esa-sceva/satcom-chunk-collection huggingface dataset:
# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
###### Abstract
We attach our proposed module and improve upon the baseline tracking algorithm of GlobalTrack [29] which is a global search-based tracker that does not employ any motion smoothness constraints and hyperparameters, where we can analyze the effect of our proposed bottleneck module in a more accurate manner. Furthermore, our proposed bottleneck module can be attached to any Siamese network-based tracker and yield a substantial reduction in computational load. To exhibit the benefits of the proposed tracking framework, we conduct evaluations on recently introduced large-scale benchmarks for visual tracking, LaSOT and GOT-10k, for quantitative and qualitative evaluations for comparison between other tracking algorithms. For further analysis, we perform ablation experiments on the LaSOT dataset to validate the performance gains and reduction in computation brought by our proposed bottleneck module. The motivation for our bottleneck module for the tracking framework is shown in Figure 1.  
The contributions of the proposed method are as follows:  
1. In contrast to previous lightweight tracker design approaches where only backbone feature extractors are compressed without consideration of prediction heads, or require a specialized network structure for an architecture search, our feature bottleneck approach is more versatile in terms of backbone network selection and can reduce the computation of prediction heads. 2. Under evaluation, we obtain comparable tracking accuracy metrics on multiple benchmarks for visual tracking, LaSOT and GOT-10k, using lighter backbone feature extractors, compared to the other heavier ResNet backbones used in [29]. 3. Owing to our proposed feature bottleneck module, we accomplish a real-time processing speed of 74 fps which is substantially faster than the sub-real-time speeds of previous global search-based, computationally heavier visual tracking algorithms.
## 3 Methodology
### Obtaining the Local ET,\(LE_{loc}\)
#### 3.1.2 Calculation of \(T_{a-loc}\)  
Because \(T_{a-loc}\) is specific under the condition of energy balance closure, it can be calculated by the energy balance equation (Equation (3)). Together with the sensible heat flux equation (Equation (4)) and the Bowen ratio, \(\beta\), equation (Equation (7)), \(T_{a-loc}\) can be derived as follows:  
\[\beta=\frac{H}{LE} \tag{7}\]  
\[T_{a-loc}=\text{LST}-\frac{\beta\left(R_{n}-G\right)}{\beta+1}\frac{r_{a}}{ \rho C_{p}} \tag{8}\]  
In Equation (8), the Bowen ratio, \(\beta\), is determined according to an empirical equation presented by [PERSON] et al. (2017). The equation is written as:  
\[\beta=\text{A}\frac{\left(P_{max}-P_{min}\right)-\left(P_{i}-P_{min}\right)}{ \left(P_{i}-P_{min}\right)} \tag{9}\]  
where A is an empirical coefficient that equals 0.66, \(P\) (W-m\({}^{-2}\)-s\({}^{0.5}\).\({}^{\circ}\)C) is the simplified thermal inertia, and \(P_{max}\) and \(P_{min}\) are, respectively, the maximum and minimum thermal inertia that are determined from the trapezoid space formed by the vegetation index (VI) and LST. The simplified thermal inertia \(P\) is defined as:  
\[P=\frac{\overline{R}_{n}\left(t_{1}-t_{1}\right)^{0.5}}{\left(T_{02}-T_{01} \right)} \tag{10}\]  
where \(\overline{R}_{n}\) (W/m\({}^{2}\)) is the average net radiation during \(t_{1}\)(s) and \(t_{2}\) (s), \(T_{02}\) (\({}^{\circ}\)C) is the surface temperature when the satellite is passing, and \(T_{01}\) (\({}^{\circ}\)C) is the daily minimum surface temperature. \(t_{2}\) and \(t_{1}\) are the occurring times of \(T_{02}\) and \(T_{01}\), respectively.
# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 3 Related Work
Whenever a cut-vertex failure is detected, the neighbor CHs broadcast Recovery messages to all their neighboring nodes toward the sink node until the next actor node or the next CH is found for lost connectivity. Recovery Phase is executed by finding a stable sensor with high transmission power and higher coverage. The stable sensor CHs (as per the GA-based criterion) among their neighbor nodes is the bridging router for connecting the disjoint network. Even though actor nodes include higher transmission power, the process still consumes more energy; the algorithm consumes energy because of the use of clustering. Moreover, sensor involvement in the recovery process impacts the overall network lifetime and performance due to the sensors' limitations. The Partitioning detection and Connectivity Restoration (PCR) algorithm is introduced to endure critical actor failure [50]. The PCR regulates critical/non-critical actors using localized information and replaces each critical node with a suitable backup. The pre-designated backup process determines the failure if it is primary actor node and starts the post-failure recovery process that includes coordinated multi-actor relocation. The author constructed the formal specification of PCR using Z notation. An update of the selection of the actor's polling points is proposed in [51]. The update selection process involves the residual energy and the locations of the nodes. The approach dynamically generates the multi-hop routing trees used by the polling points in order to balance energy consumption of the node to prolong the network lifetime. However, the paper focused on sensor nodes and also did not address the actual node failure recovery process. All existing approaches either attempt to recover the failure actor or try to reduce the overhead [8; 9; 30; 31; 32; 33; 34; 35; 38; 52; 53; 54]. We conclude that existing approaches have attempted to replace the critical node with another backup node, but they fail to maintain the QoS parameters and energy consumption. For guaranteeing QoS in our EAR proposed algorithm, the Node Monitoring and Critical Node Detection algorithm (NMCND) monitors the activities of the nodes to determine the nodes' types to distinguish critical nodes. Additionally, our proposed approach not only determines the critical nodes, but handles the packet forwarding process when a primary node fails. To handle packet forwarding, the Network Integration and Message Forwarding (NIMF) algorithm is introduced. In addition, the Process-Based Routing for Node Failure Avoidance algorithm (PRNFA) is developedto handle the routing process and to eliminate routing process of the redundant packets to other nodes in order to avoid the network congestion and reducing the latency. Therefore, the goal of this work is to improve the recovery node process while maintaining the QoS provisioning and power efficiency.
# Numerical Study of the Seismic Response of an Instrumented Building with Underground Stories
## 1 Introduction
Recognizing the importance of the previous studies and their findings, the present work aims to further examine the effect of, including the underground stories and their connection with the surrounding soil into the numerical model of building structures. To this end, different soil/substructure modeling approaches are tested for their fit to a previously instrumented building. The selected structure corresponds to an RC mixed system of frames and shear walls with 16 stories above ground level and three subterranean levels, and it is located in the city of Vina del Mar, Chile. The monitoring system recorded the building's response to several aftershocks that followed the 2010 Mw 8.8 Maule, from which 11 earthquakes with different epicenters and depths were selected. These shakings were perceived as low-intensity ground motions (the epicenters were several hundreds of kilometers away from the city of Vina del Mar), so the building did not respond in the nonlinear range, which allows for linear elastic analysis. The structure's numerical model was created in the ETABS computer package [34] based on the structural drawings. Different building models, including underground floors, were used to compare the numerical and experimental results. Acceleration records registered at the base of the building were entered as input into the numerical model. Each model's results were compared to determine the best approximation to the recorded building response and to evaluate if more rigorous foundation modeling may contribute to better engineering practice.
## 2 Literature Review
### The Shift Toward Unsupervised Learning  
In response to these limitations, the research community has increasingly turned to unsupervised learning methods, which identify patterns and anomalies in unlabeled data [15; 16; 17; 18; 19]. These techniques are especially useful for applications in PdM, where equipment failures are rare and labeling is impractical. Unsupervised methods are particularly advantageous in scenarios where failure modes are poorly understood or evolve, as they enable adaptive modeling of equipment degradation [20; 21].  
Among the most prominent unsupervised methods are autoencoders and Gaussian Mixture Models (GMMs) [22; 23]. Autoencoders, a type of neural network, are used for compressing high-dimensional sensor data into lower-dimensional latent spaces that capture essential features [24; 25; 26]. Variants such as variational and denoising autoencoders further improve performance in noisy or incomplete datasets [27; 28; 29].  
Among unsupervised techniques, dimensionality reduction and clustering algorithms have gained prominence for their ability to distill high-dimensional sensor data into actionable insights. Principal Component Analysis (PCA) and t-SNE are widely employed to visualize and interpret multivariate sensor streams [30; 31], while clustering methods like K-means partition data into distinct health states [23]. Clustering methods, such as GMMs and K-means, categorize equipment states (e.g., healthy, degraded, faulty) without prior labels. GMMs are particularly suited for industrial sensor data due to their probabilistic foundation, which handles overlapping clusters and non-uniform distributions better than hard clustering methods like K-means [32; 33; 34; 35].
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
###### Abstract  
Given the burgeoning necessity for high-speed, efficient, and secure wireless communication in 6G, visible light communication (VLC) has emerged as a fervent subject of discourse within academic and industrial circles alike. Among these considerations, it is imperative to construct scalable multi-user VLC systems, meticulously addressing pivotal issues such as power dissipation, alignment errors, and the safeguarding of user privacy. However, traditional methods like multiplexing holography (MPH) and multiple focal (MF) phase plates have shown limitations in meeting these diverse requirements. Here, we propose a novel spatial multiplexing holography (SMH) theory, a comprehensive solution that overcomes existing hurdles by enabling precise power allocation, self-designed power coverage, and secure communication through orbital angular momentum (OAM). The transformative potential of SMH is demonstrated through simulations and experimental studies, showcasing its effectiveness in power distribution within systems of VR glasses users, computer users, and smartphone users; enhancing power coverage with an 11.6 dB improvement at coverage edges; and securing data transmission, evidenced by error-free 1080P video playback under correct OAM keys. Our findings illustrate the superior performance of SMH in facilitating seamless multi-user communication, thereby establishing a new benchmark for future VLC systems in the 6G landscape.  
digital holography; visible light communication; orbital angular momentum +
Footnote †: journal: _photonics_
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 3 Results
### Extreme Gradient Boosting  
XGBoost is proven to be an ensemble algorithm that has excellent performance. However, more hyperparameters need to be tuned, such as the number of trees in the forest, maximum tree depth, regularization parameter and learning rate, etc. Similar to Random Forests, Bayesian optimization is employed for tuning. Compared with the grid search and other tuning methods, Bayesian optimization can find the global optimal solution with high efficiency [38].  
As shown in Table 5 and Figure 10, in the best prediction model, RMSE = 1.3290% and \(R^{2}\) = 0.9234 for OMC, and RMSE = 0.4447 kN/m\({}^{3}\) and \(R^{2}\) = 0.9089 for MDD. The results show that XGBoost has a good performance in predicting both OMC and MDD, and its \(R^{2}\) can reach above 0.90.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-5}  & **RMSE** & \(R^{2}\) & **RMSE** & \(R^{2}\) \\ \hline OMC & 0.9368 & 0.9619 & 1.3605 & 0.9198 \\ MDD & 0.3486 & 0.9378 & 0.5664 & 0.8523 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance results of RF.  
Figure 9: Regression plots between actual and predicted values with RF; (**a**) OMC; (**b**) MDD.  
XGBoost has superior model performance and generalization ability whether it deals with classification problems or prediction problems. As a result, XGBoost has become one of the preferred algorithms for problem-solving in the field of machine learning. However, XGBoost has many built-in hyperparameters and still needs to be carefully tuned for the problem to be solved to obtain the best model performance.
## 4 Results
### Visual Results of Segmentation Outputs--KITTI Dataset  
The visual results of segmentation outputs on the KITTI dataset were presented in Figure 9 and provide a qualitative assessment of the model's performance in urban driving environments. This dataset, known for its real-world street-level scenes, helps evaluate how well the model identifies and segments key objects such as roads, vehicles, pedestrians, and lane markings. By visually comparing the predicted outputs with the ground truth, these results highlight the model's ability to maintain spatial accuracy and semantic consistency, reflecting its suitability for autonomous driving applications.  
The training accuracy and validation loss are presented in Figures 10 and 11, respectively. The plots illustrate the training progress of a model over 100 epochs for the KITTI dataset, focusing on accuracy and loss for both the training and validation sets. In the accuracy plot, the training accuracy steadily improves and converges near 100%, indicating that the model has effectively learned from the training data. The validation accuracy also rises sharply in the initial epochs and stabilizes around 90%, though with slight fluctuations, suggesting good generalization with minor instability across validation samples.  
Figure 8: Validation Loss (Cityscapes dataset).  
Figure 9: Segmentation results of the proposed method for the KITTI dataset.  
In the loss plot, training loss decreases rapidly and approaches zero, confirming strong learning efficiency. The validation loss follows a similar downward trend initially but shows occasional spikes in the later epochs, which could be due to overfitting or variability in the validation data. Overall, the model demonstrates strong learning performance, with high training accuracy and low training loss, alongside reasonably stable validation metrics, making it suitable for real-world deployment with minimal tuning.  
Figure 12 illustrates the performance of the proposed hybrid model across three widely-used datasets: Cityscapes, BDD100K, and KITTI. The model demonstrates consistently high performance across all evaluation metrics, including Accuracy, Precision, Recall, F1-Score, AUC, and mIoU. On the Cityscapes dataset, the model achieved an accuracy of 98.88%, precision of 98.85%, recall of 98.86%, F1-score of 98.85%, AUC of 98.10%, and a mean Intersection over Union (mIoU) of 89.02%. For BDD100K, it showed slightly improved results with an accuracy of 98.94%, precision of 98.91%, recall of 98.93%, F1-score of 98.91%, AUC of 98.40%, and the highest mIoU of 89.46% among all datasets. On the KITTI dataset, the model maintained robust accuracy (98.79%) and F1-score (98.76%), though the mIoU was slightly lower at 88.13%. Overall, the hybrid model delivers strong and balanced performance, particularly excelling in segmentation quality on the BDD100K dataset, confirming its suitability for real-world ADAS applications.  
Figure 11: Validation Loss (KITTI dataset).  
Figure 10: Train Accuracy (KITTI dataset).
## 3 Data Set Description  
### CYGNSS data  
There are three levels of CYGNSS data products in version 2.1, which represent the second post-provisional based on calibrated and validated level 1 algorithms. The level 1 (L1) dataset contains the measurement of surface Normalized Bistatic Radar Cross Section (NBRCS). The level 2 (L2) dataset includes derived ocean surface wind speed and Mean Square Slope (MSS). The level 3 (L3) dataset delivers hourly averaged wind speed and MSS on a 0.2 degree \(\times\) 0.2 degree grid.  
We used CYGNSS L1 data as the lowest level of the available data products. The format of the data is NetCDF (Network Common Data Form). Daily observations of each of the eight CYGNSS satellites are included in a NetCDF file. Accordingly, there are up to eight files for every Day Of a Year (DOY). The daily base data is available free of charge on the website of Physical Oceanography Distributed Active Archive Center (PO. DAAC) of NASA's Jet Propulsion Laboratory (JPL) at [[https://podaac.jpl.nasa.gov](https://podaac.jpl.nasa.gov)]([https://podaac.jpl.nasa.gov](https://podaac.jpl.nasa.gov)). Table 2 shows the main variables of the L1 data [24] which were used in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline ddm\_snr & Delay Doppler Map (DDM) signal-to-noise ratio, in dB \\ gps\_tx\_power\_db\_w & GPS transmit power, in dB. \\ rx\_to\_sp\_range & Distance between the CYGNSS spacecraft and the specular point, in meters. \\ tx\_to\_sp\_range & Distance between the GPS spacecraft and the specular point, in meters. \\ gps\_ant\_gain\_db\_i & GPS transmit antenna gain. Antenna gain in the direction of the specular point, in dBi \\ sp\_rx\_gain & Specular point Rx antenna gain. The receive antenna gains in the direction of the specular \\  & point, in dechel isotropic (dBi). \\ quality\_flags & Per-DDM quality flags \\ sp\_lat & Specular point latitude, in degrees North \\ sp\_lon & Specular point longitude, in degrees East \\ sp\_inc\_angle & The specular point incidence angle, in degrees \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Cyclone Global Navigation Satellite System (CYGNSS) data source parameters.  
Figure 3: Rate of torrential precipitation in the Sistan and Baluchestan province over the period of six days from 8 January to 13 January, 2020. The maps were generated using the data provided by the authors of [37].
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 4 Discussion  
The application of the novel graph-based algorithm for segmenting retinal layers in spectral-domain Optical Coherence Tomography (SD-OCT) images represents a significant advancement in the field of ophthalmic imaging. This study highlights the algorithm's efficacy, especially when different denoising techniques are applied prior to segmentation. The comparative analysis between wavelet-based denoising and Gaussian filter-based denoising reveals important insights into the trade-offs between accuracy and computational efficiency.
# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 1 Introduction
Section 3 presents methods and techniques used to retrieve irrigation information from Earth observation. Sectionreviews irrigation mapping studies that are based on modeling and data assimilation. Section 5 discusses user needs and the degree to which current irrigation information retrieved from satellites (and models) meet them. Section 6 provides a synthesis of the review results and future perspectives and can guide the reader to disentangle among the huge number of studies in the field by listing the main results and conclusions (the reader interested in a general overview of the topic can skip directly to this section).
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 3 LIMS System: fs-Laser Ablation Ion Source and Time-of-Flight Mass Analyser
### Femtosecond Laser Radiation
#### 3.1.4 Optical Arrangement of Laser Ablation Ion Source
The laser fluence was sufficient to ablate, atomise and ionise the sample.
# Picosecond Q-Switched 1064/532 nm Laser in Tattoo Removal: Our Single Center Experience
###### Abstract  
Background: Tattoo removal is becoming increasingly popular, and Q-switched lasers represent the gold standard in the treatment of this condition. In this study, we report our experience with a new Q-switched picosecond laser device, evaluating its effectiveness and safety. Methods: A total of 34 patients asking for fattoo removal were consecutively enrolled in this open study. The clinicians decided on operating settings based on the Fitzpatrick phototype, the type of fattoo, and the fattoo location. A maximum of seven sessions, with a minimum interval of eight weeks between each session, were performed. At the six month follow-up visit following the last treatment session, patient satisfaction was assessed using a visual analogue scale and two dermatologists evaluated the aesthetic outcome based on pictures taken before and after treatment. Results: A total of 34 patients were included and analyzed: 17 females (50%) and 17 males (50%). The mean patient age was 43.6 \(\pm\) 11 years. Participants' Fitzpatrick skin type ranged from II to IV. The mean number of treatment sessions performed was 3.3 \(\pm\) 2.0 per patient. Over 40% of patients showed complete removal of the fattoo, with most of the patients indicating satisfaction with the treatment. Conclusions: The Q-switched 1064/532 nm laser may be considered the gold standard treatment for fattoo removal. Picosecond pulses seem to guarantee fewer sessions and excellent results when compared to other laser systems in fattoo removal.  
p 2021 11 2021 14 October 2021 18 October 2021 11 September 2021 14 October 2021 18 October 2021 11 September 2021 14 October 2021 18 October 2021 11 September 2021 14 October 2021 18 October 2021 11 September 20  
may represent a better option [11]. Nd YAG lasers, acting at a 1064/532 nm wavelength, effectively manage this condition [12].  
Furthermore, recent reports indicate that lasers releasing a huge quantity of energy in a fraction of a second may act specifically on tattoos, leaving the adjacent skin unarmed [13]. Picosecond pulses seem to guarantee higher effectiveness than longer nanosecond pulses, thus reducing the number of treatment sessions and improving cosmetic outcomes [14,15]. This paper estimates the safeness and efficacy of tattoo removal with a picosecond Q-switched 1064/532 nm laser.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 5 Results  
In this section, we describe the experiments that we conducted to verify the effectiveness of the query rewriting rules implemented. The purpose is to verify the extent to which the proposed incremental query mechanism with and without the rewriting rules will result in a significant performance gain to the overall query evaluation. More specifically, in our experiments, first we compared our incremental approach with the batch processing approach to show the benefits that we can get for continuous queries when evaluated incrementally to avoiding unnecessary query evaluations. We also present further performance gains achieved through our proposed rewritings.