esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Test Results
### Dynamic Stress
#### 4.1.2 Relationship between Dynamic Stress and Velocity and Load  
The variation pattern of dynamic stress amplitude with vehicle velocity is depicted in Figure 7. The curves in the graphs all exhibit a relatively consistent trend, where dynamic stress amplitude tends to increase with the rising vehicle speed, indicating a positive correlation. However, the rate of growth is not particularly pronounced.  
The dynamic stress amplitude at locations with the greater burial depth exhibits an inflection point with increasing vehicle speed. This behavior is primarily attributed to variations in the compaction degree of the roadbed fill material below, induced by high-frequency excitation, which subsequently impacts the vibration characteristics. As a result, dynamic stress amplitude fluctuates, although the overall trend of a positive correlation remains evident.  
The variation in the dynamic stress amplitude at different depths as the load is altered is illustrated in Figure 8. Two speed groups, namely 90 km/h and 100 km/h, can be distinguished based on differences in the axle load.  
The findings indicate that, under the influence of load, there is a relatively consistent trend in the dynamic stress. Specifically, the surface dynamic stress increases in proportion to the load. However, as the depth increases, a negative correlation between dynamic stresses at each point and the load becomes evident. This implies that, overall, the amplitude of dynamic stresses tends to decrease with the increasing load at greater depths within the roadbed.  
It is evident that the correlation between the dynamic stress, speed, and load depends on the burial depth conditions. When the burial depth is shallow, increases in the speed  
Figure 8: Variation of dynamic stress amplitude with load at different depths: (**a**) 90 km/h; (**b**) 100 km/h.  
Figure 7: Variation of dynamic stress amplitude with velocity at different depths: (**a**) 30 t; (**b**) 40 t.  
and load result in higher dynamic stress amplitudes. However, when the burial depth is deeper, specifically below the road base, an increase in speed leads to an overall rise in dynamic stress, with some fluctuations, while an increase in the load leads to an overall decrease in dynamic stress.
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 3 Methods
### Estimation of LAI  
In this study, NDVI was selected as a parameter for calculating the LAI due to its universality for most of the multispectral remote sensing data. The red band reflectance at 665 nm and the near-infrared band reflectance at 842 nm were used to calculate the NDVI according to Equation (1).  
\[\text{NDVI}=\frac{r_{842}-r_{665}}{r_{842}+r_{665}} \tag{1}\]  
The PROSAIL model was used to simulate surface reflectance data with different LAI values. Based on the regression analysis of the simulated dataset, the e-index equation of NDVI was found to be a powerful expression for calculating the LAI using Formula (2), with the Pearson determination coefficient (R) of 0.998, as shown in Figure 3.  
\[\text{LAI}=0.11\times\varepsilon^{3.83\times\text{NDVI}} \tag{2}\]
## 4 Simulation and Analysis
### Single Phase-to-Ground Fault
Figure 14: Comparison of the frequency of the fault current with an adopted DVR-based SMES at a voltage dip depth of 70%; an adopted DVR-based SMES at a voltage dip depth of 20%; crowbar circuit protection at a voltage dip depth of 70%; and crowbar circuit protection at a voltage dip depth of 20% when the single phase-to-ground fault occurs. Figure 13: The system’s responses with a DVR-based SMES during the single phase-to-ground fault (depth of 70%). (**a**) Fault voltages on the 220 kV side; (**b**) fault voltages on the 35 kV side; (**c**) compensating voltage of the DVR-based SMES; (**d**) compensated fault voltages on the 35 kV side; (**e**) stator voltage of the DFIG; (**f**) stator current of the DFIG. Figure 15 shows a comparison of the DFIG output power with an adopted DVR-based SMES at a voltage dip depth of 70%; an adopted DVR-based SMES at a voltage dip depth of 20%; crowbar circuit protection at a voltage dip depth of 70%; and crowbar circuit protection at a voltage dip depth of 20% when the single phase-to-ground fault occurs. With the DVR-based SMES at voltage dip depths of 70% and 20%, the DFIG output power was mostly maintained in the normal output state, which can be seen by comparing this with the crowbar circuit protection at voltage dip depths of 70% and 20%. To sum up, during single phase-to-ground faults with voltage dip depths of 70% and 20%, the DVR-based SMES can improve the frequency of the fault current by compensating for the fault voltage. Additionally, the frequency of the fault current, which fluctuates between 42-53 Hz with crowbar circuit protection, can be maintained at 50 Hz with DVR-based SMES protection. Additionally, adopting DVR-based SMES to assist the DFIG in achieving the fault ride through during single phase-to-ground faults with voltage dip depths of 70% and 20%, the DFIG can output power at a normal value.
# HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method
## 5 Discussion  
Although HP-YOLO shows promising results across both the COCO and bedside pose estimation datasets, several limitations warrant further investigation. First, our bedside dataset, while domain-specific, remains relatively small and lacks the breadth needed to ensure robust generalization in diverse clinical settings. As discussed in Section 4, additional data collection and validation on larger, more varied patient cohorts would bolster the reliability of our approach.  
Second, the proposed OKS-L1 pruning strategy relies on an empirically chosen threshold. Although we conducted ablation studies (Section 4) to identify a reasonably effective pruning ratio, automated threshold selection or adaptive pruning policies could further optimize accuracy-speed trade-offs, especially under different real-time constraints. Moreover, exploring structured or block-level pruning might maintain critical spatial features more effectively than channel-wise or weight-wise pruning.  
Third, while the ELSKA and RepNCSPELAN4 modules successfully address the challenges of pose variation and multi-scale feature fusion, future research could investigate complementary methods, such as attention-based Transformers or graph convolutional networks (GCNs), to further enhance keypoint detection in highly cluttered or occluded scenarios. Likewise, hardware-aware optimizations (e.g., quantization, mixed-precision training) are essential for deployment in resource-constrained environments, aligning with the real-world need for minimal latency in bedside monitoring applications.  
Finally, although preliminary bedside experiments (Figure 5) indicate that HP-YOLO can handle complex patient postures, rigorous clinical validation under institutional review is imperative to ascertain its efficacy in routine healthcare workflows. Such studies would examine how HP-YOLO's improved accuracy and speed translate to tangible improvements in patient care, for instance, by reducing the frequency of falls or enhancing intervention timing for pressure ulcer prevention. Ultimately, further expansions to both methodology and dataset diversity are crucial for establishing HP-YOLO as a robust, generalizable tool in real-world medical settings.
## 4 Result and Discussion
### Performance Metrics Evaluation  
The performance analysis is estimated utilizing metrics such as average end delay (AED), dropped packets (DP), end-to-end delay, packet-delivery ratio (PDR), throughput, and routing overhead (ROH), and the resulting outcome is computed hundreds of times (100 s) at five nodes.  
* Average end delay (AED): The unit of average end delay is the second (s). AED is described as the mean time it takes for the total number of packets to travel from two nodes, which is computed using Equation (29): \[AED=\sum_{i=0}^{n}\frac{(T_{R}-T_{S})}{total\ amount\ of\ packets}\] (29) \(T_{R}\) represents the total amount of time the packet takes to be received, \(T_{s}\) represents the total amount of time the packet takes to be sent.
* Dropped packet (DP): The unit of the dropped packet is percentage (%). The fraction of lost packets compared to sent packets is used to calculate packet loss. \[DP=\frac{total\ of\ packet\ sent-total\ of\ packet\ received}{total\ of\ packet\ sent}\] (30)
* Throughput: The unit of throughput is bits per second (bps). A certain number of data packets are sent to the receiver over a specific period of time. It is usually computed in bits of information per second. \[Throughput=\frac{\sum number\ of\ packets_{received}}{\sum total\ time}\] (31)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Processor** & Intel®Core (TM) i3-3245 [EMAIL_ADDRESS] Ghz 3.40 GHz \\ \hline
**Installed memory (RAM)** & 4.00 GB (3.83 GB usable) \\ \hline
**System type** & 64-bit operating system \\ \hline
**Pen and touch** & No pen or touch input is available for this display \\ \hline \hline \end{tabular}
\end{table}
Table 2: System configurations.  
* End-to-end delay: The unit of end-to-end delay is milliseconds (ms). The amount of time required for a packet to travel from the point of origin to the destination across a system is known as the end-to-end delay. \[d_{end\ to\ end}=T\frac{L}{R}\] (32) Here, \(T\) represents processing time, \(L\) represents the length of time, and \(R\) represents the total ratio.
* Packet-delivery ratio (PDR): The packet-delivery ratio (PDR) is measured as a percentage (%). The PDR is a proportion of the total number of packets transferred to the total number of packets received by the initial node from the system destination node. \[PDR=\left(\frac{\sum\text{Received\ packets}}{\sum\text{Sent\ packets}}\right)\times 100\] (33)
* Routing overhead (ROH): The ROH is measured as a percentage (%). The ROH represents the total number of packets sent by all nodes for controlling the total number of packets received from the destination node. \[ROH=\frac{total\ amount\ of\ packets\ send}{total\ amount\ of\ packets\ received}\] (34)
## 4 Results and Discussion
### Detected Anomalies
#### 4.5.2 Faulty Link  
As earlier seen, Figure 8 shows a detected link failure in the meshed network topology we used for data collection. In a meshed WSN topology, sensor nodes can communicate with numerous nodes within the network, guaranteeing redundancy against connection failures. Connection failures are common in sensing systems because of the inherent unreliability of the shared communication media. Besides, connection disappointments are often caused by hardware failure, software failure, communication failure, aging phenomenon or human error. Since information sent over a fizzled link is lost, the time between a failure and its detection is pivotal for a solid end-to-end communication in the Mesh WSN. Once the link failure is recognized, an effective route is chosen by the routing protocol and communication can proceed. In this way, to increase the unwavering quality of the Mesh WSN, the discovery time for link failures must be kept as low as can be expected under the circumstances. The link failure happened between Node 9 and Node 3 using channel 12. Our investigation reveals that Node 3 temporally lost power at the time Node 9 was establishing its connection. Thus, the detected faulty link was due to a hardware failure.
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 9 Experimental Results
### Relative Position Measurement Experiment  
Next, we performed experiments with only relative position measurements, similar to the simulations in Section 8.4, where the absolute orientation measurements are available throughout the experiments for all vehicles. In the first experiment (no CL), the three vehicles propagate their position throughout while vehicle 1 measures its absolute position at 40 s for only 1 s. The second experiment (CL) is similar to the first except that vehicles 1 and 2 measure the relative positions of vehicles 2 and 3, respectively, for the first 40 s. Note that vehicle simply propagate their positions for the last 39 s in both cases.  
Figure 12 shows the comparisons between the time history of the position error norms of the two experiments for the three vehicles. For the first 40 s, it is evident that CL has a mixed performance, where vehicles 2 and 3 seem to benefit from it while vehicle 1 localization deteriorates. However, after 40 s when vehicle 1 measures its own position for only 1 s, there is a significant drop in estimation errors of vehicles 2 and 3 when compared to the \"no CL\" case. This experiment confirms that even with intermittent absolute orientation measurements, CL can be very effective in improving localization errors.  
Figure 11: Comparison of error norms when vehicle 3 measures its own pose for 1 s at the 40 s marked. Dashed lines indicate CL with relative and absolute pose measurements starting at 40 s, and solid lines indicate no CL.  
Figure 12: Comparison of error norms when vehicle 1 measures its own position for 1 s at the 40 s mark. Dashed lines indicate CL with relative position measurements for the first 40 s, and solid lines indicate no CL.
# Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data
## 4 Discussion
### Comparison of High-Frequency and Low-Frequency Signals  
In the aforementioned cases, when the receiver picks up the echo signal that is creating a false bottom, it travels a greater distance compared with the true-bottom echo. Combining the absorption loss characteristics of sound propagation [31], the following equation can be derived:  
\[TL_{2}=ax \tag{14}\]  
where \(TL_{2}\) represents the propagation loss resulting from medium absorption, as indicated by the absorption coefficient, and \(x\) represents the propagation distance. It is important  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Parameters** & **Quantity** \\ \hline \multirow{3}{*}{First type} & Total & 200 \\  & \textless{}2\% & 195 \\  & 2\%\textless{}5\% & 5 \\  & \textgreater{}5\% & 0 \\ \hline \multirow{3}{*}{Second type} & Total & 200 \\  & \textless{}2\% & 192 \\  & 2\%\textless{}5\% & 6 \\ \cline{1-1}  & \textgreater{}5\% & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of the formula’s calculation of false-bottom depth errors.  
to note that, under identical conditions, the absorption loss of propagation increases in proportion to the propagation distance.  
\[\alpha=A\frac{Sf_{rf}f^{2}}{f_{r}^{2}+f^{2}}+B\frac{f^{2}}{f_{r}}(dB/km) \tag{15}\]  
where \(A=2.03\times 10^{-2}\), \(B=2.94\times 10^{-2}\), \(S\) is the salinity (\(\%\)), \(f\) is the frequency of the sound wave (kHz), and \(f_{r}\) is the relaxation frequency (kHz). In the same medium, at the same temperature and the same propagation distance, there is a positive correlation between the acoustic propagation loss and the acoustic frequency, suggesting that high-frequency signals attenuate more quickly during propagation [32,33]. All input data in this study were obtained from an underwater acoustic instrument called the Scientific Echosounder [34], which is a high-precision, single-beam, multi-frequency detection echosounder for marine organisms. It can be operated using multiple transceivers of different frequencies, allowing for the simultaneous use of multiple band pulses by combining high-frequency signals that experience faster attenuation during propagation. The use of high-frequency pulse signals effectively suppresses false-bottom occurrences without compromising target and bottom recognition. However, it is important to note that, due to the significant loss that occurs during high-frequency signal transmission, weak echo signals may not be detected. Thus, while this approach successfully eliminates false bottoms, it may decrease echo intensity resolution due to the high audio frequency that is emitted.
## 4 Supervised Learning as Statistical Analysis for RVS
### Support Vector Machine  
An SVM [66] is a powerful and flexible model in ML. It performs well in linear, nonlinear classification and regression problems and also for outlier detection. SVM's foundation is built over linear classification problems, primarily for separating two distinct classes using decision boundaries created by the support vectors [67]. Often hyperparameter \(c\) is tuned to regularize the over-fitting of the SVM model. By transforming the input space into a higher dimension, SVM can easily fit for polynomial features, often called as _kernel trick_. The following list shows the most popular polynomial kernels used for nonlinear as well as multi-feature classification problems:  
* Linear: \(K(a,b)=a^{T}\cdot b\);
* Polynomial: \(K(a,b)=(\gamma a^{T}\cdot b+r)^{d}\);
* Gaussian RBF: \(K(a,b)=exp(-\gamma||a-b||^{2})\);  
Figure 7: Data distribution of (**a**) Captive Columns, (**b**) Column Area, (**c**) Concrete Wall Area (EW), (**d**) Concrete Wall Area (NS), (**g**) No. of Floor, and (**h**) Total Floor Area for Pohang.  
* \(\quad\) Sigmoid: \(K(a,b)=tanh(\gamma a^{T}\cdot b+r)\); where \(K\): kernel; \(d\): degree of polynomial; \(a\) and \(b\): vectors in the input space \(\in\mathbb{R}\); \(\gamma\): mapping function; \(r\): an independent parameter, such that \(r\geq 0\).  
With an increase in the complexity of the Kernels, the computational time for the classifier also increases. Linear Kernel SVC is comparatively faster among the rest of the kernels and fits well for more massive data. In the case of nonlinear and complex data, Gaussian RBF works fast.
# Machine Failure Prediction Using Survival Analysis
## 3 Background
### Survival Analysis
#### 3.1.4 Random Survival Forest  
RSF is a machine learning method for predicting survival outcomes, extending the classical random forest (RF) algorithm to handle censored survival data. RSF constructs an ensemble of decision trees, where each tree is grown using a random subset of the data and a random subset of the features. To handle censored data, RSF introduces a new splitting criterion that considers the distribution of the survival times in each node of the tree. Specifically, the splitting criterion is based on the log-rank statistic, which measures the difference in survival times between two groups of observations [13].  
The log-rank statistic is used to measure the difference in survival times between two groups of observations. It is calculated as:  
\[LR=\frac{(O-E)^{2}}{V} \tag{4}\]  
where \(O\) is the observed number of events in the group, \(E\) is the expected number of events based on the Kaplan-Meier estimator, and \(V\) is the variance of the number of events based on [PERSON]'s formula.  
The splitting criterion in RSF is based on the log-rank statistic. At each node of the tree, the algorithm considers all possible splits on all possible features and selects the split that maximizes the log-rank statistic. Specifically, the splitting criterion is defined as:  
\[S=\frac{\left(LR_{left}-LR_{right}\right)}{SE} \tag{5}\]  
where \(LR_{left}\) and \(LR_{right}\) are the log-rank statistics for the left and right child nodes, and \(SE\) is the standard error of the log-rank statistic.  
Once the tree ensemble is constructed, RSF can make predictions for new observations. The predicted survival probability for an observation is calculated as the average of the survival probabilities predicted by all the trees in the ensemble. The survival probability for a given time t is estimated as:  
\[P(t|x)=e^{-H(t|x)} \tag{6}\]  
where \(H(t|x)\) is the hazard function predicted by the tree ensemble for the observation with covariate x.
# Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction
## 4 Experiments
### Analysis of the Pruning Effect of Syntax Trees  
Syntactic features are beneficial for RE tasks because it can capture dependencies between different components of a sentence and usually expresses as tree structure. When we focus on extracting the semantic relationship between two entities in a sentence, some irrelevant information in the dependency tree may interfere with our analysis. Therefore, we choose several different methods for pruning the syntax tree to remove irrelevant information as much as possible. For details about the implementation, see Section 3.4.  
The comparison results are shown in Tables 3 and 4. We find that, for both Conll04 and SciERC, the LCA method shows better performance than ALL and SDP. Intuitively, retaining the entire tree may keep too much irrelevant information, and keeping only the shortest dependency path will be too aggressive for removing information. Therefore, using the LCA method can retain effective features more reasonably. Our experiments have also verified this.  
* **All Model**: Use the complete model proposed above for testing;
* **Local Context Focus**: Remove the local attention module for context, and then explore its contribution to overall performance improvement of the model;
* **Syntactic Feature Fusion**: Remove syntactic features from the multi-head self-attention module and then analyze its contribution.  
When we remove the local context focus mechanism, the performance of the relation extraction task decreases for both datasets. It contributes 1.39 micro-F1 and 1.44 macro-F1 for Conll04, and 2.45 micro-F1 for SciERC. For the global context, we find that the performance of relation extraction becomes worse if we remove all syntactic heads. It contributes 2.59 micro-F1 and 2.84 macro-F1 for Conll04, and 1.34 micro-F1 for SciERC.  
All results described above demonstrate that using part of the heads to focus on syntactic feature is effective. In addition, the pruned syntax tree is an important feature for RE tasks. In addition, by attending to the semantic information of local context, it is possible to exploit more contextual features for the RE task.
## 4 Case Studies
### Sensitivity Analysis of Parameters within Uncertainty Sets
Therefore, the optimized objective function value will also decrease accordingly, and the conservatism of the robust control model for the automatic load commutation device is reduced. In practical applications, decision-makers should consider the impact of objective factors, such as demand-side policies, on the fluctuation of load demand and renewable energy based on the actual distribution network situation. Based on this, they can choose appropriate uncertainty parameters, exclude certain extremely low probability adverse scenarios, and achieve a balance between robustness and economy in the control model of the automatic load commutation device. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Uncertainty Factor** & **Deviation Amount/\%** & \
\begin{tabular}{c} **Objective Function Value of** \\ **the Deterministic Control** \\ **Model/USD** \\ \end{tabular} &
\begin{tabular}{c} **Objective Function** \\ **Value of Robust** \\ **Control Model/USD** \\ \end{tabular} \\ \hline \multirow{3}{*}{Uncertainty of Load Demand} & 10 & 366.17 & 337.09 \\  & 20 & 442.95 & 403.58 \\  & 30 & 528.42 & 483.59 \\  & 40 & 622.66 & 569.20 \\ \hline \multirow{3}{*}{Uncertainty of Renewable Energy Output} & 10 & 294.73 & 315.61 \\  & 20 & 293.84 & 314.92 \\ \cline{1-1}  & 30 & 288.71 & 310.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Impact of uncertainty set deviation on objective function values.