esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Control Loop of the BOROWIEC SLR Space Debris Tracking System
## 1 Introduction
The laser technique, whose task is to accurately and precisely measure the range between the SLR ground station and orbital object in the time domain, also allows determining the dynamics of an object (spin) using a picosecond laser source [11; 12]. Passive optical acquisition allows the use of a telescope and digital cameras (CCD, CMOS, sCMOS) to determine the orbital parameters of an object [13] and can also be used to obtain additional information using techniques such as light curves [14], which show the momentary variability of the brightness of objects, or further attempts to characterize the mission. The combination of these techniques makes it possible to obtain more data during a single pass of an object over a ground station. The positional uncertainty of radar ephemerides in the form of Two-Line Elements set files (TLEs), in terms of their initial accuracy resulting from the inaccuracy of the radar survey technique, often cause problems in the tracking process behind an orbital object. Particular mention should be made of deorbiting events (orbit perigee \(h_{p}<200\) km), the trajectory of which is constantly disturbed, implying a rapid obsolescence of ephemeris information, hindering the process of effective tracking of such objects. SLR stations perform point measurements, so the quality of the reflection of the theoretical ephemeris position of the orbital object above the station in relation to the real trajectory is important. Classically, the Field of View (FoV) of the transceiver telescope together with the photon detector enables tracking of an orbital object from the LEO region with an offset to the theoretical trajectory usually no greater than 50 ms (TimeBias). For offsets greater than the limit value of the FoV of the telescope, measurements are difficult to perform (the object is not visible in the field of view of the receiving telescope). In the case of regular tracking of cooperative satellites under the International Laser Ranging Service (mainly altimetric satellites and GNSS), it is possible to obtain current information about the appropriate correction to the object's position (TimeBias). Thus, taking into account the value of the given offset, it is possible to correct the tracking ephemeris. In the case of tracking of uncooperative objects, the information about the theoretical trajectory, which is based on the TLE ephemeris, it is impossible to determine the offset of the position of the orbital object before performing the tracking process. This situation is particularly important in the case of re-entry events (object deorbiting), where the difference between the theoretical position of the object and the real trajectory is even a few seconds. It should also be mentioned that the positional accuracy of the TLE ephemeris over time is lost. For the LEO region, the TLE ephemeris loses its operational accuracy for the classic SLR station after 24 h. Due to the above, there is a need to make ongoing corrections of the tracking process behind the orbital object.
## 3 Research Framework of Internet of Emergency Services
### Components and Devices of Internet of Emergency Services
#### 3.2.3 Wearable Devices  
Wearable devices are electronic devices that can be worn on the body and are capable of collecting, processing, and transmitting data in real-time. They have become increasingly popular in emergency management and response due to their ability to provide \begin{table}
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Table 2: Summary of Papers on Use of Sensors for Emergency Management and Response. critical information about the physical state and location of individuals in emergency situations [12]. One of the most important applications of wearable devices in emergency management and response is in search and rescue operations [5; 49]. Wearable devices can be used to monitor vital signs and detect the location of individuals who are trapped or lost in disaster situations. For example, the Los Angeles County Sheriff's Department uses wearable devices to track the heart rate, body temperature, and other physiological data of search and rescue team members during missions. Another application of wearable devices is in monitoring the health and safety of emergency responders. In hazardous environments, such as wildfires or chemical spills, wearable devices can detect toxic gases, monitor radiation levels, and provide alerts in case of dangerous conditions. The New York City Fire Department is currently testing wearable devices to monitor the exposure of firefighters to toxic chemicals and gases [12; 49]. Wearable devices can also be used to monitor the physical and mental health of individuals during emergency situations. For instance, during the COVID-19 pandemic, wearable devices were used to track the temperature and respiratory rate of patients in hospitals and quarantine centers [77]. In addition, wearable devices can be used to monitor the stress and emotional state of individuals during and after disasters, providing valuable insights into the psychological impact of emergency situations. However, the use of wearable devices in emergency management and response is not without challenges and limitations. One major challenge is the accuracy and reliability of the data collected by these sensors. Factors such as movement, sweat, and skin color can affect the accuracy of vital sign measurements. Additionally, the privacy and security of the data collected by wearable devices must be carefully managed to ensure the confidentiality and protection of individuals' personal information. In conclusion, wearable devices have great potential in emergency management and response, providing critical data for search and rescue operations, monitoring the health and safety of emergency responders, and tracking the physical and mental health of individuals during and after disasters (see a list of papers in Table 3). However, careful attention must be paid to the accuracy, reliability, privacy, and security of the data collected by these sensors.
# A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi
## 7 Application of ML on Hybrid OFDM Forms
Figure 12 shows the predicted value versus the actual value of the DCO constellation size for the case of linear regression. It can be seen that the plots do not follow the linear relationship in most cases. Figure 13 shows the predicted value versus the actual value of the DCO constellation size for the case of polynomial regression of order 3. The plots of Figure 13 show an approximately linear relationship. The effectiveness of the plots is calculated using the coefficient of determination or R\({}^{2}\) score, which measures how the predicted values fit the actual values. Table 1 shows the R\({}^{2}\) and RMSE values of the linear and polynomial regressions. In Table 1, results are shown for linear regression and polynomial regression of degrees 2, 3, and 4. The results for degree 5 and above are omitted as the R\({}^{2}\) values were very low. Table 1 shows that linear regression has a low R\({}^{2}\) value and thus is not suitable in this case. On the other hand, polynomial regression with degrees 2, 3, and 4 have high R\({}^{2}\) scores at different values of testing and training samples. This high R\({}^{2}\) score, as described in the literature [38], can ensure the fitness of the model. It can be seen from Table 1 that the best performance can be obtained when polynomial regression of order 4 was applied to estimate the constellation size of the DCO component of DPO for a given dimming target and given constellation size of PAM-DMT. This is for the case of 10% testing and 90% training samples. The high R\({}^{2}\) score of 98.18% and low RMSE value of 1.18 indicate that polynomial regression of order 4 is suitable [38] for predicting DCO elements within DPO-based LiFi. Similarly, the constellation size of the PAM-DMT component of DPO can be estimated for a given dimming target and given constellation size of DCO. Figure 12: Predicted versus actual \(M_{DCO}\) of dc-biased pulse amplitude modulated orthogonal frequency division multiplexing (DPO-OFDM) for the case of linear regression. Note that \(M_{DCO}\) stands for the constellation size of DC-biased optical OFDM (DCO-OFDM).
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Slotted-ALOHA (S-ALOHA)
#### (xiii) ALOHA by collision avoidance and ALOHA by prior notification (Aloha-CA, Aloha-AN)  
The authors of the [110] have proposed two different types of ALOHA protocol, i.e., ALOHA by conflict avoidance (ALOHA-CA), and ALOHA by prior notification (ALOHA-AN). These both types are making the communicating system being able to increase the efficiency and lower the energy consumption by decreasing the amount of collisions between data packets in a UWSNs. To avoiding conflicts in ALOHA-CA, each node monitors the channel's condition, so that to keep a check on the noisy moment generated by a given frame of the channel's independent alternatives. This process is upgraded to ALOHA-AN by adding a transmission node, that has the capability to sends an acknowledgement to its neighbors prior to data transfer between nodes. The ALOHA-AN is resource intensive as compared to ALOHA-CA.  
Figure 8: Comparison of average delay between different ALOHA protocols.  
Figure 10: Comparison of energy consumption between different ALOHA protocols.  
Figure 9: Comparison of dropped nodes between different ALOHA protocols.  
_(xiv)_: _Tone-Lolti (T-Lolti)_  
Another MAC energy-efficient protocol is created on a tone based booking technique [53]. The sender node communicates a small tone as well as hears the channel during a dispute cycle. The transmitter channel is reserved in case if other communicating tones are not present. However, if there an alternative tone accrues, the backs up will try to relate the message so that it can be understood by the receiving node [126].
# Evaluation of Land Subsidence during Groundwater Recovery
###### Abstract  
The Chao Phraya River basin is located in the central area of Thailand, which experiences many land subsidence issues due to groundwater pumping. The Department of Groundwater Resources (DGR) has been recording data on the changes in the groundwater level due to water pumping since 1960 until the present time. In 1997, the DGR issued a law regulating the use of groundwater due to its effect on the changes in the groundwater level. The changing of the groundwater level was separated into two periods. The first period is the high groundwater pumping ratio that led to a rapid decrease in the groundwater level of about 27 m from the ground surface. After the DGR issued the new law in the second period, the pumping ratio decreased and the groundwater level increased. The groundwater level tends to reach the ground surface. In the past, the groundwater level decrease was affected by the land subsidence. Therefore, this study focused on calculating and learning the behavior of the soil surface displacement during groundwater level recovery to the ground surface in Bangkok, Thailand. We obtained the 3D soil profiles adopted from eight boreholes from the Department of Public Works and Town & Country Planning. The soil profile data were verified by monitoring the data from the Department of Groundwater Resources (DGR) in the same area. The soil layers of the 3D soil profile were analyzed to calculate the soil surface displacement based on the consolidation theory of Terzaghi. We also examined the displacement behavior of the clay layers during the groundwater level recovery to the ground surface by assuming that the soil layers below the groundwater level do not settle or rebound. The surface displacement results showed that the surface ends to move upward or rebound, which is a similar trend to that reported in previous research. All the considered locations showed similar soil surface displacement trends. The soil displacement ratio is 0.21 to 0.53 cm/year during the groundwater recovery.  
B 20 June 2022  
Accepted: 2 August 2022  
Published: 6 August 2022
# Human Action Recognition Based on Improved Two-Stream Convolution Network
## 3 Improved Network Structure
### Network Structure of BiGRU  
The updated formula of GRU is as follows:  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]) \tag{11}\]  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]) \tag{12}\]  
\[\tilde{h_{t}}=tanh(W\cdot[r_{t}\times h_{t-1},x_{t}]) \tag{13}\]  
\[h_{t}=(1-z_{t})\times h_{t-1}+z_{t}\times\tilde{h_{t}} \tag{14}\]  
The above formulas can be expressed by \(h_{t}=GRU(x_{t},h_{t-1})\), where \(r_{t}\) represents the reset gate at time \(t\), \(z_{t}\) represents the update gate at time \(t\), \(\tilde{h_{t}}\) represents the state of candidate activation at time \(t\). The tilde represents the output of the tanh function, \(h_{t}\) indicates the state of activation at time \(t\), \(h_{t-1}\) represents the state of hidden layer at time \((t-1),W_{r},W_{z},W\) are the weight matrixes. GRU network can reduce the amount of computation and training difficulty under the effect equivalent to that of an LSTM network, but its memory ability is unidirectional. Inspired by GRU, our network uses the BiGRU network to realize the bidirectional memory and enhance its memory ability.  
BiGRU network is composed of two unidirectional GRU network in opposite directions. The hidden layer state of BiGRU network at time t can be obtained by weighted summation of forward hidden layer state \(\overrightarrow{h_{t-1}}\) and reverse hidden layer state \(\overleftarrow{h_{t-1}}\) with the calculation formula as follows:  
\[\overrightarrow{h_{t}}=GRU(x_{t},\overrightarrow{h_{t-1}}) \tag{15}\]  
\[\overleftarrow{h_{t}}=GRU(x_{t},\overleftarrow{h_{t-1}}) \tag{16}\]  
\[h_{t}=w_{t}\overrightarrow{h_{t}}+v_{t}\overleftarrow{h_{t}}+b_{t} \tag{17}\]  
where \(w_{t},v_{t}\) are weight matrixes, \(b_{t}\) is offset. The right arrow indicates the output of the sequential GRU unit, which is specifically expressed as the output of the lower-layer GRU unit \(h_{1}^{1},h_{2}^{1},h_{3}^{1}\) in Figure 5; the left arrow indicates the output of the reverse-order GRU unit, which is specifically expressed as the upper-layer GRU in Figure 5. The output of the unit \(h_{3}^{2},h_{2}^{2},h_{1}^{2}\) sums the three groups of inputs of the BiGRU network to obtain the output of the whole BiGRU network.  
Figure 7: Structure of GRU.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### Potential Applications
#### 4.2.2 UAV-Aided 5G  
UAVs may play an important role for 5G and beyond 5G (B5G), as future dronecell networks [49]. Because cellular-connected UAV communication has unique characteristics related to ground conventional cellular communication, this field offers new research challenges and opportunities. [PERSON] et al. [5] presented an overview of 5G in UAV communications, as well as emerging technologies and potential challenges.  
CR is an enabling technology for using UAVs in 5G networks. In 5G, CR-based UAVs may simultaneously access the wireless channel and fulfill their different roles in traffic surveillance, disaster management, and package delivery [32].
# A BIM-Enabled Workflow for the Rehabilitation of Heritage Steel Bridges
## 4 Case Study for the Savarsin Heritage Steel Bridge
### The IDS File  
Based on these documents, the IDS outlines the data requirements for specific tasks and/or phases in within the BIM workflow and ensures that all project stakeholders provide and manage information in a standardized, efficient manner.  
A natural language IDS example for heritage steel bridge rehabilitation is presented hereafter.  
* **General Information**
* Project ID: SAV-HB-2024;
* Phase: development, implementation, handover.
* BIM-related standards: ISO 19650-1:2019 [24], ISO 16739-1:2024 [40], ISO 7817-1:2024 [30].
* **Data Exchange Requirements**
* File formats
* IFC4 for interoperability
* Native file formats for design tools (e.g., Revit, Tekla Structures, Bonsai, etc.).
* CSV or JSON for IoT sensor data.
* Software compatibility: must be compatible with common BIM tools and platforms such as Navisworks, BIM 360, and open-source tools adhering to building SMART standards
* **Information Requirements by Phase**
* Development phase
* Structural assessments:
* **Input**: 3D scans (point cloud in.e57 format), initial condition reports (XML).
* **Output**: preliminary BIM model with structural analysis data.
* Environmental impact:
* **Input**: GIS data (.shp files) and climate analysis reports.
* **Output**: environmental risk assessments incorporated into the BIM model.
* Implementation phase
* Construction data:
* **Input**: specifications for retrofitting materials (steel grades, paint types, rivets).
* **Output**: updated BIM model showing real-time construction progress (weekly.ifc updates).
* Monitoring integration:
* **Input**: IoT sensor placement data (JSON).
* **Output**: integration into a digital twin for real-time monitoring.
* Handover phase
* As-built documentation:
* **Input**: final scans and updated design data.
* **Output**: comprehensive as-built BIM model with maintenance schedules embedded.
* Training materials:
* **Input**: operation manuals, sensor data management protocols.
* **Output**: interactive digital guides linked to the BIM model.
* **Data Validation and Quality Control**
* Validation tools
* Model-checking tools (e.g., Solibri, Navisworks).  
* Custom scripts for rule-based validation (e.g., Python scripts).
* Quality control metrics
* Completeness (percentage of mandatory fields populated).
* Accuracy (validation against reference standards e.g., ISO 1090-1, ISO 1090-2).
* **Governance and Access Control**
* Access levels
* Read-only for public authorities.
* Edit access for BIM managers and contractors.
* Audit trail
* Maintain version history of the BIM model and associated documents.  
Complementing the IDS framework described before and enabling the development of an XML-based IDS, Table 2 outlines specific data fields and their requirements, tailored to the case of the rehabilitation of heritage steel bridges.  
Based on information requirements, the IDS XML file is developed, with an example presented in Appendix A.
## 2 Materials and Methods
### Feature Selection Methods  
Feature selection (FS) methods aim to select a subset of features from an original feature set, identifying the most relevant features and discarding the irrelevant ones [35]. FS becomes crucial in predictive tasks because decreasing the number of features could lead to maximizing generalization capacity and reaching better predictive results [36]. Additionally, FS may help in better data understanding and interpretability, which is crucial in certain areas, such as healthcare. FS methods are generally split into three categories [36]: filter, wrapper, and embedded methods.  
Filter methods select features that present a strong relationship with the target and are performed independently of any predictive model. These methods are computationally efficient and fast because they do not involve a training stage of predictive models [36]. In this work, chi-squared (chi2) [37] and mutual information (MI) [37] were considered as filter methods.  
Wrapper methods iteratively train a predictive model to evaluate and choose the feature subsets [38]. Depending on whether a forward or backward selection approach is followed, the features are added or discarded until the best subset is identified [39]. Since these methods use several predictive models to select the feature subsets, they can be computationally complex and time-consuming [38]. In this work, a backward selection approach was considered, where the ML algorithm is initially trained with the whole set of features to which one feature is discarded at each repetition until the algorithm is trained with the empty set of features (backward selection approach) [40]. For performing wrapper methods, a filter-based method using ML was employed to set the hierarchy of importance of the features and select which feature to discard at each iteration.  
Embedded methods intrinsically perform feature selection during the ML algorithm training. We selected two tree-based embedded methods: decision tree (DT) and random forest (RF), where the importance is used to select the features. Also, the regularized  
Figure 1: Example of a Clarke error grid plot.  
method least absolute shrinkage and selection operator (LASSO) [41] was included in the analysis.
# Computing Persistent Homology of Directed Flag Complexes
## 2 Results
### Sample Computations
The complex is 21-dimensional with more than 1.2 trillion 11-dimensional simplices. Computation was run in parallel on two nodes of an HPC cluster with 256 CPUs each, required 1 GB of memory and took five days to complete (run as 10 different jobs of 24 h each). Figure 2: Cell count in the Blue Brain reconstruction of the somatosensory cortex. Computation was run on an HPC cluster using 256 CPUs, required 55.69 GB of memory and took 7.5 h to complete. The somatosensory cortex (Figure 2) is a digraph with about 1.7 million vertices and 1.1 billion edges (average connection probability 0.04%). The Blue Brain Project model of the full mouse cortex is available online [8]. The full reconstruction consists of approximately 10 million neurons. Its synaptic connections fall into three categories. Local (short distance) connections, intermediate distance connections and long distance connections. We computed the cell count of the PL region of the left hemisphere in the reconstruction, consisting of approximately 64,000 neurons and 17.5 million local connections (average connection probability 0.4%). We did not consider any connections but the local ones. Interestingly the number of neurons in the model of the PL region [8] is roughly twice that in the reconstruction of the neocortical column studied in [6], and the probability of connection is roughly half. Hence we expected a comparable performance of Flagser. The results are therefore quite surprising. The likely reason for the difference in computation speeds is the higher dimensionality of the model of neocortex of the mouse, resulting in a gigantic number of simplices in the middle dimensions (approximately 1.1 trillion 11-dimensional simplices), compared to the neocortical column of the rat, with the former containing simplices of dimension 21 (see Figure 3) and the latter only containing simplices up to dimension seven. These computation were done on an adapted version of Flagser available at [9]. This alternate version has been adapted in the following ways: the code to compute homology is removed as this is not needed, the option to load the graph from compressed sparse matrix format is included as this speeds up the time required to read the graph, the graph can be stored using Google's dense hash map to reduce the memory needed for very large graphs, and partial results are printed during run time which allows for the computation to be resumed if it is stopped (for example, if the process is killed due to time limitations on HPC).