esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Round 2 Training and Testing (Model Improvement by Adding 25% Outliers)  
Table 3 shows the results of the second round of training (Round 2) using the base images supplemented with 25% outliers. A comparison between R2 and other rounds is shown in Figure 3. Considering Level 1 and Level 2 testing results, all models demonstrated high performance with accuracy exceeding 98% and AUC exceeding 99.8% (except VGG-19 at Level 2 with a 95% accuracy). Compared to Round 1, the sensitivity of all models remained high (\(\sim\)99%) despite a minor decrease (<1%); meanwhile, both the specificity and precision increased slightly (\(\sim\)1%).  
At Level 3, all considered models exhibited marked performance improvements, suggesting that the addition of 25% outliers, equating to 62 randomly selected images, effectively addressed the shortfall in outlier-related features. Notably, the classification accuracy increased from 56% to 71% on average, while the sensitivity jumped from 36% to 67% (Figure 3a). This sharp rise in sensitivity highlighted two factors: (1) The Round 1 training dataset (i.e., Base) shared minimal commonality with outlier discriminative features, resulting in notably low accuracy and sensitivity, and (2) even adding a small  
Figure 3: Comparison of averaged classification metrics of the four CNN models with different training datasets (i.e., Rounds 1–5), (**a**) Level 3, and (**b**) Level 4.  
Figure 2: Averaged model performance metrics at four levels of testing after Round 1 training.  
number (62) of relevant images to a large training dataset (Base, 11,538 images) could significantly improve model performance, particularly the sensitivity. Other performance metrics also improved substantially, with AUC increasing from 78% to 90% and precision from 48% to 66%. There was a slight increase in specificity, from 71% to 74%, likely due to the significant imbalance in the distribution of outliers compared to regular images in the training datasets (62 vs. 11,538).  
Round 2 model performance at Level 4 testing (external center) was marked by a slight decrease in classification accuracy (i.e., from 86% to 83%) but a large decrease in sensitivity (i.e., from 67% to 55%, Figure 3b). On the other hand, the specificity increased slightly, from 95% to 97% (Figure 3b). The AUC and precision metrics displayed mixed trends and varying magnitudes (Table 2).
###### Abstract  
When high-speed trains enter tunnels, an initial compression wave is generated. As the compression wave propagates at the local speed of sound to the tunnel exit, it radiates into the surrounding environment, forming micro-pressure waves (MPWs). MPWs create sonic booms, resulting in significant environmental issues. The magnitude of the micro-pressure waves is directly proportional to the pressure gradient of the compression wave at the tunnel exit. The nonlinear effects of the initial compression wave during propagation lead to a significant increase in pressure gradient. Therefore, the propagation characteristics of the initial compression wave during the tunnel are the crucial factor affecting the amplitude of MPWs. Based on the one-dimensional compressible unsteady non-isentropic flow model and the improved generalized Riemann variable characteristic method, this paper researched the propagation and evolution characteristics of an initial compression wave generated when 400 km/h high-speed trains enter tunnels with three portal shapes: (no tunnel entrance hood (no hood), an oblique, enlarged tunnel entrance hood (type A), an enlarged equal-section non-uniform opening hole tunnel entrance hood (type B)). The results show that when the initial compression wave propagates inside very long tunnels, the pressure gradient of the compression wave exhibits a trend of initially increasing and then decreasing with the increase in propagation distance. When the pressure gradient of the compression wave reaches its maximum value, the corresponding propagation distance is the steepening critical distance. For no tunnel entrance hoods, type A tunnel entrance hoods, and type B tunnel entrance hoods, the steepening critical distances are 5 km, 6 km, and 16 km, respectively. The steepening critical distance shortens with increasing train speed. Steady friction and unsteady friction effects mainly affect the pressure amplitude and pressure gradient during compression wave propagation, respectively. At lower ambient temperatures, the nonlinear effects in compression wave propagation are significantly enhanced. The mitigation effects of type A tunnel entrance hoods and type B tunnel entrance hoods on pressure gradient reduction are mainly concentrated within 4 km and 12 km, respectively. It is necessary to determine the optimal matching relationship between the tunnel entrance hood and tunnel length based on the characteristics of compression wave propagation to ensure their mitigating performance is maximized.  
high-speed railway; tunnel micro-pressure wave; very long tunnel; compression wave propagation; pressure gradient; tunnel entrance hood
# Extraction of Creation-Time for Recovered Files on Windows FAT32 File System
## 2 File Allocation Behavior of Windows FAT32 File System
### Allocation of Directory Entries to Record Meta Information of Files  
In the FAT32 file system, each directory entry records the meta information of its corresponding file. The directory entry contains the file creation-time and the offset of clusters allocated to storing of file content. The first directory entry records the meta information of a system file, which is never deleted. Directory entries are usually stored in the first available cluster of the data area, which is called directory entry cluster in this paper. If there is no more available space in the selected directory entry cluster, another available cluster is allotted to recording of directory entries.  
In the Windows FAT32 file system, searching an available directory entry of a new file works mostly in accordance with the next available algorithm. The file system searches for an available directory entry space sequentially from the recently assigned directory entry. When it reaches the end of a directory entry cluster, it goes back to the beginning of the cluster and searches sequentially for an available directory entry space among assigned directory entries. An assigned directory entry becomes available with a deletion mark '0 xE5' in the first byte when its corresponding file is deleted. If the file system fails to find an available directory entry space due to full assignment, it allots another available cluster as an additional directory entry cluster and searches for an available directory entry space sequentially from the beginning of this cluster. After assigning the last directory entry space of this cluster, it goes back to the beginning of the firstly allotted directory entry cluster and search  
Figure 4: Deletion-backward next fit allocation of Windows FAT32: (**a**) content clusters when allocating files A-E and next deleting files B and C, (**b**) content clusters when allocating another file F after (**a**)  
sequentially until it reaches the end of the lastly allotted directory entry cluster. We refer to this allocation pattern of directory entry space on the Windows FAT32 file system as wrap-around next available algorithm. The meta information of recently deleted files still remains in their corresponding directory entries until the file system selects them in order to record meta information of new files only after searching the end of the lastly allotted directory entry cluster. If the meta information of a deleted file remains in its corresponding directory entry, the exact creation-time of a deleted file can be retrieved by matching the cluster offset of the recovered file in the data area with the meta information of all directory entries.  
Although the cluster allocation patterns explained in Section 2.1 are addressed in the [PERSON]'s study [25], the directory entry allocation patterns explained in Section 2.2 are newly verified in our study. Our method is designed by utilizing both the cluster allocation patterns and the directory entry allocation patterns of the Windows FAT32 file system.
# Analysis of Distributed Measurement Method for Array Antenna Position
## 2 Scheme and Principle
### Distributed Position and Orientation System (POS) Technology
\(\delta V^{{}^{\prime}}=\left[\delta V^{{}^{\prime}}_{E}\ \delta V^{{}^{\prime}}_{N}\ \delta V^{{}^{\prime}}u\right]\) is the speed measurement, which is the speed difference between the main POS and the sub IMU after speed compensation by lever arm and the formula is shown as follows:  
\[\delta\mathbf{V}^{{}^{\prime}}=\mathbf{V}_{s}-\mathbf{V}_{m}-\mathbf{V}_{r}^{ l_{m}} \tag{14}\]  
where, \(\mathbf{V}_{s}\) and \(\mathbf{V}_{m}\) are the speed of subsystem and main system in navigation coordinate respectively, \(\mathbf{V}_{r}^{l_{m}}\) is the lever arm speed, which can be calculated by the following formula:  
\[\mathbf{V}_{r}^{l_{m}}=\mathbf{C}_{b_{m}}^{l_{m}}(\mathbf{\omega}_{ib_{m}}^{b_{m}}\mathbf{ \times}\mathbf{r}^{b_{m}})+\mathbf{C}_{b_{m}}^{l_{m}\pi}\mathbf{\}} \tag{15}\]  
where, \(\mathbf{r}^{b_{m}}\) is the deflection displacement measured by the FBG sensing technology, and deflection rate \(\mathbf{r}^{b_{m}}\) can be obtained by differential operation. \(\delta\mathbf{P}^{{}^{\prime}}=\left[\delta L^{{}^{\prime}}\ \delta\lambda^{{}^{\prime}}\ \delta h^{{}^{\prime}}\right]\) is the position measurement, which is the difference between the latitude, longitude and height between the main POS and the sub IMU after the deflection compensation measured by the grating and it can also shown as follows:  
\[\delta\mathbf{P}^{{}^{\prime}}=\mathbf{P}_{s}-\mathbf{P}_{m}-\mathbf{C}_{b_{m}}^{l_{m}}\mathbf{r} ^{l_{m}} \tag{16}\]  
where, \(\mathbf{P}_{s}\) and \(\mathbf{P}_{m}\) are the positions of subsystem and main system in navigation coordinates respectively. The measurement of flexible deformation \(w^{\prime}\) can be obtained directly by FBG. Through the position, velocity, attitude correction of the main system and the measurement of the FBG sensors, the accurate matching amount of the transfer alignment can be obtained, which is helpful to improve the measurement accuracy of the phase center. The accurate matching amount's formula can be shown as follows:  
\[y=\left[\delta\psi\ \delta\theta\ \delta\gamma\ \delta V^{{}^{\prime}}_{E}\ \delta V^{{}^{\prime}}_{N}\ \delta V^{{}^{\prime}}u\ \delta L^{{}^{\prime}}\ \delta\lambda^{{}^{\prime}}\ \delta h^{{}^{\prime}}\ w\right]^{T} \tag{17}\]
# Impact of LGE-MRI in Arrhythmia Ablation
## 2 Use of LGE-MRI for the Detection of Fibrosis
### Protocol of Image Acquisition and LGE Analysis  
To date, there is no agreement on the best standardized way to acquire MRI images for the detection of myocardial fibrosis [28; 29; 30; 31]. Either 3T or 1.5 scanners can be used to acquire postcontrast images applying fast 3D gradient echo series with fat suppression and ECG gating. Subsequent inversion recovery sequences are used to nullify the signal of healthy myocardium and improve signal intensity and T1 contrast. The optimum inversion time (TI) that suppress healthy tissue (typically 250-300 ms) is initially determined empirically. Scar areas will thus appear hyperenhanced relative to healthy myocardium. Adjustment of TI values during acquisition may be necessary to accommodate incremental T1 values of the normal tissue owing to gadolinium washout. For this reason, ECG gating is important to limit motion artifacts and the MRI acquisition window is limited to less to 20% of the RR interval. For patients with AF, cardioversion is often recommended prior to the study to improve image quality [30; 32]. Finally, in some patients another limiting factor could be the need for long breath-holds. To solve that limitation, free-breathing 3D navigators can be used that suppress respiratory artefacts through respiratory gating. Typical LGE-MRI sequences result in a voxel size of 1.25 \(\times\) 1.25 \(\times\) 2.5 mm with scan times of 10-15 min, depending on heart rate and breathing patterns.  
Regarding myocardial fibrosis, to obtain high-quality LGE-MRI images to evaluate it, the time delay between contrast injection and image acquisition is crucial, as the LGE amount depends on wash-in and washout kinetics. Despite there being no official consensus, usually LGE-MRI acquisition is performed 15-25 min (atrium) or 7-15 min (ventricle) after gadolinium contrast agent injection. The time delay may even be adjusted for each patient due to individual perfusion (cardiovascular function) and washout kinetics (renal function).  
In our centre, the acquisition of ventricle images is performed 10-15 min after an intravenous gadolinium injection, and 20 min in the atrium.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 2 Materials and Methods  
### Q-Learning Agent Model  
We formalize a model in which the agent incrementally constructs a sequence of actions, guided by feedback from the environment in the form of rewards. The process of constructing an action sequence can be viewed as a Markov Decision Process (MDP) [29], which is defined by a 5-tuple:  
\[\big{\langle}\mathcal{S},\mathcal{A},\mathcal{P}\big{(}s^{\prime}|s,a\big{)}, \mathcal{R}(s,a),\gamma\big{\rangle},\]  
* \(\mathcal{S}\)--The set of states representing the current state \(s\);* \(\mathcal{A}\)--The set of admissible actions in state \(s\);
* \(\mathcal{P}(s^{\prime}|s,a)\)--The state transition probability function, defining the probability of moving to a new state \(s^{\prime}\) after making action \(a\) in state \(s\);
* \(\mathcal{R}(s,a)\)--The reward function, assigning a numerical reward for performing action \(a\) in state \(s\);
* \(\gamma\in(0,1)\)--The discount factor.  
In our case, the state space is explored by a Q-learning agent. During the interaction process, at each step \(t\in\mathbb{N}\), the agent is in a particular state \(s_{t}\in S\), selects an action \(a_{t}\in\mathcal{A}\), appends the action to the partially synthesized TC, executes it via the environment and receives a reward \(r_{t}\), with further transition to a new state \(s_{t+1}\).  
Since classical Q-learning is a model-free method, the agent does not explicitly use the state transition probability function \(\mathcal{P}\ (s_{t+1}|s_{t},a_{t})\). Instead, it learns based on the Q-function update algorithm [30]. This update rule follows the standard Temporal Difference (TD) learning approach, where the agent refines value estimates by combining observed and expected rewards over time [31]. The utility function \(Q(s,a)\) is approximated as a Q-table that stores the expected reward value for each state-action pair. This enables the agent to improve its test generation behavior through interaction, without relying on specifications or a model. Accordingly, the Q-table values are updated using the classical Bellman equation:  
\[Q(s_{t},a_{t})\gets Q(s_{t},a_{t})+\alpha\cdot\left[r_{t}+\gamma\cdot \underset{a_{t+1}}{\max}Q(s_{t+1},a_{t+1})-Q(s_{t},a_{t})\right] \tag{1}\]  
\(\alpha\in(0,\ 1]\) is the learning rate.
## 4 Experimental Results and Comparative Analyses
### Performance Evaluations  
In this section, we present comprehensive empirical findings from our experiments, analyzing the effects of different components of our methodology on topic modeling performance across the 6G patent datasets.
## 4 Lifetime Improvement Mechanisms in Routing
### Mobile Sink Mechanism
#### 4.2.3 Controlled Sink Mobility
The data forwarding technique which is used in MSRP to send packets towards the mobile sink works as follows. The algorithm can be divided into two main phases based on protocol operation: Setup Phase and Steady Phase. (1) _Setup Phase:_ the sink node begins this phase by sending a beacon message to the neighboring nodes. After that, the sensor nodes which receive that beacon generate and forward a registration message to the sink node. Setup phase consists of three stages that are explained as follows: (A) _Initialization:_ In this stage, the network will be partitioned into several clusters and cluster members send the sensed data to the corresponding CHs. Eventually, the CH nodes wait for the mobile sink to pass nearby in order to forward aggregated data to it. (B) _Mobile Sink Advertisement:_ when a mobile sink reaches a predefined destination, it starts broadcasting a beacon message including its current location information. As soon as a CH node receives this message, it realizes that the mobile sink is in its vicinity and it's time to relay data packets. (C) _CH Registration:_ Upon receiving a beacon message as described in the previous stage, the CH node checks whether it has previously forwardedany data packets to the mobile sink in the current cycle or not. If it has done so before, the CH simply ignores the advertisement message. Otherwise, it replies by sending a registration message back to the mobile sink and waits for an acknowledgment packet. (2) _Steady Phase:_ in this phase, data gathering which is the main responsibility of the mobile sink is discussed completely. Just like the setup phase, it is also divided into three stages as follows: (A) _TDMA Scheduling:_ The registered CHs should receive a TDMA-based scheduling table from the mobile sink. The table consists of time slots in which each neighboring CH node should relay its data to the mobile sink without any conflict with other CHs. (B) _Forwarding to Sink:_ a multi-hop or single hop mechanism will be used by each CH to send sensed data accompanied with the CH's residual energy to the sink. The energy level of CHs can help the mobile sink make a decision for its next movement. (C) _Sink Movement:_ At the first cycle, the sink trajectory is predefined, but in subsequent cycles, it relocates to the position of CHs having a higher power level in order to balance energy consumption all over the network [11]. According to the experimental results [11], the average energy per packet is decreased on the order of about 50% and 10% in comparison with static sink and multiple sink approaches, respectively. However, according to the principles of this algorithm, additional energy consumption would be imposed on sensor nodes for relaying control packets in a multi-hop manner such as periodic CH selection, advertisement broadcasting in each cycle, CHs registration messages and the messages including TDMA-based scheduling table.
# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 4 Results
### Computational Requirements Comparison  
Efficient deployment of semantic segmentation models in real-world robotic systems requires consideration of inference latency and computational resource usage. This section presents a detailed evaluation of latency, memory footprint, and GPU utilization across a range of encoder-decoder architectures tested at two different input resolutions. Table 8 reports the inference performance of four state-of-the-art semantic segmentation architectures (U-Net, DeepLabv3+, FPN, SegFormer) each combined with three ImageNet-pre-trained encoders (MobileNetV4, ResNet-34, EfficientNet-B2). All models were benchmarked over 1000 forward passes using input images at resolutions of 960 \(\times\) 608, with selected architectures being additionally evaluated at the reduced resolution of 480 \(\times\) 304. It summarizes the following metrics: latency statistics (the minimum, maximum, mean inference time, standard deviation) all expressed in milliseconds; the GPU memory requirement for parameter loading in megabytes (MB); and the peak GPU utilization percentage during inference. Inference was measured by running each FP32 ONNX model on GPU via ONNX Runtime CUDA execution provider. In order to ensure experimental consistency, all evaluations were performed on a single computer equipped with an NVIDIA RTX 4070 GPU. Among the evaluated configurations that were processing full-resolution images, DeepLabv3+ with MobileNetV4 delivered the shortest inference time, with an impressively low average inference time of 5.64 ms, combined with tiny GPU memory footprint of just 24 MB. This combination is ideal for any application where short inference time and minimal resource usage are critical. In our experiments, MobileNetV4 outperformed other encoders in terms of processing speed across all evaluated models. Its mean of inference latency was below 9 ms for all model combinations. Additionally, MobileNetV4 demonstrated minimal peak GPU utilization in all model-encoder configurations except when integrated with FPN. EfficientNet-B2, despite requiring intermediate memory for parameter loading, achieved the longest mean inference time for the three model variants. When ranking architectures by overall efficiency, DeepLabv3+ with MobileNetV4 ranks first, closely followed by FPN+ MobileNetV4. When paired with MobileNetV4, FPN achieves an average inference time of 6.97 ms, while requiring only 24 MB of GPU memory. This configuration is preferable when the improved boundary detail offered by multi-scale feature fusion outweighs the slight increase in latency. For systems where the GPU must be shared with other processes, SegFormer paired with MobileNetV4 presents a strong
## 3 Results and Discussion
### Spatial and Seasonal Variations in Heavy Metals
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Station** & **As** & **Hg** & **Cr** & **Cu** & **Ni** & **Pb** & **Zn** \\ \hline  & & & Dry season (December 2015) & & & & \\ F1 & 24.3 & 0.21 & 15.8 & 17.3 & 17.3 & 19.6 & 148 \\ F2 & 37.8 & 0.39 & 58.7 & 32.7 & 26.7 & 22.7 & 200 \\ F3 & 28.5 & 0.36 & 28.6 & 18.6 & 24.0 & 18.6 & 251 \\ F4 & 37.7 & 0.72 & 31.9 & 77.8 & 31.9 & 31.3 & 353 \\ F5 & 25.2 & 1.44 & 91.3 & 64.6 & 34.0 & 26.0 & 218 \\ F6 & 13.1 & 0.99 & 295 & 143 & 57.1 & 49.9 & 501 \\ F7 & 12.6 & 0.39 & 235 & 118 & 39.1 & 33.5 & 343 \\  & & & Wet season (June 2016) & & & & \\ F1 & 19.5 & 0.18 & 26.0 & 31.2 & 27.2 & 11.6 & 119 \\ F2 & 41.1 & 0.33 & 81.9 & 79.4 & 37.7 & 28.0 & 144 \\ F3 & 18.4 & 0.29 & 46.6 & 48.1 & 32.5 & 29.3 & 139 \\ F4 & 44.1 & 0.66 & 46.4 & 91.6 & 62.1 & 44.0 & 292 \\ F5 & 21.1 & 0.31 & 53.1 & 53.2 & 51.8 & 24.9 & 211 \\ F6 & 14.3 & 1.30 & 353 & 175 & 64.3 & 72.8 & 533 \\ F7 & 11.2 & 0.40 & 293 & 148 & 41.1 & 49.5 & 431 \\ ERL & 33 & 0.15 & 80 & 70 & 30 & 35 & 120 \\ ERM & 85 & 1.3 & 145 & 390 & 50 & 110 & 270 \\ TEL & 5.9 & 0.174 & 37.3 & 35.7 & 18 & 35 & 123 \\ PEL & 17 & 0.486 & 90 & 197 & 36 & 91.3 & 315 \\ SEL & 33 & 2 & 110 & 110 & 75 & 250 & 820 \\ UCC & 4.8 & 0.05 & 92 & 28 & 47 & 17 & 67 \\ \hline  & & Criteria values for effect range low (ERL), effect range median (ERM), threshold effect level (TEL), probable effect level (PEL), and severe effect level (SEL) of sediment quality guideline for heavy metals in freshwater ecosystem [26]. Average contents of the upper continental crust (UCC) [21]. \\ \end{tabular}
\end{table} 
Table 1: The concentrations (mg/kg) of heavy metals (As, Hg, Cr, Cu, Ni, Pb, Zn) in the sediments of Fengshan River in dry and wet season. than the criteria value of ERL. As exceeded the ERL at two sites (F2 and F4), Pb exceeded the ERL at three sites (F4, F6, and F7), both Cr and Cu exceeded the ERL at four sites (F2, F5, F6, and F7 for Cr, and F2, F4, F6, and F7 for Cu), Ni exceed the ERL at six sites (except F1), and both Hg and Zn exceeded the ERL at all the sampling sites. Moreover, Hg, Cr, Ni, Zn showed concentrations exceeding the criteria value of ERM, where Hg exceeded the ERM at one site (F5), Cr exceeded the ERM at two sites (F6 and F7), and both Ni and Zn exceeded the ERM at three sites (F4, F5, F6 for Ni and F4, F6, F7 for Zn). These findings indicated that the concentrations of Hg, Cr, Ni, and Zn were severely contaminated in Fengshan River sediments, leading to the frequent occurrence of adverse biological effects. Those kinds of heavy metals pollution may attribute to wastewater discharges from leather processing and metal finishing factories within the river watershed, particularly along the river upstream and tributaries [19; 34].