esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation into the Construction Response of Tunnels through Fault: Model Test
## 7 Conclusions  
This study used large-scale 3D model tests to investigate the impact of different fault dip angles on the central diaphragm method (CD method) tunnel excavation process. The results of the study are as follows:  
1. The radial surrounding rock pressure increases with steeper fault dip angles. When the fault dip angle is 75\({}^{\circ}\), the surrounding rock pressure can be up to 1.75 times that without a fault. The vault radial pressure increases initially and then decreases during the tunneling process, with an excavation influence range of one times the tunnel diameter (1 D).
2. As the excavation depth increases, there are significant displacements at the spandrel and vault. The range of surrounding rock relaxation is less than one times the tunnel diameter.
3. Lining structure stress increases with the fault dip angle. The pressure at fault dip angles of 60\({}^{\circ}\) and 75\({}^{\circ}\) in the radial direction is 1.08 and 1.16 times, respectively, that at the angle of 45\({}^{\circ}\). In the longitudinal direction, the pressure at the vault with fault dip angles of 60\({}^{\circ}\) and 75\({}^{\circ}\) is 1.16 and 1.79 times, respectively, that at the angle of 45\({}^{\circ}\).
4. The numerical simulation confirms the reliability of the model test results.
5. Faults have a significant impact on tunnel structures, and the construction risk of tunnels in fault areas is high. Safety issues should not be overlooked.  
Overall, the findings of this study provide valuable insights into the impact of fault dip angles on tunnel construction and highlight the importance of taking into account the geological conditions when designing and constructing tunnels. The results can be used to optimize the design and construction process of tunnels in fault areas, ultimately improving the safety and efficiency of tunnel construction.  
Figure 18: Vertical displacement curve of the lining for different fault dip (the data come from the situation of the 10 cm fault dislocation in [14]).  
**Author Contributions:** Methodology, S.W.; Validation, S.W.; Formal analysis, P.L.; Investigation, P.L.; Data curation, P.L. and S.W.; Writing--original draft, P.L., S.X. and Z.W.; Writing--review & editing, X.Y., Z.W. and S.W.; Supervision, Z.W.; Funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research work reported in this paper was supported by the Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102212204).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The authors declare that all the data of this paper are available.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### Motivations  
A lot of research has focused on addressing co-channel interference issues in a single network by proposing efficient resource allocation schemes but without considering critical scenarios like the overlapping of two LTE networks [14; 15; 16; 17; 18]. Moreover, the concept of the mobile personal cell has been used previously by considering a single network [13; 19]. However, deployment of the mPC has not been evaluated in the complex environment of coexisting public safety networks. Mobile communications technology is evolving into 5G systems in which the user needs identical, reliable connectivity everywhere and anytime. Therefore, we focus on the coexistence of next-generation public safety networks, and we employ an efficient QoS priority-based mPC deployment scheme for load balancing and interference reduction. However, the deployment of an mPC in complex scenarios generates new challenges, i.e., user associations, CCI, QoS prioritization, etc. Thus, immense interest and practical research are needed to resolve the aforementioned issues. In the overlapped environment of Korea's PS-LTE and LTE-R networks, where both high-priority users and low-priority users exist in a random fashion, it is very important to deal with users based on their QoS requirements and defined service priority levels during the resource allocation process.
## 3 Results
### Side Effects of HIFU
#### 3.2.1 Skin Complications  
MFU-V/HIFU devices are moderately painful and may cause transient erythema with or without edema. A recent review of sixteen studies (with a total of 337 patients) on the safety and complications of micro-focused ultrasound (MFU) reported rare adverse effects, including the following:  
* Dysesthesia (numbness or hypersensitivity);
* Bruising and stinging;
* Mandibular burns;
* Striations (streak-like marks on the skin);
* Contact dermatitis [3].  
Different device settings and treatment protocols can result in varying risks. Some studies have reported that edema, erythema, and pain are common on the first day after treatment, while hyperpigmentation may occur three months post-HIFU therapy [4]. In some cases, healthcare professionals may struggle to identify complications affecting the epidermis. Additionally, it can be challenging to determine why HIFU treatment has failed or why the skin appears worse than before.  
Reports suggest that plastic surgeons often encounter difficulties when performing facelifts on patients who have previously undergone incorrectly applied non-invasive HIFU treatments. If tissue overheating occurs due to improper device settings, multiple passes on the same area, or inadequate time between energy-based treatments, complications may include the following: (a) cheek fat atrophy and (b) significant scar tissue formation [18].  
A study combining HIFU with Botox and temporary and semi-permanent dermal fillers (within six months of treatment) reported seven adverse events:  
* Bruising/purpura;
* Swelling;
* Paresthesia;
* Herpes simplex virus (HSV) outbreak.  
Of these, only the HSV outbreak was attributed to the combined treatments [19].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Complications** \\ \hline Pain \& Sensory & Pain, prolonged numbness [13; 14] \\ \hline Skin & \
\begin{tabular}{c} Edema, erythema, bruising/purpura, hypopigmentation, \\ scarring [9; 15; 16], cheek fat atrophy, atrophy, and/or cutaneous \\ necrosis [17], significant scar tissue formation, swelling, paresthesia, \\ and herpes simplex virus (HSV) outbreak [18]. \\ \hline Eyes &
\begin{tabular}{c} Potential ocular complications (raumatic cataract, visual \\ impairment, and injuries to the iris and conjunctiva) [6]. \\ \end{tabular} \\ \hline Fat Tissues & Fat atrophy [19; 20]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complications of HIFU treatments in esthetic procedures (Modified by [PERSON] [PERSON], 2025).
# Topology Optimization of Functionally Graded Structure for Thermal Management of Cooling Plate
## 2 Methodology
### Filtering and Projection  
To avoid the phenomenon of mesh dependency and check-broad problems and to limit the minimum length scale of the optimized structure, the Helmholtz filtering operator is utilized to deal with a such situation, which is constructed as Equation (10):  
\[-r^{2}\
abla^{2}\tilde{\phi}+\tilde{\phi}=\phi \tag{10}\]  
where \(r\) is the influence factor that can adjust the feature size of the structure, and \(phi,\tilde{\phi}\) are the original and filtered design variables, respectively.  
Although the filtering operation can relieve the phenomenon of check-broad problems, it introduces numerous gray elements during optimization. Thus, the projection function constructed in Equation (11) is utilized to clear the layout of the topology structure, where \(\tilde{\phi}\) is the projected design variable, namely, physical density. The parameter \(\beta\) controls the sharpness of the projection function, and \(\eta\) is the projection threshold, which is usually set to 0.5.  
\[\tilde{\phi}=\frac{tanh(\beta(\tilde{\phi}-\eta))+tanh(\beta\eta)}{tanh(\beta( 1-\eta))+tanh(\beta\eta)} \tag{11}\]
## 3 Results
### Accuracy  
Since monitors 1 and 2 were calibrated by collocation with three research-grade TSI Siderpaks Model 510, which had themselves been calibrated against gravimetric monitors, we estimate the accuracy of the CF_1 algorithm using the average of these two monitors (four sensors) as the reference. Regressions of the CF_1 estimates against the alt CF3.4 estimates for sensors 1a, 1b, 2a and 2b resulted in slopes of 1.60, 1.65, 1.63, and 1.70, indicating overestimates by 60-70% for the CF_1 algorithm. Similar overestimates have also been noted by multiple investigators [3; 9; 10; 12; 13; 14]. An example is shown from the most recent dataset from 1 January 2023 to 13 July 2023 (Figure 6).  
Limit of Detection (LOD)  
The LOD for the two algorithms was calculated using a method introduced in [30]. The method involves identifying all cases with the mean/SD < 3 and searching for their appearance in \"batches\" of 100 or so samples ordered by concentration. If, beyond a certain concentration, there are no cases with five or more such values appearing in each 100-sample batch, then the LOD has about a 95% probability of being that concentration. The LOD is of particular interest for indoor studies since indoor concentrations are often quite low. For the five collocated indoor PA-II and Flex monitors in the most recent 2023 data from Santa Rosa, the LOD was 0.106 \(\upmu\)g/m\({}^{3}\) for the pm2.5 alt algorithm and 1.22 \(\upmu\)g/m\({}^{3}\) for the CF_1 algorithm. Although both values seem fairly small, because of the low concentrations  
Figure 6: Regression of CF_1 estimates of PM\({}_{2.5}\) for sensor 1b vs. the calibrated pm2.5 alt estimate as the reference.  
in general, only 29% of the 39,014 measurements were above the CF_1 LOD, compared to 99% above the LOD for the pm2.5_alt algorithm.
## 3 Results and Discussion
### Field-Effect ICEO of a Janus micropillar Subjected to a Controllable Gate Voltage  
As revealed previously, when the metal phase of Janus post is floating in potential or exempt from external Ohm contact (Figures 3 and 4), two strong electro-osmotic vortexes in reverse-rotating directions selectively appear on its conducting side and meet one another at the equatorial point along the centerline of the channel length (Figure 4B). This left-right symmetry breaking in ICEO streaming leads to a sufficiently intense forward-pump-fluid motion along the channel axial direction under a neutral metal-phase angle \(\alpha\) = 180\({}^{\circ}\). Inspired by previous scientific reports on field-effect flow rectification in microfluidic networks [37; 66; 67; 68], an arbitrarily biased gate voltage \(\mathrm{V}_{\mathrm{G}}\) can help regulate electro-convective mass transfer in the transversal direction. Thus, an organic combination of AC field-effect flow control with the already axially biased ICEO streaming induced around a Janus pillar may simultaneously cause adjustable symmetry breaking in the ICEO's flow profile along the length and width of the channel orthogonal to each other, namely, the technique of Janus AC-FFET.  
To validate this intuitive hypothesis, a direct numerical simulation was conducted under a variable gate-voltage offset of the metal phase:  
\[\bigtriangleup\mathrm{V_{G}}=\mathrm{V_{G}}-0.5\mathrm{V_{D}} \tag{36}\]  
or, equivalently, in terms of the biasing ratio of the gate voltage:  
\[\beta=(\mathrm{V_{G}}-0.5\mathrm{V_{D}})/0.5\mathrm{V_{D}} \tag{37}\]  
The gate-voltage polarity is defined such that it is positive (negative) for a positive (negative) \(\bigtriangleup\mathrm{V_{G}}\) or \(\beta\). In correspondence with the negatively or positively polarized gating metal matrix, the background electric-field strength augments on the top or bottom side of the Janus pillar but attenuates on the other side (not shown). This directly gives rise to the transversal asymmetric distribution of the local field intensity, interacting actively with the in situ enhanced double-layer polarization to induce a biased ICEO-vortex flow field with newly generated lateral symmetry breaking parallel to the applied voltage, which is effectively superimposed on the pre-existing horizontal-flow asymmetry subjected to no gate-voltage bias.
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 1 Introduction
Hidden layers that are not sent to the parameter server are accumulated in the worker's local cache. When the accumulated value is greater than the threshold, the hidden layer stored in the worker's local cache is sent to the parameter server to ensure training accuracy. * Second, we propose an efficient method to pick a threshold value. The threshold is a value that can reduce the size of the gradients that the worker will send to the parameter server by a ratio \(R\) from the size of the total gradients. It takes much time to calculate a threshold by reflecting all the gradients. Therefore, in this paper, we compute the threshold by replacing the value of each gradient with the L1 norm of the gradients included in each hidden layer. Since the L1 norm is the sum of the absolute values of each element of the vector, the L1 norm of the gradients of each hidden layer shows the effect of the gradients on the model parameters. By using the L1 norm of each hidden layer, a large number of gradients to be used for threshold calculation can be  
Figure 1: Communication time increases as the number of workers increases. reduced to the number of hidden layers in the deep learning model. The threshold can be fixed at the value calculated at the beginning of training or can be calculated again at specific cycles. In our experiments, we calculated a new threshold every 100 steps. We implement a distributed deep learning system and the proposed optimization scheme using MPI and TensorFlow and show improvement in performance when performing distributed deep learning through experiments. When communication is optimized by applying the layer dropping scheme proposed in this paper, we confirm that the communication time decreased by about 95% and the total deep learning execution time by about 85% in a 1 Gbit network environment. The rest of the paper is organized as follows. We present the related work in Section 2. In Section 3, we describe the architecture of distributed deep learning as a background. The layer dropping scheme we proposed is described in detail in Section 4. In addition, Section 5 describes how to compute a threshold value for performing layer dropping. In Section 6, we evaluate the performance of the proposed communication optimization scheme. Finally, we conclude our paper in Section 7.
## 4 Experiment Results and Discussions
### Analysis of Different Schemes  
We conducted a series of ablation studies on our constructed validation set to evaluate the contributions of the primary components of CRTD, specifically the Thin Cloud Removal (TCR) module and Small Target Detection (STD) module. These studies demonstrated the critical role of these modules in addressing thin cloud interference and enhancing small target detection. While the unified ROI-Loss control strategy is integral to the overall CRTD framework, its contribution was assessed indirectly through the performance improvements achieved with TCR and STD integration. Detailed analyses of the ROI-Loss strategy and its theoretical benefits are provided in Section 3.3. We used RT DETR as the baseline network and sequentially integrated each module to validate the contribution of each scheme. To ensure fairness in the experiments, all parameters remained constant except for the modules being evaluated. The results show that each successive method outperforms the previous one. Methods 1-5 correspond to the sequential integration of the different modules, with each method representing an incremental addition to the baseline network. When all three schemes are incorporated into the model, the Average Precision (AP) increases to 89.05%. This demonstrates that all proposed schemes in this paper are indeed effective.  
Additionally, the enhanced STD component (second row in Table 2) increased the overall AP value by around 3% compared to the baseline network (first row in Table 2), demonstrating its effectiveness in improving the detection of small ships in low-resolution images. Similarly, the third row shows that integrating the STD component to boost the contrast between targets and their surroundings resulted in a 4% increase in both AP and recall rate. The final row displays the results of CRTD processing remote sensing images under thin cloud cover, indicating that the AP and recall rate were approximately 3% higher than in the previous scheme. This underscores the crucial role of the ROI-Loss strategy within the dual supervision framework.  
Figure 9: Precision–recall curve for ship detection: comparison of different methods. This curve plots precision (%) against recall (%), where higher precision indicates fewer false positives, and higher recall reflects better identification of true ship targets. The area under the curve (AUC) serves as a key metric for model performance, with a larger AUC indicating better detection accuracy across thresholds.  
Figure 10: Comparison of ship detection results using different methods. This figure shows the detection results for various methods: Faster R-CNN (**a**), SSD (**b**), YOLO (**c**), RT-DETR (**d**), and our proposed method (**e**). Green boxes indicate true ship targets, red boxes show detected regions, and blue boxes highlight incorrect detections (false positives or false negatives). Subfigure (**f**) displays the ground truth for comparison, providing a visual analysis of each method’s accuracy and performance in detecting ship targets across different scenarios.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### Transmission Tower Identity Matching Framework  
The pseudocode for the identity matching process of the transmission tower is presented as Algorithm 1. To facilitate real-time detection on mobile devices, the YOLOv5n-conv head is employed in this study for efficient transmission tower detection during the model's inference phase. Firstly, input images are processed through the YOLOv5n-conv head for transmission tower detection. In scenarios where the input images do not contain transmission tower instances, these images are skipped without further processing. However, when transmission tower instances are detected, they are cropped from the input images for subsequent matching processes. The focus of this section is on cases where transmission tower instances are present in the input images. For images with transmission tower instances, the matching algorithm is shown in Figure 11. In our designed matching algorithm, we utilize different databases for matching based on the presence or absence of standard GPS data. Consequently, this paper employs arrows of three different colors--yellow, black, and red--to guide the reader through the entire matching procedure. In Figure 11, the yellow arrows indicate the necessary steps for the matching algorithm. The black arrows serve as indicators, illustrating the algorithm's operational flow when GPS data are absent in the input image. Conversely, the red arrows depict the algorithm's process when GPS data are present in the input image. Feature vectors, denoted as features1, are obtained through the matching network discussed in Section 2.3.1. The system determines whether the input image contains GPS information. If the input image lacks GPS data, the model compares the feature vectors generated by the matching network with those stored in the template library using the Euclidean distance, a process detailed in Section 2.3.6. If the input image contains GPS data, the model first filters the template  
Figure 10: Template library construction flowchart.  
library using this GPS information to form a candidate set (Subset). The feature vectors from the input image are then compared with those vectors in the candidate set using the Euclidean distance to identify the best match. When the Euclidean distance between the most similar image and the original exceeds 1, the model determines that the transmission tower is not present in the template library.  
Figure 11: Framework for identity matching of transmission towers from input images.
# Design of Wideband Decoupling Antenna Array for 5G Smartphones at N77/N78/N79/WLAN 5 GHz Bands
## 4 Conclusions  
In this paper, an antenna pair with a coupled feed structure and a defected ground structure is proposed by the CMA method, which is characterized by wide bandwidth, high isolation and compact structure. Simulation results show that the proposed MIMO antenna pair has an impedance bandwidth of 3.28-5.85 GHz (\(-10\) dB) with an isolation of lower than \(-16.7\) dB, and the antenna pair's radiating direction graphs can be spatially complementary to each other, which makes the antenna pair have good omnidirectionality. The proposed antenna pair is utilized to construct an eight-element MIMO antenna system, and the composite decoupling technique is applied to decouple the two antenna pairs. The constructed eight-element MIMO antenna system is simulated, a prototype is fabricated for measurements, and the results show that the MIMO antenna system has a \(-10\) dB impedance bandwidth of 3.30-5.83 GHz, isolation lower than \(-12.42\) dB, ECC lower than \(0.01\), the total efficiency is generally better than 50%, and the maximum is up to 88%. Finally, the effect of the human hand on the performance of the antenna system when the user is using the cell phone is simulated, which further validates the robustness of the proposed MIMO antenna system with its potential to be applied to future 5G mobile terminals.  
Conceptualization, Z.L. and Y.W.; methodology, Z.L. and Y.H.; software, Z.L. and L.Z.; validation, Z.L and Y.W.; formal analysis, Z.L. and Y.H.; writing--review and editing, Z.L.; supervision, Y.W.; project administration, Z.L.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 61366006). This research was funded by Gansu Provincial Department of Education: Outstanding Graduate Students \"Innovation Star\" Project, grant number 2023 CXZX-545.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.