esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synthetic Cases
### Multi-Layer Inversion
The best fits for the synthetic study with different sample size are shown in Figure 4. For this example, the small ensemble sizes, 100 and 1000, lead to strong fluctuations that hinder a straightforward interpretation of the best fit functions. As expected, it shows that a larger ensemble leads to smaller fluctuations around the synthetic true. \begin{table}
\begin{tabular}{l c c c} \hline  & **VES** & **FDEM** & **Joint** \\ \hline RMSE [mS/m] & 18.32 & 15.05 & 11.89 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of root-mean-square errors (RMSEs) between the synthetic true model and the three best fit models (ensemble means) for separate and joint inversion of VES and FDEM data. Figure 3: Kalman ensemble generator (KEG) inversion results for the same synthetic true model using the same prior model for vertical electrical sounding (VES) data, frequency domain electromagnetic (FDEM) data, and joint inversion of VES and FDEM data. We first correct the best fit for the synthetic true model by subtracting the respective true conductivity value from the best fit functions, in logarithmic domain, in order to quantify the reduction in fluctuation. For discrete layers that cover a subsurface layer boundary, we subtract a weighted mean based on the share of of the discrete layer above and below the layer boundary. This computation results in an approximately Gaussian distribution around zero for all model parameters. The root mean square (RMS) of these distributions might serve as a measure for the sampling error introduced by small ensemble sizes (Table 2). In general, the STD becomes smaller with an increasing sample size. However, the decrease of the STD is steeper for the comparison of small absolute ensemble size. In contrast, the STD-decrease between 10,000 and 100,000 ensemble members is minor. Likewise, this minor difference reflects in the similarity of the best fit functions in Figure 4. Therefore, it is possible to derive a threshold value above which an increase in the sample size does not yield a significant improvement in the posterior estimate. This threshold is likely dependent on the data type and the model complexity. However, the threshold might be approximated using synthetic benchmarking, as presented here. A similar observation has been made for other approximate Bayesian methods (e.g., [13]).
# Digital Content Management Using Non-Fungible Tokens and the Interplanetary File System
## 4 Digital Content Management (DCM) System
### Discussion
\begin{table}
\begin{tabular}{l l} \hline \hline
**Issue** & **Contents** \\ \hline IPFS Nodes and & - & IPFS is a distributed file system that identifies data via hashes and \\ Network & - & stores them across a network. \\ Infrastructure & - & Adeuate nodes are required for data retrieval based on these hashes, \\  & necessitating a robust network infrastructure for high availability and \\  & data integrity. \\ \hline Smart Contracts & - & Utilizing a blockchain platform is essential for creating and managing \\ and Blockchain & - & This involves executing smart contracts to guarantee the uniqueness \\ Platforms & - & and ownership of NFTs, requiring consideration of security, scalability, and transaction processing capabilities. \\ \hline Data Security & - & To uphold digital content security, encryption technologies are crucial. \\ and Encryption & - & Establishing secure storage and transmission protocols through \\  & encryption standards and robust key management systems is necessary. \\ \hline Scalable & - & Managing and storing large volumes of files and metadata requires \\ Databases and & - & scalable databases and storage solutions. \\ Storage & - & Efficiently handling distributed storage, replication, and backups of \\  & data is essential. \\ \hline \hline \end{tabular}
\begin{tabular}{l l} \hline \hline \multirow{4}{*}{Distributed Computing Resources} & - & Processing and analyzing data necessitate distributed computing resources. \\  & - & Cloud computing enables the distribution of large-scale tasks, \\  & enhancing performance through parallel processing. \\  & - & These technical elements ensure the functionality and stability of \\  & managing digital content using NFTs and IPFS. \\  & - & Designing and implementing systems considering aspects like network \\  & establishment and maintenance, data security and integrity, as well as \\  & distributed processing and storage are critical for constructing a \\  & scalable and efficient digital content management system. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Requirements of scalability and time efficiency. These technical elements ensure the functionality and stability of managing digital content using NFTs and the IPFS. Designing and implementing systems considering aspects like network establishment and maintenance, data security and integrity, as well as distributed processing and storage are critical for constructing a scalable and efficient digital content management system.
# Uncertainty Quantification for Infrasound Propagation in the Atmospheric Environment
## 1 Introduction
The Sobol sensitivity analysis method is used to quantify the uncertainty of the parameters affecting infrasound propagation, to determine the parameters that have a decisive influence on infrasound propagation, and to reveal the mechanism generating infrasound propagation uncertainty. There are many kinds of uncertainty parameters in the atmosphere. The tau-p model [25; 26; 27; 28; 29], also known as the ray-tracing model [25], is used as a numerical simulation of infrasound propagation in a related paper. The atmospheric environmental parameters in this model can affect infrasound propagation. The sensitivity analysis of the atmospheric environmental parameters in the tau-p model is carried out using Sobol sensitivity analysis in order to rank the importance of the environmental parameters. Therefore, the objective of this paper is to quantify the uncertain atmospheric environmental parameters that affect infrasound propagation in the atmosphere. The Sobol sensitivity-based approach is proposed to be able to rank the importance of uncertain atmospheric environmental parameters affecting infrasound propagation. First, the atmospheric parameters affecting infrasound propagation are identified in this paper. Secondly, the model of infrasound propagation in the atmosphere is determined. Finally, the importance ranking of the atmospheric parameters affecting infrasound propagation is performed using Sobol sensitivity analysis. This paper is organized as follows. The infrasound propagation model is introduced in Section 2. The principles of Sobol sensitivity analysis and Sobol sequence are introduced in Section 3. The sensitivity analysis of the infrasound propagation model using data from the horizontal wind model (HWM93) and the mass spectrometer incoming scatter radar model (MSISE00) is presented in Section 4. Finally, conclusions are drawn in Section 5.
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
## 6 Conclusions  
In order to tackle the challenge of multi-step traffic speed prediction, we proposed an ensemble model, i.e., the Detrending and Direct Strategy Ensemble Learning Model (DDSELM). The detrending technique could separate original dataset into mean trends and residuals, and the direct strategy could decrease the cumulative error in the prediction process. To validate the effectiveness of our model, we used several benchmark models as a comparison model, including SVM, CATBOOST, KNN, and BAGGING, based on a field dataset collected in the city of Zhongshan, China. Predictive results showed that our model outperformed four benchmark ones in terms of the MAPE, MAE, MSE, and CV under three prediction intervals. For one-step-ahead prediction, the MAPE of DDSELM for northbound segments is 7.08% (14.90% for southbound segments). For two-step-ahead and three-step-ahead prediction, the MAPE of DDSELM for northbound segments is 8.77% and 10.34% (16.99% and 17.82% for southbound segments), respectively. In future works, it is necessary to consider the impact of road network characteristics and specific incidents on prediction accuracy. Moreover, the proposed model can also be integrated into some advanced ITS to alleviate traffic congestion, for example, real-time route planning systems, traffic management systems, and traffic signal control systems.  
Conceptualization: B.F. and J.X.; methodology: B.F. and Y.L.; formal analysis and validation: B.F. and J.X.; investigation: B.F. and Y.L.; data collection and resources: Y.Z.; writing--original draft preparation: B.F.; writing--review and editing: J.X., Y.L., and Y.Z.; funding acquisition: J.X. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (61873098,61903145).  
Not applicable.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
The authors especially appreciate data support from the traffic police detachment, Zhongshan, China.  
The authors declare no conflict of interest.
# Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks
## 3 Proposed Architecture
### Temporal Convolutional Network (TCN)  
A recent study [36] has shown that a simple convolutional architecture is superior to classic recurrent convolutional networks, such as LSTM and GRU, on various tasks and datasets while exhibiting longer effective memory. Compared to the language modeling architecture of LSTM [28], TCNs have longer memory networks and can efficiently handle longer inputs.  
We should emphasize that lipreading is the task of Sequence Modeling. Our goal is to replace BLSTM or BGRU with TCN to solve the a disappearing training gradient and slower convergence. Figure 4 shows the basic architecture of the TCN.  
For the sequence modeling task, an input image sequence for \(x_{0},x_{1},\ldots,x_{T}\), and we wish to predict some corresponding outputs \(y_{0},y_{1},\ldots,y_{T}\) at each time:  
\[y_{0},y_{1},\ldots,y_{T}=f(x_{0},x_{1},\ldots,x_{T}) \tag{1}\]  
A simple causal convolution network can only have a limited size of feature information in the deep historical network. It is very challenging to build a sequence model using simple sequence convolution. Our solution is to implement a dilated convolution neural network to extend the receptive field. We use Dilated Convolutions to apply to the lipreading task and use Formula (2) to briefly describe its outline:  
\[F(s)=(\mathbf{x}\!*\!_{d}f)(s)=\sum_{i=0}^{k-1}f(i)\mathbf{s}_{x-d\cdot i} \tag{2}\]  
where \(x\in\mathbb{R}^{n}\) as a 1D sequence input, \(f:\{0,\ldots,k-1\}\mapsto\mathbb{R}\) as a filter, \(d\) is the dilation factor, \(k\) is the filter size, and \(s-d\cdot i\) accounts for the direction of the past.  
More radically, we replace ordinary convolutional networks with a residual block [37]. It consists of a convolutional layer with 512-dimensional kernels of 5 size and the stride of 3 size, followed by Batch Normalization (BN, [45]), Rectified Linear Units (ReLU, [46]), and Dropout [48].
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
###### Abstract  
Ensuring reliable communication in remote or disaster-affected areas is a technical challenge due to unplanned deployment and mobilization, meaning placement difficulties and high operation costs of conventional telecommunications infrastructures. To address this problem, unmanned aerial vehicles (UAVs) have emerged as an excellent alternative to provide quick connectivity in remote or disaster-affected regions at a reasonable cost. However, the limited battery autonomy of UAVs restricts their flight service time. This paper proposes a communication system based on a tethered UAV (T-UAV) capable of continuous operation through a wired power network connected to a ground station. The communications system is based on low-cost devices, such as Raspberry Pi platforms, and offers wireless IP telephony services, providing high-quality and reliable communication. Experimental tests assessed power consumption, UAV stability, and data transmission performance. Our results prove that the T-UAV, based on a quadcopter drone, operates stably at 16 V and 20 A, ensuring consistent VoIP communications at a height of 10 m with low latency. These experimental findings underscore the potential of T-UAVs as cost-effective alternatives for extending or providing communication networks in remote regions, emergency scenarios, or underserved areas.  
unmanned aerial vehicles (UAVs); tethered UAV; T-UAV; energy management; wireless VoIP; emergency network infrastructure +
Footnote †: journal: Future Internet
## 4 Machine Learning Algorithms
### Data Preprocessing
#### 4.1.4 Dataset Construction  
The study utilized logging parameters from five sub-layers of 12 cored wells in the Qiongzhusi Formation, southern Sichuan Basin, as training samples. During data preprocessing, the original logging data underwent depth calibration, followed by anomaly removal based on multi-tier quality control criteria: intervals with a borehole enlargement rate exceeding 15% (derived from caliper logs, CALI) were excluded [25]; segments exhibiting pyrite content above 5% (identified via Elemental Capture Spectroscopy, ECS logging) were discarded; and layers with a mud-filtrate invasion correction factor below 0.8 (determined by resistivity radial difference analysis) were removed. To ensure sample representativeness, the processed dataset was partitioned into training and test sets using stratified random sampling at a 7:3 ratio, yielding 900 validated samples (630 for training and 270 for testing). The dataset comprises nine logging parameters, including Gamma Ray (GR), Uranium-Free Gamma Ray (UGR), Acoustic Transit Time (AC), Compensated Neutron Log (CNL), Bulk Density (DEN), Potassium (K), Thorium (Th), and Uranium (U), with the first eight serving as input features and laboratory-measured shale TOC content as the output target.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
Primarily, the timeline was incorporated to enable the NPD to monitor the mission process in real time. Secondly, the global start node and the global end node are added to ensure that the group network plan diagram is also suitable for the time parameter calculation method of the traditional NPD. Thirdly, we have designed a queue-based mission topology sorting method and an automated dual-code coding method, which allow the NPD to deal with a collection of missions in any order when performing mission time parameter calculations. * A mission process closed-loop feedback control system based on the dual-code group network plan diagram is designed to model the mission process control problem as a mapping from mission states to plan adjustment. This system achieves real-time sensing of mission process deviations and precise control of mission plans. * A fast and precise mission process control strategy is designed based on the mission process closed-loop feedback control system. The solution of the control strategy is presented through the analytical method, and its convergence and effectiveness are rigorously proven. This makes the control strategy fully interpretable. Furthermore, the maximum acceptable deviation of the mission process is mathematically proven, providing an important decision support for mission commanders.
## 2 Materials and Methods
### The Active Approach  
Modern microwave communication networks are remotely managed. That is, the network operators can access and inquire the status of the different CMLs remotely. Specifically, most CML hardware is connected to the provider's intra-net, and uses the simple network management protocol (SNMP) to submit queries to the CMLs, and receive the requested information. The active approach is to use the SNMP to collect RSL measurements dedicated for weather monitoring. A recent publication details this methodology, and establishes a set of open-source tools which can be used to actively access the CMLs of main manufacturers, and receive the instantaneous RSL (and TSL) samples [9]. With this approach, RSLs (and TSLs) are available in real time as instantaneous samples, at sampling intervals that can be as small as 10 s. Note that to avoid unnecessary traffic load for the cellular provider, the active approach also requires adding a designated server for handling the RSL measurements. Also, in most cases, the RSL and the TSL samples collected by this approach still suffer from quantization, as the quantization process is a property (and a limitation) of the sampling hardware itself.  
CML measurements collected by the active approach are most suitable for environmental monitoring, both because of the excellent temporal resolution and the lack of the highly nonlinear min/max processing of the NMS. Moreover, the availability of real time measurements is most attractive for applications such as now-casting and flood prediction. However, the active approach requires a high level of involvement from the cellular provider, including permission to a third party to cross its firewall. Most providers are reluctant to allow it, as they see it as a potential risk to their main business, i.e., communication.  
Table 1 summarizes the two approaches.
## 4 The IDCP Algorithm  
This section describes the design of IDCP in detail, which is shown in Algorithm 1. IDCP works by iteratively adjusting the task-scheduling decision such that the solution is optimized. At every iteration, IDCP executes three steps: (1) node selection; (2) node assignment; and (3) solution update. In step 1, some nodes, which may be critical to the solution quality, are selected. In step 2, the nodes selected are rescheduled to retrieve a better solution. In step 3, the solution is updated based on the new task-scheduling decision. Figure 4 illustrates an example of the node migration steps in one iteration.  
```
1:Initialization
2:\(S^{*}\leftarrow\emptyset\)
3:while exit criteria are not satisfied do
4: Get \(S_{n}\leftarrow\{\) nodes on the current critical path\(\}\)
5:while\(S_{n}\
eq\emptyset\)do
6: Select a node \(n_{i}\) from \(S_{n}\) randomly
7: Get candidate positions for \(n_{i}\)
8: Calculate \(Mobility(n_{i},m)\) for all candidate positions
9: Assign\(n_{i}\) to the candidate position with the maximal \(Mobility(n_{i},m)\)
10: Remove \(n_{i}\) from \(S_{n}\)
11: Update EST and LST for all nodes in \(S_{n}\)
12: Get the solution \(S\)
13:if\(S\) is currently the best then
14:\(S^{*}\gets S\)
15:endif
16:endwhile
17:endwhile
18:Return \(S^{*}\)
```  
**Algorithm 1** The IDCP Algorithm  
_where \(C(i)\) represents the set of children nodes of \(n_{i}\). If \(n_{i}\) is the dummy node we insert at the end of DAG, then \(LST^{G}(n_{i})=\infty\)._  
\(LST^{N}(n_{i})\) _is constrained by the start time of \(nxt(n_{i})\). Therefore, one can write \(LST^{N}(n_{i})\) as:_  
\[LST^{N}(n_{i})=LST(nxt(n_{i}))-T_{ex}(n_{i}) \tag{19}\]  
_If \(n_{i}\) has not yet been scheduled or \(n_{i}\) is the ending dummy node, \(LST^{N}(n_{i})\)= \(T_{dl}\). Analogously, the LST of all nodes can be calculated by traversing the DAG from bottom to top._  
**Definition 3**.: _A critical path of a DAG is a set of nodes and edges, forming a path from the starting dummy node to the ending dummy node, of which the sum of execution time and communication time is the maximum._  
According to the definition, the nodes on the critical path are vital to the finish time of the application. Therefore, high priority shall be placed on optimizing the schedule of these nodes. To identify the nodes on the critical path, we simply check the equality of its EST and LST value [31].
## 1 Introduction
The main contributions provided by this study are summarized as follows:  
* We introduced the MIRA that identifies a disease based on user's chief complaint, understands single and multiple intent statements about a specific medical condition, and generates an appropriate response. * We added an identity and access manager, a session manager, and security event logging and monitoring to the MIRA architecture. These provide strong authentication, manage the conversational state, and monitor the system for anomalies, respectively. * We created a dataset of 816 patient chief complaints that were manually validated under the supervision of medical specialists, and were classified into glaucoma, diabetes, and other labels under the broad category of diseases. * We designed stock phrases from the recorded 816 dialogue corpora that contain 11,532 utterances. Each utterance was manually annotated for intent and context identification. * We evaluated MIRA based on a performance measure (including accuracy, precision, sensitivity, specificity, and f-measure), task completion, security, and user experience. The rest of this paper is organized as follows. The overview of literature related to virtual medical assistants is described in Section 2. Then, Section 3 provides a comprehensive description of the MIRA methodology including system architecture, digital brain, and a case study. Subsequently, the evaluation of MIRA is presented in Section 4. Finally, Section 5 summarizes the work proposed in this study.
# Technology Behavior Model--Beyond Your Sight with Extended Reality in Surgery
## 2 Literature Review
### Determinants of the Adoption of XRSG in Surgery
#### 2.3.1 Extended Really Usefulness (XRU)  
XRU refers to the ability to help medical experts perform better in clinical operations. Medical experts can learn and use XRSG during surgery to perceive their role in the procedure and whether it contributes to the procedure the efficiency of surgery by using the use of extended reality technologies, including human-space imaging, visualization enhancement, and eye-tracking human-computer exchange technologies. XRU stems from changes in perceived usefulness (PU) in TAM, which affects the use of behavioral intention in TPB. This is mainly based on preoperative pictures, data, and own experience to estimate the surgical approach. Existing extended reality technologies can improve surgeon's ability to recognize the vision, imaging, and localization for surgical purposes. These studies suggest that the use of XRSG by medical experts can help improve the success rate of surgery. Therefore, the hypothesis is as follows:  
**Hypotheses 1 (H1).**_XRU has a positive effect on the intention to adopt SGs (ITASG) surgical behavior of medical experts._
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 2 Trajectory Planning Based on MINCO Trajectory Classes
### Trajectory Planning Based on MINCO Trajectories and Safe Corridors
#### 2.2.1 Underground Coal Mine Environmental Constraints and Map Representation  
The constraints in trajectory planning in underground environments are mainly safety constraints, i.e., the robot is required to avoid environmental obstacles such as large coal blocks and debris during the planning process.  
Figure 1: The optimal trajectory diagram of path point and time vector parameterization.  
The map used is a voxel map, and the voxels can be viewed as a rough version of the point cloud, as shown in Figure 2. Occupancy information is stored in each voxel cell, and there are two states of occupancy information: the occupancy state (1) indicates that the voxel cell is occupied by an obstacle; and the null state (0) indicates that there is no obstacle occupying the cell, and the trajectory can pass through the voxel cell. As long as the obstacle appears in the voxel unit, no matter how much space the obstacle actually occupies in the voxel unit, it is always considered that the voxel unit is occupied and the trajectory cannot pass through the voxel unit.