three augmentation types themselves did not reach statistical significance as all \(p\) values were greater than 0.5. As shown in Figure 4, the three second moving average graph of angular jerk, gross levels follow similar trends and patterns in both the augmentation and non-augmented conditions, but with greater intensity and more spikes in the non-augmented conditions.  
Pairwise comparisons between levels of saliency as well did not reach significance with all \(p\) values greater than 0.80. As seen in Figure 3b, the jerk a participant experienced varied significantly based on whether augmented cues were present in the given trial, with higher levels of jerk seen in the non-augmented conditions. These results support hypothesis one, that jerk will be highest in those trials without augmented cuing, but did not support hypothesis two, that jerk would be highest in those trials with higher levels of saliency.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 4 Prospects for WBAN and Suggestions for Routing Design
### Prospects for Future Routing Design
Similar bottleneck problems also exist in WBAN, so the introduction of mobile sink into WBAN can theoretically alleviate or solve the above problems. At present, the research about this method in WBAN is just at the initiation stage. Thus, future research on this method must refer to WSN. The problem to be considered is the selection of an optimal path, and specific application scenarios need to be treated in detail. The specific application of mobile sink in WBAN still needs the efforts and exploration of other scholars in the future. In addition, when processing heterogeneous data of WBAN, relevant technologies of DTN can also be combined. The most famous technology is \"storage-carry-forward\", which is suitable for an environment of unstable connection between nodes and intermittent topology, which is very similar to the dynamic scene of WBAN. At present, there is still no good solution to deal with the dynamic scene, because the movement of the human body leads to topology separation or shadow effect. At this time, the \"storage-carry-forward\" method of DTN can be used to solve the problem of data loss during topological separation, while this method is not suitable for an emergency data processing. Therefore, we should consider its applicability and the particularity of WBAN, when referring to other routing methods. The future progress of WBAN is bound to change with each passing day, and its application scenario must be complex and changeable. This paper only reviews the routing methods of single WBAN, which is based on narrowband communication, under the standard of IEEE 802.15.6. But nowadays, more and more research for WBAN, based on IR-UWB [61; 62; 63; 64; 65], IR-UWB communication can achieve a short-distance high-speed data communication, at very low power consumption, which has a great potential in energy efficiency routing design of WBAN. In addition, this paper did not study the topological environment of multi-WBAN, which may have some limitations, but the research of single WBAN is the first step to realize the application. Only by solving the technical problems of single WBAN, can the real interconnection be realized. Therefore, it is of great significance to review the routing methods of single WBAN.
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
## 4 Examples
### Validation with E-SAR P-Band Data
#### 4.2.2 Forest Height Inversion  
Following the steps of the TSVD-based inversion method, the complex interferometric coherence observations of HH, VV, HV, HH+VV, HH-VV, opt1, opt2, opt3, PDhigh, and PDlow were used to establish the residual equations. The pure volume coherence was then estimated by the TSVD-based method from the residual equations and, finally, the forest heights were extracted from the pure volume coherence model using the estimated model parameters. The extracted forest heights are shown in Figure 6b. As in the simulated experiments, the three-stage method was also used to extract the forest heights, and the inversion results are shown in Figure 6a. Figure 6c shows the LiDAR forest heights used as a reference.  
It can be seen from Figure 6 that the inversion results of the three-stage method and the TSVD-based method follow a similar spatial trend, but significant differences are also apparent. Compared to the LiDAR results, the inverted forest heights obtained by TSVD are clearly more accurate than those of the three-stage method. In order to analyze the differences, 272 forest stands characterized by nearly uniform tree heights were selected from the LiDAR results. We took the estimated forest height average for every stand and computed the difference between that and the LiDAR forest height. The RMSE and correlation coefficient (R\({}^{2}\)) were calculated to validate the performance. The validated stand-level plots are displayed in Figure 7.  
The validated plots of the three-stage method and the TSVD-based method are characterized by R\({}^{2}\) values of 0.2166 and 0.5824, respectively. This indicates that the forest heights inverted by TSVD are closer to the LiDAR forest heights. The RMSEs of the three-stage method and TSVD are 6.6351 and 3.4096, respectively. Clearly, the inversion accuracy of TSVD is higher than that of the three-stage method, showing an improvement of 48.6%. Therefore, it is possible to state that the TSVD-based method can improve the inversion of the forest height in this test site.  
Figure 5: Test site Pauli-basis RGB composite intensity image.  
Figure 6: (**a**) inversion results of the three-stage method; (**b**) inversion results of the TSVD-based method; (**c**) forest heights derived by light detection and ranging (LiDAR).
# SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
## 6 Discussion
### Future Work  
SPD-Safe integrates several technologies in a secure manner. It preserves the SPD properties, enables active defenses and countermeasures, and can facilitate emergency response operations.  
Active and offensive types of defenses are proposing nowadays, as the next step to enhance protection and mitigate threats, that the mainstream passive mechanisms (e.g., cryptography, network slicing, anti-viruses, etc.) cannot tackle. MTD is such an approach. It is becoming harder to analyze the system and exploit its vulnerabilities. Furthermore, in conjunction with other intrusion detection techniques, it can mitigate or even block some type of ongoing attacks. Nevertheless, more research is needed in order to make guidelines for the implementation of effective MTD policies as well as strategies to mitigate more advance attacks.  
Moreover, safety-related events require the participation of relevant authorities. In modern settings, emergency authorities possess their own equipment, which is utilized during safety incidents. The cooperation of the involved systems becomes vital when it comes to rescuing lives. For the effectiveness of the response services, the systems must authenticate and authorize the various participants and exchange information (e.g., sensors' data,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **SDP-Safe** & **TrainIntegrity** & **CMLRVT** & **SENSORAIL** \\ \hline Al technologies & JADE/ Jess & NO & NO & SeNslM \\ Reasoning \& processing & Distributed & Centralized & Centralized & Centralized \\ Conflict resolution & YES & NO & NO & NO \\ Security management & YES & NO & NO & NO \\ Safety management & YES & YES & YES & YES \\ Middleware & OSGI & NO & NO & NO \\ Network layer & & & & \\ protection & & & & NO \\ Cloud management & ViMA & NO & NO & NO \\ \hline \hline \end{tabular}
\end{table}
Table 6: Smart railway systems.  
surveillance video, etc.) in real time. The seamless interoperation will be examined in future extensions of SPD-Safe.
# Dynamic Response of Dual-Disk Rotor System with Uncertainties Based on Chebyshev Convex Method
## 4 Numerical Analysis of the Rotor Dynamic Response Based on CCM
### Multi-Parameters Uncertainty  
This subsection pays attention to the influences of multi uncertain parameters [33] on the dynamic behaviors of the dual-disk rotor. Consider the uncertainties in the supporting stiffness \(K_{2}\), damping \(C\) and mass \(m_{2}\) of disk 2 that studied in the previous subsections, in which the supporting stiffness \(K_{2}\) and damping \(C\) are correlated uncertain parameters, and the disk mass \(m_{2}\) is an independent parameter. The uncertain degrees are 10% for the uncertain parameters. The deflection curves of rotor system calculated by CCM and CMCS are shown in Figure 10.  
Comparing the curves of Figures 7 and 10 to Figure 9, at the same uncertainty degree, the overall envelope range caused by multi-parameters uncertainty is significantly wider than that caused by single parameter uncertainty. The peak shifts are observed at the critical speeds. It shows that the dynamic response is significantly affected by the multiple uncertain parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**CMCS**} & \multicolumn{2}{c}{**CMCS**} & \multicolumn{2}{c}{**CMCS**} \\  & **(50 Samples)** & **(100 Samples)** & **(500 Samples)** & **(\(n=5,p=8\))** \\ \hline calculation time (min) & 3.26 & 6.01 & 32.84 & 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation time of transient response of disk 2 with different methods.  
Figure 10: Deflection of the dual-disk rotor with multi\(-\)parameters uncertainty.
## 2 Materials and Methods
### Data
#### 2.2.2 Climate and Meteorological Data  
Climate data from 2000 to 2021 for the baseline ground meteorological observation stations and automatic stations in the study area were obtained from the China Meteorological Data Network ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/))) at a monthly scale. By comparing and cross-referencing the \"Daily Value Station Coordinates\", \"Station Description Documents\", and the \"National Meteorological Station Longitude and Latitude Coordinates\" documents, the longitude and latitude of the corresponding stations were supplemented and refined. The Kriging interpolation algorithm [41] was applied to the meteorological station data in the study area, resulting in a raster dataset with a resolution of 30 m.