esa-sceva/satcom-chunk-collection huggingface dataset:
# GIS Approach for Expressing Structural Landforms: Forms, Elements, and Relationships
## 2 GIS Approach for Expressing Structural Landforms
### SLML Method for Expressing \"Forms, Elements, and Relationships\"
#### 2.3.3 SLML Relationship Construction  
In SLML, the relationships between spatial objects are constructed according to the topological rules defined in Section 2.2. In this study, topological relationships among spatial objects in different structural landforms are constructed based on the Esri geographic data framework. Figure 4 shows a class diagram of the spatial objects of structural landforms and their topological relationships. For example, Figure 4b shows the elements and their relationships that constitute the structural geomorphology of the \"anticlinal mountain\". The anticlinal mountain is composed of four elements, i.e., \"fold core\", \"fold axis\", \"stratum\", and \"ridge line\", as well as the relationship between the two elements, where the \"fold core stratum is older\" and the \"fold axis overlaps with ridge line\", to express the structural geomorphology of the \"anticlinal mountain\" in GIS.  
Figure 3: Method of SLML graphical expression.  
Figure 4: _Cont._  
The relationships between structural landform elements can be described by topological rules, and the form of the structural landform is described by a variety of spatial objects and their topological constraints. For example, anticlinal mountains can be described by four spatial objects, namely, \"fold core\", \"fold axis\", \"stratum\", and \"ridgeline\", and the topological constraints between them. Anticlinal mountains are described by the relative stratigraphic age of the \"fold core\" and the topological rules of the \"fold axis\" and \"ridge-line\". The fold core (polygon) spatial object coincides with the formation (polygon) spatial object, where the subtype of the formation is the relative age of the older strata. The axis of the fold overlaps with the ridgeline and two spatial objects (line types).
## 5 Challenges and Future Research Directions
### THz Deployment  
Although THz communication systems are still in their early stages of development, some standardization and regulating processes have already begun. IEEE 802.15.3d-2017, for example, is the first standard for THz, aiming an alternate PHY layer at frequencies between 252 and 321 GHz as an addition to 802.15.3-2016. Furthermore, the WRC 2019 conclusions offer a regulatory framework for the operation of fixed service (FS) and land mobile service (LMS) applications in frequency bands between 275 GHz and 450 GHz. On the other hand, the growing interest in THz communications naturally raises concerns about the health risks connected with high-frequency radiation, particularly as some non-scientific people still have a bad image of 5G technologies, particularly mm wave and beam forming [2]. In fact, radio frequency electromagnetic fields (EMFs) below \(3\times 1016\) GHz are non-ionizing in nature and hence do not have enough energy to ionize cells, avoiding cancer and death hazards [137]. However, they have enough energy to propel electrons and ions into higher energy levels. Because the ensuing thermal effects may cause health problems, regulatory authorities (ICNIRP, FCC, EC, IEEE and others) set maximum RF exposure limits to prevent dangerous heating effects. However, in THz, it is more critical to enforce RF exposure rules for large antenna arrays since beam forming might result in higher power density levels than omnidirectional transmissions. Nevertheless, UAV-assisted communications can increase the distance between THz radiation and human bodies, reducing RF exposure to a degree. There is no evidence of any serious health problems that has been supported by the scientific community to date [138]. However, this aspect requires significant research and experimental studies across various domains to analyze and discover any health-related issues which may arise due to THz applications.
## 4 Problem Definition and Research Methodology  
The problem of a spectral efficiency prediction is, therefore, the problem of the good estimation of the CQI and Rank Indicator. In previous work [11], authors examined the correlation between CQI and MIMO utilization, while the focus of this paper is CQI modeling. Most of the research in this field is focused on network simulators and performance models that are generic, with some assumptions in the model that can limit the model's accuracy. Furthermore, they are typically focused on system and algorithm design, and rarely deals with network planning problems [12; 13; 14; 15]. The reason for this is academics and researchers are rarely involved in industry closely enough to have access to data from real networks. Very few papers and research studies addressing CQI, that combine theoretical and empirical approaches are available. At the same time, in the network evolution process from Global System for Mobile communications (GSM) and 3G towards LTE and, lately, 5G-NR systems, network grid locations used for base stations, as well as some main characteristics of network design, such as antenna height and azimuth, do not change much. For this reason, a lot of network performance modeling can be done based on historical data and recognition of traffic patterns, and network signatures. The authors' approach to the problem of network performance prediction is based on collecting data from commercial mobile networks, operating with huge data sets, and building algorithms and models that can be applied to any network, while capturing its own peculiarities and signatures.  
Data collection in a mobile network is done though Performance Management--PM systems. The standard of 3 GPP defines how counters are collected. While each Radio Access Network (RAN) vendor has some level of freedom to build a data collection process around different events in the network, differences between RAN solutions in design of PM systems are not significant, and the main indicators are similar. The main indicator used in this study is naturally CQI reporting, analyzed as Cumulative Distribution Function (CDF) during different periods--parts of the day/week/month, cell by cell. The evolution of the CQI reporting process with changes in network load, measured as RB utilization in serving and surrounding cells, are analyzed.
## 5 Conclusions  
In this work, a dynamic forecasting model based on variational mode decomposition (VMD) and stack long short-term memory network (SLSTM) is proposed. The proposed model is successfully applied to the Dashuitian landslide in China. Compared with the two advanced forecasting models--the LSTM network and the EMD-LSTM network--the proposed model has higher forecast accuracy.  
The use of the VMD method, which is backed by mathematical theory, is proposed to solve the problem of the incomplete or excessive decomposition problems caused by traditional methods such as EMD and EEMD. The landslide displacement is decomposed into stable and fixed components by the VMD method, which lays the foundation for each component forecasting task.  
The use of the SLSTM network, which is a novel predictor that can remember historical information, is proposed to conduct each component forecasting task. As a deep learning method, the SLSTM network improves the network forecasting performance through stacking LSTM layers. Finally, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all components.  
Considering that the proposed forecasting model only achieves slightly better performance than the EMD-LSTM network when the dataset is small, the VMD-SLSTM network is promising if the dataset is expanded in future work. In addition, the complexity of the SLSTM network makes parameter  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAE/mm** & **RMSE/mm** \\ \hline LSTM network & 3.99 & 4.15 \\ EMD-LSTM network & 2.25 & 2.76 \\ VMD-SLSTM network & 1.99 & 2.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Forecast errors of landslide displacement using three forecasting models.  
Figure 10: Forecast results of landslide displacement using three forecasting models: VMD-SLSTM, empirical mode decomposition (EMD)-SLSTM, and LSTM.  
training more difficult. If better network parameters are trained, the prediction accuracy can be further improved. The residual compensation for the forecasting model should also be paid more attention.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] carried out the data acquisition and manuscript editing; and [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
This research was funded by National Key R&D Program of China, grant number 2018 YFC1508603.  
The authors declare no conflict of interest.
# Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate
## 3 Adaptive Fuzzy Control Algorithm and Analysis of Power Consumption  
### Power Consumption in Different Data Rate and Transmission Power  
The transmission power range is 0 to 14 dBm in CC1310, and its modulation is 2-FSK (Frequency Shift Keying). The configurations of eight data rates used here are introduced in Table 1. It indicates the frequency deviation and RX BW (Received Bandwidth) of eight different data rates. These parameters of each data rate are configured according to [30] provided by Texas Instruments. Table 2 presents the actual current corresponding to each transmission power, and the current in this table is the average value. The values of average current corresponding to each transmission power were measured by ourselves, using a high precision current meter. To effectively evaluate and analyze the relationship between data rate and power consumption, the required time for transmission is also an important parameter for power consumption analysis. The transmission time can be simply calculated by  
\[\textit{Transmission time}=\frac{\textit{Data length}\ (\textit{bits})}{\textit{Data rate}\ (\textit{bps})} \tag{1}\]  
where the data length is 16 bytes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Rate** & **Deviation** & **RX BW** & **Data Rate** & **Deviation** & **RX BW** \\ \hline
12.5 kbps & 5 kHz & 49 kHz & 300 kbps & 105 kHz & 622 kHz \\
50 kbps & 25 kHz & 98 kHz & 400 kbps & 140 kHz & 622 kHz \\
100 kbps & 45 kHz & 196 kHz & 450 kbps & 155 kHz & 784 kHz \\
200 kbps & 70 kHz & 311 kHz & 500 kbps & 175 kHz & 1243 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of each data rate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Transmission** & **Average** & **Transmission** & **Average** & **Transmission** & **Average** \\
**Power** & **Current** & **Power** & **Current** & **Power** & **Current** \\ \hline
14 dBm & 24.138 mA & 9 dBm & 18.181 mA & 4 dBm & 13.992 mA \\
13 dBm & 23.894 mA & 8 dBm & 17.460 mA & 3 dBm & 13.009 mA \\
12 dBm & 21.575 mA & 7 dBm & 16.569 mA & 2 dBm & 12.447 mA \\
11 dBm & 20.749 mA & 6 dBm & 15.496 mA & 1 dBm & 12.064 mA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average current.  
Figure 2: Schematic diagram of TDMA.  
The corresponding power consumption is calculated by  
\[Power\ consumption=Current\times Transmission\ time \tag{2}\]  
The relation of power consumption, data rate, and transmission power is illustrated in Figure 3. Obviously, using larger transmission power to communicate will consume more power. Furthermore, increasing the data rate, which means lowering the transmission time of fixed-length packet, will reduce opening time for radio frequency (RF) and help the node to enter the sleep mode sooner, which in turn decreases the power consumption.
## 2. Stray Current Corrosion Mechanism and Interference Characteristics
### Corrosion Mechanism and Interference Characteristics of AC Stray Current
#### 2.2.3 AC Depolarization Theory  
The theory of AC depolarization proposes that AC current changes the electrochemical kinetics of corrosion through potential fluctuations. The polarization of the cathode is faster than the polarization of the anode, triggering a depolarization effect and causing a negative shift in potential. The corrosion current density becomes larger. When the current frequency is higher, and the intensity is greater, the depolarization effect is more significant, and the corrosion rate is higher [28; 29].  
The above studies show that potential fluctuations are going to accelerate the corrosion of pipelines, and when the current intensity increases and the current frequency increases, the depolarization effect will be more obvious, and the corrosion process will be further accelerated.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## Appendix C F1 Score  
The F1 score is a standard measure of performance for classification model. In multi-class or imbalanced classification settings, different strategies exist for computing the overall F1 score: micro, macro, and weighted F1 scores.  
Micro F1 aggregates the contributions of all classes by computing the global sums of True Positives (\(TP\)), False Positives (\(FP\)), and False Negatives (\(FN\)) then applying the computation:  
\[\text{Precision}=\frac{\sum TP}{\sum TP+\sum FP} \tag{1}\]  
\[\text{Recall}=\frac{\sum TP}{\sum TP+\sum FN} \tag{2}\]  
\[F1_{micro}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{3}\]  
Macro F1, on the the other hand, computes the F1 score independently for each class:  
\[F1_{macro}=\frac{1}{N}\sum_{i=1}^{N}F1_{i} \tag{4}\]  
where \(N\) is the number of classes, and \(F1_{i}\) is the F1 score for class \(i\). Macro F1 treats all classes equally, making it sensitive to class imbalance since it does not consider class frequency.  
In this research, we used Weighted F1, which accounts for class imbalance by weighting each class's F1 score by the number of true instances in that class:  
\[F1_{weighted}=\sum_{i=1}^{N}w_{i}\cdot F1_{i} \tag{5}\]  
where \(w_{i}\) is the proportion of true instances of class \(i\) in the dataset:\[w_{i}=\frac{|C_{i}|}{\sum_{j=1}^{N}|C_{j}|} \tag{10}\]  
This approach ensures that the contribution of each class is proportional to its prevalence, making it more suitable when dealing with imbalanced datasets.
# Pruning Optimization over Threshold-Based Historical Continuous Query
## 4 Pruning Optimization
### E3 DR-Tree  
Compared with the pruning method with taking the whole query trajectory as the pruning reference object, the pruning method with taking each trajectory segment of it as the reference one can obtain more accurate pruning results. This is because the invalid space of the MBRs which enclose the trajectory segments is less than that of the MBR which encloses the whole trajectory. For example, in Figure 5, the Mindist between the MBR of _drtree_ node \(N_{1}\) and \(M(T_{q})\) is smaller than \(d\), but the Mindists between the MBR of the \(N_{1}\) and the MBRs of the trajectory segment \(l_{1}\) is greater than \(d\). Therefore, \(N_{1}\) does not satisfy the candidate requirement of \(l_{1}\), and it is not necessary to calculate the distance between any trajectory segments contained in \(N_{1}\) and \(l_{1}\) in the refinement step.  
Although the trajectory segment-based method can improve the pruning precision, in the pruning process, it is necessary to traverse the _drtree_ once for each trajectory segment of \(T_{q}\), resulting in a large increase in node traversal overhead. In order to reduce the node traversal overhead, we also organized all the trajectory segments contained in \(T_{q}\) by a spatiotemporal index called _qrtree_. In the pruning step, _drtree_ and _qrtree_ are traversed at the same time, this makes it possible to generate a precise pruning result without having to traverse the _drtree_ multiple times, thus effectively reducing the overhead of traversing the nodes. The traversal algorithm will be discussed separately in Section 4.3, this section first describes the index structure used by _qrtree_.  
_Qrtree_ uses an index structure called the Extended 3 DR-tree (E3 DR-tree). An example of the structure of E3 DR-tree is shown in Figure 6. The difference between E3 DR-tree and the traditional 3 DR-tree is shown in three aspects:  
1. Node selection condition. Whenever a trajectory segment is inserted, the node with the smallest change in the time interval is selected as the inserted node.
2. Node split processing. When a node needs to be split, while balancing the number of child nodes included in the new nodes, it is also necessary to ensure that the time intervals of new nodes have no overlap.
3. The child nodes (or trajectory segments) of a node are sorted in the ascending order of the start timestamp of the corresponding time interval.  
Figure 5: Example of Mindists of different data granularities.  
Figure 6: Example of E3 DR-tree.  
Although the construction of E3 DR-tree does not consider the spatial factor directly, for the trajectory data of the same moving object, the trajectory segments that are close in time are usually close in space, so E3 DR-tree still has a strong spatial distinguishing ability. Moreover, E3 DR-tree ensures that the trajectory segments can be processed in chronological order during traversal, which is consistent with their order in the trajectory so that the overhead of the subsequent sorting processing can be effectively reduced.
## 3 System Architecture
### Strategy of Each Player
#### 3.3.3 Vendor Supplier  
The vendor suppliers mainly provide hardware such as RU and COTS servers, which are general purpose servers. In addition, it is possible to provide a their virtualization platform software and network functions (RAN/Core software, MEC platform) based on standardization (e.g., ITU-R/3 GPP/ETSI/O-RAN). As standardization organizations play a central role in implementing multi-vendor support to avoid market monopoly by one vendor's specifications, each software must be in line with the standardization specifications, and the Private/Local Telecom Operator must be multi-vendor. On the other hand, in order for the Private/Local Telecom Operator to develop the above products, it is necessary to acquire human technical resources, as many specific skill-holders are required. Although it is difficult to describe the above option, there is a model, provided by the Legacy Telecom Operator, for all resources, making it possible to obtain the needed skill sets for both equipment provision and operation.
## 2 Materials and Methods
### Terrain Classification Methodology
#### 2.2.1 Obstacle Detection Module  
Detecting and localizing obstacles are important to realize autonomous motion and path planning. The sensors used for traditional obstacle detection mainly include laser radar sensors, ultrasonic sensors, infrared sensors, visual equipment, etc. [24]. In this paper, a fast and accurate detection method based on depth and infrared information is used [25]. The image is segmented by the mean-shift algorithm and the pixel gradient of the foreground is calculated. After pretreatment of edge detection  
Figure 1: Architecture of robot: (**a**) hardware structure; (**b**) distribution of sensors. BLi, HLi, and KLi (BRi, HRi, and KRI), respectively, indicate base joint, hip joint, and knee joint of left (right) leg. LF, LM, and LH (RF, RM, and RH), respectively, indicate front leg, middle leg, and hind leg of left (right) side.  
Figure 2: Terrain classification system.  
and morphological operation, the depth and infrared information are fused. The characteristics of depth and infrared images are used for edge detection. Thus, the false rate of detection is reduced and detection precision is improved. Since depth images cannot be affected by natural sunlight, the influence of light intensity and shadow on obstacle recognition is effectively eliminated and the robustness of the algorithm is improved. This method can accurately identify the position and size of obstacles. In this paper, the results obtained by obstacle detection with this method are used as the input of the terrain image infilling method.
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 4 Methodology
### Optimization  
In this model, we present the loss function of the model, since we want our model to be general, we use both self-supervised and supervised ways and design a loss to combine both ways. For the supervised part, we use MSELoss as the loss function, for the unsupervised part, the loss function is as follows. Suppose there are M test samples, and the labels of samples are \(Y_{real}=((y_{1,lat},y_{1,long}),(y_{2,lat},y_{2,long}),\dots,(y_{M,lat},y_{M, long}))\), which are the latitude and longitude of the precise locations. The precise locations are the real locations of the users; in the test samples, the predicted locations are \(Y_{pred}=((\hat{y}_{1,lat},\hat{y}_{1,long}),(\hat{y}_{2,lat},\hat{y}_{2,long} ),\dots,(\hat{y}_{M,lat},\hat{y}_{M,long}))\), and the formula in this part is as follows:  
\[L=\beta*L_{rnse}+L_{pos}+L_{neg}, \tag{22}\]  
\[L_{rnse}=\frac{1}{M}\cdot\sum_{i=1}^{M}\sqrt{(y_{i,lat}-\hat{y}_{i,lat})^{2}+(y _{i,long}-\hat{y}_{i,long})^{2}}, \tag{23}\]  
\[L_{pos}=log(\sigma(x_{pos})), \tag{24}\]  
\[L_{neg}=log(1-\sigma(x_{neg})). \tag{25}\]  
In this model, \(\sigma\) is the sigmoid function; using this way, the loss contains supervised learning as well as unsupervised learning. \(\beta\) is used to control the weight of the part of the metric for the \(L_{rnse}\) part.
## 3 Water-Jet Propulsion Dynamics Modeling  
### Geometric Models and Meshing  
#### 3.1.1 Submersible Geometry Model  
The geometric models of pump-jet propulsion and the submersible were built together, as shown in Figure 2. The geometric parameters of the model--the length of the pump-jet submersible (L\({}_{\rm s}\)), maximum circumferential radius (R\({}_{\rm max}\)), length of the command post from the head (L\({}_{\rm c}\)), length of the parallel middle body of the wing (L\({}_{\rm w}\)), and height of the highest point of the cross tail from the center line (L\({}_{\rm h}\))--were given in Table 1.  
Figure 1: Accelerated motion research flow chart.  
Figure 2: Two-dimensional calculation model of the submersible.  
The designed speed of the propulsion was 2.2 m/s. A screw mixed-flow pump was used as the power device of the submersible. The water-jet propulsion pump comprises an impeller, a space guide vane, and a spout. The design parameters included the head (H\({}_{0}\)), flow rate (Q\({}_{0}\)), rotation speed (n\({}_{0}\)), efficiency (\(\eta_{0}\)), and specific speed (n\({}_{\text{s}}\)); these were given in Table 2.  
The specific speed is a comprehensive characteristic number which includes the flow rate, head, and rotation speed derived from the similarity law. It is the basis for calculating the pump's structural parameters. Similar pumps are determined by Equation (1).  
\[n_{\text{s}}=\frac{3.65n_{0}\sqrt{Q_{0}}}{H_{0}{}^{4/3}}, \tag{1}\]
# Air Conditioning Load Forecasting and Optimal Operation of Water Systems
## 2 Methods
### Load-Forecasting Model
#### 2.2.4 Model Evaluation Index  
In order to avoid the overfitting phenomenon of the model, the test set data are used to test the prediction performance and generalization performance of the model. In this paper, root mean square error (RMSE), goodness of fit R\({}^{2}\), and calculation time T are used as the evaluation indexes of the prediction performance of the model. In addition, the generalization performance of the model can be evaluated by comparing the RMSE and R\({}^{2}\) of the prediction results of the training set and the test set. The calculation formulas of root mean square error and goodness of fit are shown in Equations (4) and (5).  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{5}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{ i}-\overline{y}_{i})^{2}} \tag{6}\]  
where \(n\) is the number of sample data, \(y_{i}\) is the actual value of the \(i\)th sample, \(\hat{y}_{i}\) is the predicted value of the \(i\)th sample, and \(\overline{y}_{i}\) is the mean value of the sample.