esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Smart Grid
Also, direct load control can add distributed energy resources to SG to supply higher load when the power grid produces extra power [7]. Distributed energy resources, electric vehicles, gateways, home energy display, smart devices, SMs, and tools for power consumption control can connect to each other via a home area network. Bluetooth, IEEE 802.11b, IEEE 802.11s, IEEE 802.3 az-2010, power line communication, and ZigBee technologies can be used for home area network [45]. Several SMs send their data to the corresponding data concentrator through a neighborhood area network (NAN) [34]. For example, several homes that are supplied by one transformer create a NAN. Thus, NAN should carry a large volume of data and satisfy their quality of service requirements. The following technologies and networks are some candidates for NAN: family standards of IEEE 802.11, the third and the fourth generations of wireless cellular networks (e.g., long-term evolution (LTE), worldwide interoperability for microwave access (WiMAX), wideband code-division multiple access), and optical networks (e.g., passive optical networks or Ethernet passive optical networks). Also, equipment in the field should be managed by a field area network (FAN). The geographical coverage of a FAN is like NAN. Therefore, similar communication networks and technologies can be used for FAN too. Data concentrator aggregates and compresses data from SMs in uplink connection and relays data to SMs in downlink connection. Data concentrator enhances scalability and reliability of SG, reduces power consumption of SMs, and decreases data collision between SMs' transmitted data. But it increases the delay in transmitting SMs' data somewhat. Some data concentrators are connected to the central system via a wide area network (WAN). Long-range and high-bandwidth communication technologies such as fiber optic and wireless cellular networks (e.g., WiMAX, LTE, and LTE advanced can be used in WAN [46]. The central system collects and analyzes SMs' data. It can have several components (connected via a local area network) such as a meter data management system [47], geographic information system, outage management system, consumer information system, power quality management, and load forecasting systems. As an example, a meter data management system gets SMs' data, stores them in databases, and processes them [45].
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 3 Experiments and Results
### Simulation Studies  
#### 3.2.1 Simulation Datasets  
To evaluate the performance of our proposed cognitive diagnostic approach, we generated artificial simulation data for model testing by manipulating five factors, following previous studies [6; 22; 23]. These factors included the number of candidates, the number of skills examined, the number of test items, the quality of elements, and the model used for simulation data generation. In each dataset, the number of candidates \(N\) was set to 100, 200, 300, and 500, while the number of skills was set to three and five. The quality of an item was measured using its guessing parameter \(P(0)\) and slip parameter \(1-P(1)\). When \(P(0),1-P(1)\in\text{U}(0,0.15)\), the item is deemed to be of high quality, and when \(P(0),1-P(1)\in\text{U}(0.15,0.3)\), it is believed to be of low quality. It should be noted that U denotes a uniform distribution. We designed two Q-matrices for generating simulation data. The generating rules of the Q-matrix were as follows: (a) ensure that there were items that required at least one attribute in the Q-matrix. (b) the remaining items were selected from all \(2^{K}-1\) items randomly, to satisfy the predetermined test length, where, as shown in Equation (17), \(\mathbf{Q}_{1}\) consists of 10 question items and 3 skills.  
\[\mathbf{Q}_{1}^{\top}=\begin{pmatrix}1&0&0&1&0&1&1&1&0&1\\ 0&1&0&0&1&1&0&1&1&1\\ 0&0&1&1&1&0&1&0&1&1\\ 0&0&1&1&1&0&1&0&1&1\end{pmatrix} \tag{17}\]  
Likewise, as shown in Equation (18), \(\mathbf{Q}_{2}\) consists of 31 question items and 5 skills.  
\[\mathbf{Q}_{2}^{\top}=\begin{pmatrix}101010101010101010101010101010101\\ 01100110011001100110011001110011\\ 00011110000111100001111100001111\\ 00000001111111100000000011111111111\\ 00000  
score \(\theta_{i}=[\theta_{i1},\theta_{i20},\cdots,\theta_{iK}]^{\top}\) for \(K\) skills followed the multivariate constant distribution, i.e., \(\mathbf{\theta}_{i}\sim MVN(\mathbf{0}_{K},\sum)\), where \(\mathbf{\theta}_{K}=[0,0,\cdots,0]^{\top}\) is a zero vector with \(K\times 1\) dimensions, and \(\sum\) is as shown in Equation (19).  
\[\sum=\begin{bmatrix}1&0.5&\cdots&0.5\\ 0.5&1&\ddots&\vdots\\ \vdots&\ddots&\ddots&0.5\\ 0.5&\cdots&0.5&1\end{bmatrix} \tag{19}\]  
where a medium correlation between attributes is denoted, and the elements on the non-diagonal of the matrix \(\sum\) are set to \(0.5.\mathbf{\alpha}_{i}=[\alpha_{i1},\alpha_{i2},\cdots,\alpha_{iK}]\) is the skill acquisition pattern of the student being simulated, where \(\alpha_{ik}\) is calculated as shown in Equation (20).  
\[\alpha_{ik}=\begin{cases}1,&\text{if}\ \ \theta_{ik}\geq\Phi^{-1}(\frac{k}{K+1}) \\ 0,&\text{otherwise}\end{cases} \tag{20}\]  
where \(\Phi^{-1}(\cdot)\) is the inverse function of the cumulative distribution function of the standard normal distribution.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### Barcode  
Barcode is one of the most used technologies in industry for data entry with much less error rate and higher speed than those fully dependent on a human operator. A system using barcode technology consists of four main modules including barcode labels, scanner, decoder, and a database for gathering and analyzing information [20]. The barcode labels are available in one-and two-dimensional formats. Generally, the capacity of two-dimensional barcode systems is about 100 times larger than one dimensional system. However, one-dimensional barcodes are more common in industry by having a more accepted governing standard. The scanners are usually available in three main categories such as wand scanners, charged couple devices (CCD) scanners and laser scanners. They are categorized based on maximum length of barcode to be read, maximum distance away from barcode label, ability to read barcode labels attached to irregular objects and scanning reliability. Although wand scanners can read longer barcode labels, laser ones are considered superior with regard to all other criteria mentioned. Accordingly, laser scanners were used as data collection devices in the developed system. Decoders, on the other hand, receive the digitized barcode signals from scanners and then decode them to the correct data. Decoders are available in both fixed and portable versions; however, the latter one is more common in construction projects because of the distributed locations of activities in a construction project. Finally, the data are transmitted to the hub computer over a wired or wireless network [20, 21].  
A practical application of a barcode system in the construction industry is documentation of engineering data. In this way, barcode labels are produced and, then get assigned to each document. After that, coded information is scanned and transmitted to the database. For retrieving the information, developed queries are utilized and results will be shared with industry practitioners. Besides, the performance of this system can be improved by using feedbacks from industry practitioners. A system which utilize this technology is expected to improve productivity and to save time and cost of various tasks such as procurement and document control [20, 25].
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Global Spectrum Planning
#### 6.1.1 Planning Input  
The spectrum planning module requires various inputs to function effectively. These inputs include the following aspects:  
System frequency band: The system design determines the authorized frequency band and some public frequency bands. These frequency bands, in turn, establish the range for frequency planning.  
Figure 8: Function modules of spectrum planning.  
Regional forbidden frequency band: Each region specifies frequency bands that cannot be used by other wireless systems, based on their unique needs and spectrum usage. When SBSs communicate with users in these areas, it is essential to avoid using these forbidden frequency bands.  
Historical spectrum data: The recorded and stored historical spectrum data can be used to identify patterns of temporal and spatial usage through data mining techniques. These patterns help predict future spectrum usage, which in turn informs spectrum allocation and management plans.  
Historical interference data: The recorded and stored historical interference data can be used to identify patterns of temporal and spatial interference. By analyzing these patterns, future interference events can be predicted, and the predicted results can inform spectrum planning.  
Historical user/service data: The temporal-spatial distribution of users and services is recorded and stored. Through data mining, the temporal-spatial laws governing the distribution of users and services are obtained. These laws are then used to predict the temporal-spatial distribution of active users and services in the future, serving as input for spectrum planning.  
Spectrum usage status: Spectrum planning uses two key types of input. First, it incorporates the current spectrum usage status of various areas and the space electromagnetic environment data sensed and collected by spectrum monitoring equipment. Second, the historical spectrum usage status provides essential data for mining and predicting future patterns or trends in spectrum usage.
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 2 Related Works  
Lidar odometry has been a hot topic in recent years. Lidar odometry is, in essence, equivalent to scan matching, which tries to estimate the relative transform between two frames of point clouds. The scan matching methods can be classified into three categories: point-based, mathematical property-based, and feature-based. The Iterative Closest Point (ICP) algorithm [8] and its variants [9] are the most popular point-based methods which estimate the relative transform through finding the nearest points between the two scans. For the mathematical property-based method, such as the Normal Distribution Transform (NDT) algorithm and its variants (D2D-NDT [10] and P2D-NDT [11]), the observed points are modeled as a set of Gaussian probability distribution. For the feature-based method, there have been various features applied, such as the line segment feature [12], corner feature [13], and grid feature [14]. [PERSON] et al. proposed a method called Lidar Odometry And Mapping (LOAM) [15; 16], which extracts feature points located on sharp edges or planar surfaces, and matches these feature points using either the point-to-line distance or the point-to-plane distance.  
All these lidar odometry algorithms can only output localization results at a frequency of less than 10 Hz, which might not be fast enough. Some recent approaches have started to fuse the lidar odometry with the IMU using either the Kalman Filter framework [2; 3; 4; 5; 17; 18] or the factor graph representation [6; 7].  
In [2], the authors proposed a localization system which combines a 2D Simultaneous Localization and Mapping (SLAM) subsystem with a 3D navigation subsystem based on IMU. This system can estimate the accurate state of an unmanned ground vehicle (UGV), but the usage of 2D SLAM is not suitable for the off-road environment. In [3], a hybrid lidar odometry algorithm was proposed which combines the feature-based scan matching method with the ICP-based scan matching method, and the lidar odometry result was then fused with the IMU in an EKF framework. In [6], a bunch of IMU measurements were considered as a relative constraint using the pre-integration theory, and the results of lidar odometry were fused with IMU measurements in a factor graph framework. However, in all these previous works, the IMU information was only fused with the lidar odometry in the result level; whilst in this paper, we try to utilize the IMU information to aid the whole process of the lidar odometry algorithm.
## 4 Results
Because T02 was collected in a GNSS-challenged environment, there were obvious divergences in the velocity estimation around 362,900 s, 363,600 s, and 364,000 s. The inclusion of vision weakened the impact and improved the accuracy of the velocity estimation in three directions. It can be seen from the statistics in Tables 6 and 7 that vision could not bring about obvious improvements in an open-sky environment but greatly improved the velocity estimation accuracy in a GNSS-challenged environment. The error of velocity estimation in T02 was reduced by the inclusion of vision from 0.12 m/s, 0.07 m/s, and 0.07 m/s to 0.03 m/s, 0.05 m/s, and 0.05 m/s in the north, east, and down directions, respectively. The improvement in all three directions was more than 20%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **Informal [m]** & **Improvement** & **Vertical [m]** & **Improvement** \\ \hline GC-PPP-RTK & 0.70 & 1.16 & \\ GC-PPP-RTK/INS & 0.48 & 31.4\% & 0.73 & 37.1\% \\ GC-PPP-RTK/INS/Vision & 0.44 & 37.1\% & 0.67 & 42.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMS of the velocity error of T01 in north, east, and down directions, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **Horizontal [m]** & **Improvement** & **Vertical [m]** & **Improvement** & **Availability** \\ \hline G-PPP & 0.37 & 0.35 & 100\% \\ G-PPP-RTK & 0.09 & 75.7\% & 0.26 & 25.7\% & 100\% \\ GC-PPP-RTK & 0.09 & 75.7\% & 0.22 & 37.1\% & 100\% \\ GC-PPP-RTK/INS & 0.08 & 78.4\% & 0.09 & 74.3\% & 100\% \\ GC-PPP-RTK/INS/Vision & 0.08 & 78.4\% & 0.09 & 74.3\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMS of the positioning error of T01 in horizontal and vertical directions, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **Horizontal [m]** & **Improvement** & **Vertical [m]** & **Improvement** & **Availability** \\ \hline G-PPP & 1.61 & 2.40 & 90.5\% \\ G-PPP-RTK & 1.55 & 3.7\% & 2.30 & 4.2\% & 90.5\% \\ GC-PPP-RTK & 0.70 & 56.5\% & 1.16 & 51.7\% & 95.1\% \\ GC-PPP-RTK/INS & 1.84 & \(-\)14.3\% & 0.98 & 59.2\% & 100\% \\ GC-PPP-RTK/INS/Vision & 0.83 & 48.4\% & 0.91 & 62.1\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMS of the positioning error of T02 in horizontal and vertical directions, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **Informal [m]** & **Improvement** & **Vertical [m]** & **Improvement** \\ \hline GC-PPP-RTK & 0.70 & 1.16 & \\ GC-PPP-RTK/INS & 0.48 & 31.4\% & 0.73 & 37.1\% \\ GC-PPP-RTK/INS/Vision & 0.44 & 37.1\% & 0.67 & 42.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMS of the position difference of T02 with part of the epoch excluded in horizontal and vertical directions, respectively.
# A New Top-Mounted Shear-Hinge Structure Based on Modal Theory and Rubber-Pad Damping Theory
## 5 Installation Modal Testing
### Modal Test Principle  
The basic idea of the modal test is to first establish a multi-degree-of-freedom dynamic ordinary differential equation, obtain the transfer function through its pull-type variation, and determine whether the transfer function contains the information of the modal parameters.  
The differential equation of the system with N degrees of freedom after the structure has been discretized is:  
\[[M]\ddot{X}+[C]\dot{X}+[K]X=F \tag{11}\]  
where \([M]\) is the mass matrix; \([C]\) is the damping matrix; \([K]\) is the stiffness matrix; \(\ddot{X}\), \(\ddot{X}\), and \(X\) represent the acceleration vector, velocity vector, and displacement vector of each discrete mass point in the system, respectively; and \(F\) represents the excitation-force vector.  
In order to obtain the solution of the differential equation, a pull-type transformation of Equation (11) is performed, which yields:  
\[[M]\Big{[}s^{2}X(s)-sX(0)-\dot{X}(0)\Big{]}+C(sX(s)-X(0))+KX(s)=F(s) \tag{12}\]  
When the initial conditions are all zero, the above equation changes to:  
\[\Big{(}s^{2}[M]+s[C]+[K]\Big{)}X(s)=F(s) \tag{13}\]  
According to the definition of a transfer function, the transfer function of the system is:  
\[[H(s)]=\frac{1}{s^{2}[M]+s[C]+[K]} \tag{14}\]  
Using the orthogonality of the matrix, when the modal matrix vibration is \([\varphi]\), the modal mass matrix, modal damping matrix, and modal stiffness matrix are:  
\[[M]_{r}=[\varphi]^{T}[M][\varphi]=diag(m_{1},\cdots,m_{r},\cdots,m_{N}) \tag{15}\]  
\[[C]_{r}=[\varphi]^{T}[C][\varphi]=diag(c_{1},\cdots,c_{r},\cdots,c_{N}) \tag{16}\]  
\[[K]_{r}=[\varphi]^{T}[K][\varphi]=diag(k_{1},\cdots,k_{r},\cdots,k_{N}) \tag{17}\]  
By substituting the above equation into the transfer function, we obtain:  
\[[H(s)]=[\varphi]\Big{[}s^{2}M_{r}+sC_{r}+K_{r}\Big{]}^{-1}[\varphi]^{T} \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Work Conditions** & **Shear Rod** & **Rear-End Bolt** & **Mid-End Bolt** & **Front-End Bolt** \\ \hline Metal pad/dB & 162.29 & 110.63 & 105.58 & 124.60 \\ Rubber pad/dB & 114.32 & 113.56 & 114.50 & 119.32 \\ Improvement value/dB & 47.97 & \(-\)2.93 & \(-\)8.92 & 5.28 \\ \hline \end{tabular}
\end{table}
Table 10: Comparison of the magnitudes of the frequency responses of the TMSHs placed on the metal pad and the rubber pad under vertical excitation.  
Expanding the transfer function matrix reveals that each row or column contains the following basic modal information: all the elements of the modal mass matrix, modal damping matrix, modal stiffness matrix, and all modal vectors.
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 2 Related Work
Differently from D-DSME, the solutions proposed in [13; 23; 24; 25; 26; 27; 28] do not address any sharing mechanism to improve the GTSs use, i.e., the ratio between the number of GTSs actually used and the number of the assigned ones, when the flow period is larger than the multi-superframe one. Moreover, they do not support unconfirmed retransmission of multiple replicas of the same message in order to improve reliability. Recently, the growing interest in DSME has also fostered the development and release of open-source implementations. Among them there are [9; 25; 29].
## 6 Conclusions  
This study addresses the issue of GNSS signal interruptions in urban environments by proposing a CNN-GRU neural network-assisted factor graph integrated navigation method. Through vehicular experiments conducted in typical urban settings in Beijing, we have validated the effectiveness and superiority of this method in navigating complex road environments. The experimental design covered various typical urban driving scenarios, including areas with high-rise buildings, overpasses, tunnels, and other dense constructions that cause GNSS signal blockages. By comparing this method with traditional factor graph optimization methods and LSTM-assisted factor graph methods, we found that the CNN-GRU-FGO algorithm demonstrated significant performance improvements in all aspects. The application of the CNN-GRU neural network-assisted factor graph integrated navigation method in urban environments showcases its technical innovation and operational efficiency. This method not only provides a new direction for future smart transportation technologies but also offers robust technical support and a theoretical foundation for solving similar navigation and positioning challenges.  
[PERSON] wrote the paper, and conducted the final editing; [PERSON] and [PERSON] developed the methodology, analyzed the data, and structured the discussion; [PERSON] reviewed the results and supervised the conclusions. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Section 2** & **Algorithm** & **Orientation** & **RMSE (m)** & **Maximum Error (m)** \\ \hline \multirow{4}{*}{winding road} & \multirow{2}{*}{FGO} & east & 5.73 & 25.42 \\  & & north & 41.48 & 141.69 \\ \cline{2-5}  & & east & 0.48 & 1.66 \\  & & north & 1.08 & 3.62 \\ \cline{2-5}  & \multirow{2}{*}{CNN-GRU-FGO} & east & 0.19 & 1.49 \\  & & north & 0.28 & 2.18 \\ \hline \multirow{4}{*}{long straight road} & \multirow{2}{*}{FGO} & east & 1.33 & 3.81 \\  & & north & 30.79 & 98.44 \\ \cline{2-5}  & & east & 0.41 & 2.58 \\ \cline{2-5}  & \multirow{2}{*}{CNN-GRU-FGO} & east & 0.13 & 1.08 \\  & & north & 0.24 & 1.60 \\ \hline \multirow{4}{*}{all road} & \multirow{2}{*}{FGO} & east & 4.16 & 25.42 \\  & & north & 36.55 & 141.69 \\ \cline{1-1} \cline{2-5}  & & east & 0.45 & 2.58 \\ \cline{1-1}  & & north & 0.88 & 3.62 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{CNN-GRU-FGO} & east & 0.16 & 1.49 \\ \cline{1-1}  & & north & 0.26 & 2.18 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Position error statistics for Section 2.  
Beijing Municipal Education Commission Research Project Funding (No.: KM202311232015; No.: KM202311232016). Beijing Natural Science Foundation Project (No. 4244091, No. 4242036). Supported by Open Project of Beijing Key Laboratory of High Dynamic Navigation Technology.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Automated Destination Renewal Process for Location-Based Robot Erands
## 3 Methods
### Robot Positioning Based on Objects
#### 3.2.2 Object Detection  
One of the essential technological components in location-based errand services is to establish a process that recognizes the location of target objects on a grid map and to integrate this information with a topological map to facilitate grabbing.  
To achieve this, during the initial exploration of the space, the robot utilizes the yolo module to recognize target objects from sensor data extracted using an RGB-D camera. This enables the automatic estimation of the movable destination for manipulation. Specifically, the bounding box of objects recognized by yolo is mapped to depth image. The 3D information in the robot's reference frame is then projected to estimate the distance. The robot's movable optimal position is estimated by intersecting with information such as robot size, workspace of the manipulator, and obstacle data. This estimated optimal position is converted to grid map coordinates and registered in the topological map. For further details, refer to our previous research [35].