esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation Scenario
### Evaluation Metrics
#### 5.3.3 Network Profitability  
Finally, we consider a metric, called network profitability, to express the monetary gains achieved by the operator. Intuitively, the network profitability is defined as the difference between monetary energy savings and monetary reparation costs that arise from lifetime variations. The total network profitability \(R_{total}\) is expressed as:  
\[R_{total}=\left(E_{total}^{SBN}-E_{total}^{EMA}\right)\cdot r_{Wh}-|T|\cdot \Delta t\cdot r_{MTTR}\cdot r_{CM}\left(\sum_{i\in V}\sum_{k=1}^{X_{i}^{LC}}AF _{i}^{k}(|T|\cdot\Delta t)\cdot r_{FR}\right)\quad\text{(USD)}. \tag{15}\]  
where: \(r_{Wh}\) is the energy cost, equal to \(1.6\cdot 10^{-4}\) [USD/Wh] [3; 42; 43], \(r_{MTTR}\) is the Mean Time To Repair (MTTR), equal to 2 h [3; 42], \(r_{FR}\) is the Failure Rate (FR), equal to \(2.9\cdot 10^{-6}\) failure/h [3; 44], \(r_{CM}\) is the hourly rate of a reparation crew member, equal to 190 [USD/h/failure] [3; 45] (we assume that one crew member is required for repairing the LC), and \(|T|\) denotes cardinality of the set \(T\). Similarly to the AF, we compute \(R_{total}\) after 14 days of network operation, (i.e., \(T\) spans over 14 consecutive days with the same daily traffic profile in each day).
# A UAV Coverage Path Planning Method Based on a Diameter-Height Model for Mountainous Terrain
## 2 Problem Statement  
### The Definition of the DH Model  
In order to provide better coverage effects for mountain terrain, a simplified mountain model has been proposed, taking into account the mountain geometrics. Intuitively, according to the morphological characteristic of the mountain, the simplified mountain model consists of a small cone at the top and a large frustum at the bottom, as shown in Figure 1. Thus, the simplified mountain model can be represented by diameters and heights. For the sake of simplicity, this simplified mountain model is termed as the DH model, where \(D\) stands for diameter and \(H\) denotes height. In the DH model, the heights of the frustum and the cone are indicated as \(h_{1}\) and \(h_{2}\), respectively. The bottom diameter and top diameter of the frustum are denoted as \(d_{1}\) and \(d_{2}\), respectively.  
Similarly, for the sake of simplicity, the 3D map of terrains with sparsely distributed mountains can be constructed based on the proposed DH model. The boundary contour of the mountainous terrain is regarded as a polygon. All the UAV coverage path planning problems of mountainous terrain will be described from the perspective of computer graphics, and the simplified 3D map for mountainous terrain can be illustrated in Figure 2. The boundary of mountainous terrain is indicated by the red dashed line, and three mountains located in the mountainous terrain are denoted by \(DH_{1}\), \(DH_{2}\), and \(DH_{3}\), respectively.  
Figure 1: Schematic diagram of the DH model for simplified modeling of mountains.
# Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning
## 3 Controller Design
### Reinforcement Learning  
Reinforcement learning is an unsupervised learning process that follows a Markov decision process (MDP). Generally, finite MDPs have four parts, \((S,A,p,R)\). \(S\) represents the state space of the environment, \(A\) denotes the action space, \(p\) represents the unknown probability of state transitions, and \(R\) is the reward function. The specific process of reinforcement learning can be seen in Figure 3.  
The agent interacts with the environment at each discrete moment. At each step \(t\), the agent receives the environment's state \(s_{t}\) and selects an action \(a_{t}\) according to the current policy \(\pi\) and then receives a reward value \(r_{t}\) from the environment to evaluate the quality of \(a_{t}\), where \(s_{t}\in S\), \(a_{t}\in A\), \(r_{t}\in R\). The decision-making process can be expressed as follows:  
\[a_{t}=\pi(s_{t}) \tag{3}\]  
Figure 3:  
The environment's state will change to \(s_{t+1}\) after the agent performs the action \(a_{t}\), with \(p(s_{t+1}|s_{t},a_{t})\) as the transition probability. Reinforcement learning aims to maximize the agent's reward value to find the optimal strategy \(\pi^{\bullet}\).
## 4 Application of UWSN
### Monitoring
#### 4.1.5 Sports  
UWSN in sports plays a vital role in determining the fairness of the game. In sports such as swimming, UWSN is used to determine who crossed first in close-call scenarios. In [88], the authors use UWSN sensors to determine the performance of the swimmer in real time.
# A New Method to Determine the Steel Fibre Content of Existing Structures--Evaluation and Validation
## 2 Materials and Methods
### Evaluation of the Results  
The fibre content of the specimens, as already presented in [2], was calculated from the electrical resistivity of the SFRC specimens in relation to one of the PC specimens by the equation of [11] (see Equation (2)).  
\[\frac{\sigma}{\sigma_{m}}=1+\left[\sigma\right]_{\Delta}*\Phi \tag{2}\]  
with: \(\sigma\): electrical conductivity of the SFRC in \(\Omega\)m  
\(\sigma_{m}\): electrical conductivity of the PC in \(\Omega\)m  
\(\Phi\): fibre volume fraction  
\([\sigma]\): intrinsic conductivity (Equation (3))  
\(\Delta\): ratio of conductivity of fibres and PC  
The intrinsic conductivity in Equation (2) was calculated in dependence of the aspect ratio of the fibre by an empiric equation in accordance with [11]:  
\[\left[\sigma\right]_{\infty}=\frac{1}{3}\left(\frac{2*(AR)^{2}}{3*ln[4(AR)]-7 }+4\right) \tag{3}\]  
with: \(AR\): aspect ratio of the fibres.  
The aspect ratio of the fibres in this study was 60, and thus the intrinsic conductivity of 255.5 resulted. With this factor, the fibre volume fraction of each concrete specimen could be calculated by Equation (2). As several directions of the specimens were analysed, a global conductivity value had to be determined for each kind of specimen. For the cubic specimens, it was calculated as a mean value of the three pairs of parallel surfaces, while for cylindrical specimens, several configurations have been checked to find an optimum according to the real behaviour of the material.  
The use of Equation (2) in different directions, such as only one pair of parallel surfaces of the cubic specimens, in relation to two orthogonal directions, enables the calculation of orientation factors for the single directions. It is summarized that the three orientation factors of one concrete specimen give 100%; therefore, each of the factors is between 0 and 100%.  
Figure 4: Resulting isosurfaces, calculated by Comsol with different simulated electrical conductivity/resistivity of the electrodes. (**a**) electrical conductivity of electrodes: 1.4 \(\times\) 10\({}^{6}\) S/m; (**b**) electrical resistivity of electrodes: 100 \(\Omega\)m.
## 4 Performance Evaluation
### Simulation Setup  
The simulation parameters are set as shown in Table 1, generally consistent with those in [13]. For example, the number of tiles in the vertical and horizontal directions in a projected video plane is set based on [22,23], and the data size per tile per frame is set according to [24]. Interested readers can refer to [13] for more details. The test video is a 20-s video titled \"BlueWorld\" from YouTube. The main difference is that, in the multiuser scenario as studied in this paper, when applying equal-rate transmission resource allocation,the transmission rate \(R_{2,\mathbf{x}}(t)\) from the MEC server-connected BS to each user is no longer set according to the uniform distribution given in [13], but is instead evaluated using the Shannon formula, which is based on the following: the BS's transmission power and bandwidth resources, the distance from the user to the BS, the path loss exponent, small-scale fading generated by a Rayleigh distribution with unit mean, and the noise power. After the wireless resources are allocated using equal-rate transmission, the transmission rate is calculated. According to the max-flow min-cut theorem, the transmission rate \(R_{3,\mathbf{x}}(t)\) from the cloud server to the user is the minimum value between the transmission rate from the cloud server to the MEC server and the transmission rate from the MEC server to the user. Under the premise of fair sharing of the wired and wireless networks' resources among multiple users, we set \(R_{3,\mathbf{x}}(t)\) based on the following formula:  
\[R_{3,\mathbf{x}}(t)=\min\left\{\frac{R_{1}(t)}{\sum_{i=1}^{l}x_{i}},R_{2, \mathbf{x}}(t)\right\} \tag{17}\]  
Two performance metrics, delay violation probability and user access probability, are applied for evaluation. The first metric, defined in Section 3.1.2, is used to measure the QoS for admitted 360\({}^{\circ}\) VR video users. The second metric reflects the resource utilization efficiency of the proposed multiuser access control algorithm, defined as the ratio of the number of admitted 360\({}^{\circ}\) VR video users to the total number of requesting users. To ensure statistical significance of the simulation results, for each group of requesting users, 100 different user location combinations are tested, and 1000 video plays are conducted for each user location. The simulations are implemented through a custom-developed platform built in MATLAB R2023b.
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
## 5 Model Verification and Analysis Results  
### Setting of the Simulation Conditions  
The conditions before and after the supply of the ozone-treated base flow within Gulpocheon were compared through annual simulations, by building a hydraulic and water quality simulation model. Quantitative analysis was conducted for the water quality improvement on supplying ozone-treated base flow. Table 2 shows the overview of the hydraulic and water quality experiments.  
The minimum calculation time step of the model for unsteady analysis was set to 0.01 s to minimize the effect of model errors and divergence. The horizontal eddy viscosity set up with Smagorinsky formulation with 0.28 value. The manning coefficient representing the roughness of the river bed was 1/32, and it is a condition with stones and weeds in general.
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
## 3 ERP Estimation Based on the Single LEO Satellite
### Impact of Using Ambiguity-Fixed Orbit on the ERP Estimation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Orbit Type**} & \multicolumn{2}{c}{**E (mm)**} & \multicolumn{2}{c}{**N (mm)**} & \multicolumn{2}{c}{**U (mm)**} \\ \cline{3-8}  & & **Median** & **IQR** & **Median** & **IQR** & **Median** & **IQR** \\ \hline All & Ambiguity-float & \(1.4\) & \(20.2\) & \(0.0\) & \(23.9\) & \(1.6\) & \(16.9\) \\ \cline{2-8} stations & Ambiguity-fixed & \(-0.2\) & \(16.6\) & \(1.0\) & \(20.5\) & \(2.6\) & \(14.7\) \\ \hline Core & Ambiguity-float & \(1.2\) & \(17.7\) & \(-3.2\) & \(23.0\) & \(-0.6\) & \(12.5\) \\ \cline{2-8} stations & Ambiguity-fixed & \(-0.1\) & \(14.9\) & \(-0.6\) & \(19.6\) & \(-0.5\) & \(10.8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Coordinate differences of SLR stations w.r.t. SLRF2014 for the GRACE-D solutions based on the ambiguity-float orbit and ambiguity-fixed orbit. Figure 2: Time series of pole coordinates and the length-of-day differences with respect to finals2000A.data series for the GRACE-D solutions based on the ambiguity-float orbit and ambiguity-fixed orbit. Values in the top right corner indicate the mean and RMS of differences for these two schemes.
# Efficient MIMO Signal Predistortion for Secrecy-Enhancing
## 5 Conclusions and Discussion  
This paper proposed efficient PLS schemes for STBC and SM-based MIMO systems. Compared to the conventional AN-based PLS scheme, the proposed schemes neither require additional power nor have a PAPR problem, because they distort the phase of the complex signals. The performance simulation results showed that, in addition to high security protection, the proposed scheme for the STBC-based MIMO system achieves BER performance approximating those of the conventional schemes with full rate or full orthogonality. The proposed scheme for the SM-based MIMO system also showed almost no information leakage to the illegal eavesdropper.  
The proposed scheme requires accurate CSI at both the transmitter and the receiver, which may not be the common assumption for the STBC-based MIMO systems. The future work should focus on other alternative methods which do not require CSI at the transmitter, by considering STBC as a transmit diversity technique. In addition, we investigated the effect of CSI estimation error by using a commonly used model, but the estimation error accumulation effect may impact further on the performance of the proposed SM-based MIMO scheme. Hence, future work should focus on detailed investigation on the effect of imperfect channel information, leading to more efficient PLS schemes.  
Conceptualization, H.L. and S.C.; methodology, H.L. and S.C.; validation, H.L. and S.K.; formal analysis, H.L.; investigation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, S.K.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1A2C1003121).  
The authors declare no conflict of interest.
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 3 Results  
### Data Transmission  
#### 3.1.1 Generating Initial Received Signals with Different SNRs  
In ultrasonic communication systems, the characteristics of the channel are highly sensitive to many physical conditions such as channel length, the coaxial alignment offset of the transmitting and receiving transducers, and environmental temperature. Any variation in channel conditions may lead to fluctuations in energy dissipation, acoustic echo, and noise intrusion within the channel. These variations and fluctuations generally have a considerable impact on signal attenuation, delay, and waveform distortion. SNR is one of the most crucial metrics for assessing signal quality. In order to evaluate the effectiveness of the LMS adaptive filter and DPRNN models for echo cancellation, we artificially made the received signals \(S_{R}(t)\) with different SNRs. Here, SNR was defined as the ratio of the power of the reference waveform to the power of the estimated waveform as follows:  
\[SNR(x,\hat{x})=10 log_{10}\frac{\left\|\hat{x}\right\|^{2}}{\left\|\hat{x}-x \right\|^{2}} \tag{14}\]  
where \(x\) and \(\hat{x}\) represented the estimated waveform and reference waveform, respectively.  
Two methods were adopted to generate the above-mentioned signals at a 2 Mbps transmission rate. Method 1 was to change the channel length by using aluminum plates  
Figure 8: Actual constructive of the signal receiving side.  
Figure 9: Photo of actual ultrasonic through-metal communication system.  
with thicknesses of 30 mm, 40 mm, 50 mm, and 60 mm, respectively. Method 2 was to adjust the alignment offset distance of the transmitting and receiving transducers along their axis while keeping the 50 mm long aluminum channel for data transmission. For both transducers with the same diameter of 16 mm, offset distances were set as 0 mm, 6 mm, 12 mm, and 18 mm, respectively. Table 1 shows the different SNRs of the signals collected at the AD conversion circuit of the signal receiving side under various channel conditions, implying that SNR is highly affected by channel conditions.
## 2 Methodology  
### Research Framework  
The proposed framework aims to monitor the compliance of concrete pouring height regulations by analyzing object relationships in real construction environments. As illustrated in Figure 1, the framework consists of five main modules: data collection, data labelling, Region of Interest (ROI) definition based on boom detection, relationship-based weight analysis, and compliance verification.  
Figure 1: Research framework.  
A diverse set of concrete pouring images is collected through web crawling. From the initial dataset, images that clearly capture the pouring scene are selected for analysis. These selected images are annotated to define the concrete boom and concrete objects, which serve as input for the detection module. The YOLOv11 architecture is used to detect the boom object, and the ROI is defined based on its bounding box. Within the ROI, the relationship between the boom and concrete objects is analyzed based on three factors: distance, attribute, and spatial relationships. These relationships are converted into association values, normalized, and weighted to calculate a final detection score. The pouring height is estimated from the bounding box dimensions of the detected concrete object. The height is then compared against a predefined threshold to determine whether the pouring complies with construction regulations. This framework enables a structured and objective approach to real-time pouring monitoring in construction sites.
## 5 Conclusions  
Based on the road project of National Highway 332 in the Great Khingan Mountains permafrost forest region, a study on the influence of vegetation cover on the settlement behavior of the permafrost subgrade was carried out through field observation, experimental testing, and numerical simulation. The conclusions are as follows:  
1. In the forest region of the Great Khingan Mountains, the underground temperature reaches its lowest value in March and the highest value in September. Vegetation cover mainly influences the seasonal active layer temperature of permafrost and its cooling effect on permafrost in the warm season is more significant compared with the warming effect in the cold season.
2. The volumetric water content of underground rock and soil mass reaches the maximum when the underground depth is 1.5 m. At the same depth, the volumetric water content of the subgrade with vegetation cover is greater than that without vegetation cover in the cold season. The situation is just the opposite in the warm season.
3. The closer to the center of the subgrade, the smaller the magnitude of subgrade freeze-thaw deformation. The damage to the subgrade is mainly reflected in the settlement caused by the thawing of frozen soil. The maximum settlement of the subgrade with and without vegetation cover is 8.3 mm and 9.5 mm at the foot of the subgrade slope, respectively. After construction, the settlement behavior of the permafrost subgrade will undergo a degradation period of 3 years, a restoration period of 2 years, and finally, tend to be stable.  
Conceptualization, G.W.; formal analysis, Z.X. and W.C.; methodology, Z.X.; software, Z.X.; validation, Z.X.; investigation, Z.X. and W.C.; resources, G.W.; data curation, Z.X.; writing--original draft, Z.X. and W.C.; writing--review and editing, Z.X.; visualization, W.C.; supervision, G.W.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the research project of Beijing Municipal Road and Bridge (Group) Co., Ltd. (Key Techniques of Frozen Soil Road Construction in High Latitude and Low Altitude Forest Region, No. 342020003).  
Not applicable.  
Data Availability Statement: All data generated or analyzed during this study are included in this published article.  
We would like to thank all staff members who contributed to this study who are not named here.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Geometric Characteristics
#### 3.3.2 Fourier Descriptor in Normalized Form  
To ensure rotational, translational, and scale-transformation invariance, it is necessary to normalize the descriptor of the target profile curve. This is because the initial point, size, and dimension of the curve have an impact on the size of the Fourier descriptor. Additionally, considering the scale change and rotational motion of the fruit target that needs to be recognized, normalization becomes crucial.  
The process of normalization is carried out using the \(a(1)\)-basis to achieve the normalized Fourier descriptor, as stated in Equation (4).  
\[D(u)=\frac{\left\|a(u)\right\|}{\left\|a(1)\right\|},\ u=0,1,2,\cdots K-1 \tag{4}\]  
Using the cucumber contour image as a reference, we performed scale modification and calculations to extract its first 8 normalized Fourier descriptors. The resulting data records may be found in Table 2.  
Based on the information presented in the table, it is evident that the normalized Fourier descriptor of the picture that has undergone scale transformation has minimal alteration. This indicates that the normalized Fourier descriptor \(D(u)\) preserves its properties of being unaffected by rotation, translation, and scaling.
# Intelligent Agents and Causal Inference: Enhancing Decision-Making through Causal Reasoning
## 2 Materials and Methods
### Implementation of Causal Agent (CBOT)
#### 2.5.2 Conditional Probability Distribution (CPD)  
A conditional probability distribution (CPD) is a table that shows the probabilities of one variable with respect to others [24]. According to [25] these structures allowed us to make queries to the model and reason about counterfactuals using Equation (1).  
\[P(A\mid B)=P(A\cap B)/P(B) \tag{1}\]  
where:  
* \(P(A\mid B)\): Represents the probability of event A occurring given that event B has already occurred.
* \(P(A\cap B)\): Is the probability of both events A and B occurring simultaneously.
* \(P(B)\): Is the probability of event B occurring, without any additional conditions.  
We used the pgmpy package [26] to generate a CPD and conducted the following queries:  
* What would happen if the GBDT could both see and listen?
* What would happen if the GBDT could see but not listen?
* What would happen if the GBDT could listen but not see?  
To validate the results of these \"what if\" queries [27], we divided the sample into a control group and a treatment group using the causalnex package [28]. By employing Equation (2), we were able to quantify the effects of various interventions, such as activating or deactivating a sensor, using the average treatment effect (ATE). Through a comprehensive  
Figure 4: Example of a match and the data generated. (**a**) Starting positions of the agents. (**b**) Snapshot of the dataset.  
Figure 5: Structural Causal Model (SCM).  
analysis of this metric, we have successfully ascertained the level of accuracy pertaining to the CBOT's confidence in its decision-making process.  
\[ATE=\frac{1}{N}\underbrace{\sum_{i=1}^{N}(Y_{1}(i))}_{\text{Total outcome with treatment}}-\underbrace{\sum_{i=1}^{N}(Y_{0}(i))}_{\text{Total outcome without treatment}} \tag{2}\]  
where:  
* \(N\): Number of samples.
* \(Y_{0}(i)\): Outcome without treatment for sample \(i\).
* \(Y_{1}(i)\): Outcome with treatment for sample \(i\).
* \(i\): Index representing individual samples.