esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Review and Related Work
### Other Works Using Spatial Side Information  
In [200], RSS fingerprinting-based positioning is discussed, implemented using a CNN and an input image which preserves the topology of the access points in 2D. The approach proposed in [200] is related to our work through the use of spatial side information; however, it is considered for the access points generating the fingerprints.  
In [201], spatial side information is used to generate position coordinates along a path, navigating from a previously created map of clusters, with pre-calculated transition probabilities. Each position's coordinates on the path generated have an RSS fingerprint associated with them, thus defining a time-series of fingerprints. The time-series of fingerprints is used to train an LSTM NN to estimate the position coordinates for the input path provided.  
In [202], a circular antenna array operating in downlink is mounted into a vehicle for 2D positioning. Fingerprinting is performed through a downlink azimuthal-delay representation of the wireless channel.  
In [199], RSS fingerprints are collected by moving a positioning device along a path using a hybrid positioning system. The collection of the fingerprints is assisted by side information from an accelerometer and a gyroscope. This auxiliary system provides side information in 2D about the traveled distance and direction. When the trajectory followedis in 2D, the area covered is a small portion of the whole positioning area. Then, a matrix of fingerprints is associated with this small area. The elements in the matrix are associated with the discretization of the small area in the form of a 2D grid of 1 m \(\times\) 1 m squares. The approach proposed in [199] is aligned with ours in terms of gathering fingerprints in 2D; however, it differs in the collection method. In [199], the fingerprints are collected sequentially, with a granularity of 1 m, and in some cases following trajectories that cannot gather side information in 2D. The pattern-matching method proposed is based on finding the best correlation of the measured fingerprints to the stored in a radio map.  
In general, we may think that any implementation using a receiver with more than one antenna can be repurposed to exploit spatial side information if independent fingerprints can be extracted from each antenna. So, from one perspective, any receiver with an antenna array can be viewed as a simplified example of the proposed 2D sensor array. However, in the literature reviewed for the downlink case, either (1) linear arrays are considered, suggesting the use of spatial side information in 1D, as, for example, conducted in [203; 204] and the other works listed in Table 3, or (2) the geometry of the antenna array is not discussed. Furthermore, the antenna arrays used or proposed for positioning in the literature are not benchmarked against a different antenna arrangement or type to evaluate how different antenna arrangements affect the positioning performance.
## 5 Research Challenges and Future Directions
### Hardware Constraints  
Many kinds of communication systems, including frequency bands, network topologies, and service delivery, will need to work together to implement 6G [164]. Hardware configurations for access points and mobile terminals will also vary greatly. The transition from 5G to 6G will include an update to the massive MIMO technology, which might need a more intricate network design. This adds another layer of complexity to the design of the underlying communication protocol and algorithms. However, AI and machine learning will be incorporated into communication [165]. Furthermore, many forms of communication devices have diverse structural designs. Hardware implementation difficulties might also arise from using unsupervised and reinforcement learning [166]. As a result, bringing together disparate forms of communication into a unified system will be difficult. There is active research on power amplifiers with a frequency threshold of 500 GHz to 750 GHz, and developing future transceivers that can function in the THz range is a key problem for the industry [167]. Nonetheless, redesigning antennas to accommodate extraordinarily dense arrays is necessary because of the shortwave lengths involved.
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results
### Analysis of Error Variation with Epicenter Distance, SNR, Depth, and Magnitude
#### 3.4.3 Effect of Depth on Magnitude Estimation Accuracy  
Deeper earthquakes tend to produce longer seismic wave propagation times, leading to longer signal acquisition times, which may cause some signals to be ignored or misclassified. The overall error in the results of this paper converges gradually with the increase in depth (Figure 8). The error of large earthquakes is more concentrated within 70 km, stabilized in the distribution of the negative error region. Some events with more significant errors occur at a depth of about 25 km. Analyzing the error distribution at 1 s, the estimation value of large earthquakes is significantly lower than the overall estimation average at a depth of about 13 km, the error is more concentrated (there may be a significant fault), and the most significant error reaches level 4 at a depth of 25 km, which indicates that the distribution of large earthquakes is more extensive. The estimation accuracy is lower (there may be a low-velocity layer).  
Figure 6: Error variation with epicenter distance at 1–20 s (all events in gray; events larger than magnitude 6 in orange).
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 4 Experimental Setup, Procedure, Results, and Analysis  
### Experiment Parameter  
Figure 8 shows the experimental device for mini-crack detection of conveyor belt installed in the laboratory. The conveyor belt consists of three layers, with a width of 500 mm and a thickness of 20 mm for each layer. For the rubber material, the longitudinal tensile strength is 1250 N/mm, wear rate is less than 90 mm\({}^{3}\)/(N\(\cdot\)m), the maximum running speed is 1 m/s, the running speed of this experiment is 0.05 m/s, and the maximum operating temperature is 40 \({}^{\circ}\)C, which is close to the actual mining conveyor belt.  
The laser thermography system mainly includes an infrared camera, a CW laser scanning device, a field programmable gate array (FPGA) embedded terminal, a wireless signal transmission device, a personal computer (PC), and monitoring software developed in the laboratory.  
The local excitation is realized by a -CW laser with a wavelength of 808 nm and an effective maximum power of 15 W. The point laser is converted into line laser through the collimator and projected onto the conveyor belt. The length and radius are 500 mm and 0.3 mm respectively of line laser on conveyor belt, and the maximum power density of 50 mW/mm\({}^{2}\). The focal length \(f\) of focusing lens is 8 mm. FPGA embedded terminal in our laser thermography system is used for image reconstruction. It could reduce great redundant information before the mini-crack feature image transmits to the personal computer. The temperature changes on the belt surface are recorded with an infrared camera (FLIR Tau 640) with 640 \(\times\) 480 pixels at a frame rate of 60 Hz and a minimal temperature resolution of about 50 mK, also be expressed as NETD (Noise Equivalent Temperature Difference) and the corresponding FOV becomes 625 \(\times\) 500 mm\({}^{2}\). The computer configuration is as follows: Inter(R) Xeon(R) CPU E5620@ 2.40 GHZ 2.40 GHZ, Memory 8 GB.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### Performance Metrics Analysis  
The \(\alpha\)-cut values from Tables 4 and 5 demonstrate how bulk service, single service, and vacation policies impact key performance indicators, including throughput (TH), waiting time (W), and system utilization (U).
## 2 Results and Discussion
### Method Validation  
In order to ensure the best chromatographic parameters and low detection and quantification limits, different wavelengths were chosen for each antibiotic, considering the full advantage of the multichannel capacity of the DAD detector. The multichannel DAD detector allows for the optimal detection and quantification of compounds that would be impossible to be analyzed in the same run over single wavelength detection. The optimal wavelength for Amox is 350 nm, ensuring a LOQ of 0.7 \(\upmu\)g/mL. The peak corresponding to Dox is visible at this wavelength, but the LOQ is more than 20 times higher than that obtained at the optimal wavelength of Dox (230 nm). Therefore, an intermediate wavelength should have been chosen for an even response from bought compounds in the same run on a single wavelength, resulting in a method with lower chromatographic parameters, and a higher limit of detection (LOD) and limit of quantification (LOQ). Consequently, two different wavelengths (230 and 350 nm) were selected to monitor Amox (retention time at 1.70 min) and Dox (retention time at 5.23 min) (Figure 2).  
The proposed method for the simultaneous determination of Amox and Dox in pharmaceutical formulations and wastewater samples was validated relating to the conformity  
Figure 2: HPLC chromatograms of Amox and Dox recorded at two different wavelengths: (**a**) 350 nm and (**b**) 230 nm, at 100 \(\upmu\)g/mL.  
of the chromatographic parameters (tailing factor, resolution, peak symmetry, theoretical plates), LOD, LOQ, working and linear ranges, precision, and recovery, according to current guidelines and previously published papers [41; 42; 43; 44; 45; 46; 47].
## 4 Results' Analysis
### Results' Evolution  
As previously stated, the daily product requirements were collected for 90 days, while the optimization only considered 60. This means that the ideal stocks could be calculated for 30 days to observe the temporal evolution of the ideal stocks. The optimizations ran for each of the rolling forward periods and were run 10 times each. Figure 4 shows the 30 Pareto fronts of risk against energy. It is important to mention that for each day, only one version is shown: the one whose median values of the three objectives had the smallest Euclidean distance to the origin. Note that the Pareto curves shown are smoothed approximations to reduce the visual clutter and allow an easier analysis.  
Figure 4: Approximated Pareto fronts for the optimizations with the lowest median objectives of each day of the rolling-forward schedule. The Pareto front’s lines are smoothed for easier comprehension.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Storage** & \multicolumn{2}{c}{**Solution Index**} \\
**Location** & **4** & **14** \\ \hline Cold & \(7.67\times 10^{5}\) & \(7.55\times 10^{5}\) \\ \hline Tanks & \(6.93\times 10^{1}\) & \(2.19\times 10^{1}\) \\ Warehouse & \(7.37\times 10^{6}\) & \(7.03\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Total inventory units at each storage location (tanks’ inventory levels are aggregated) for each of the optimal solutions tested.  
The figure shows that the Pareto fronts at the initial days tended to be slightly worst than the average (in regards to their distance to the origin), worsening their values until the 10 th day. The Pareto fronts then moved closer to the origin, with the closest results around the 20 th day. Finally, the fronts degraded slightly, ending on the 30 th day slightly closer to the origin than on the first day. The distance to the origin broadly indicated the material requirements--Pareto fronts closer to the origin tended to regard production schedules with larger material requirements.  
While there were some variations along the month, the stocks did not change extensively. This was a desirable behaviour, as the changes in the stock requirements from one day to the next were not very substantial--there were only changes in one day out of the 60 days considered, since when \(t\) changed to \(t+1\), the material requirements changed from \(N_{i,t+60}\) to \(N_{i,t+1:1:t+61}\). Nevertheless, the figure shows that the Pareto fronts were further from the origin in the initial days, then improved until around \(t=20\) and ended at a reasonable distance. A smaller distance of one front to the origin meant that both objectives had better values than the one farther from the origin--this means that the problem's conditions changed sufficiently between those two scenarios, and the stock requirements in the front farther from the original were larger than the ones from the closer front.
# Effect of Injecting Epoxy Resin Adhesive into Cement Mortar on Tile Adhesion Performance
###### Abstract  
Large porcelain tiles have attracted increased demand owing to their cost-effectiveness and superior esthetics. Here, an epoxy resin adhesive was injected into cement mortar, which was then applied to tiles. The adhesion performance of the tiles was subsequently evaluated in terms of the permeability and drying shrinkage under various curing conditions. The epoxy resin adhesive not only penetrated the tile-mortar and mortar-concrete interfaces, but also directly penetrated the mortar, thus enhancing the mechanical adhesion at each interface. In addition to the mechanical adhesion between the tiles and mortar, the epoxy resin adhesive prevents the degradation of adhesion due to shear stress by minimizing the moisture evaporation and shrinkage of the mortar. Evaluation of the adhesion characteristics under water and freeze-thaw curing conditions revealed the vulnerability of the epoxy resin to moisture; however, adequate adhesion performance was observed when the epoxy resin was air-cured prior to being exposed to harsh environments. Moreover, the injection method did not prolong the construction period, but could potentially reduce it during actual application. Nevertheless, further research on the adhesion performance of tiles with injected epoxy resin adhesive is required to evaluate the long-term durability.  
epoxy resin; injection; tile; cement mortar +
Footnote †: journal: Applied Sciences
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 5 Conclusions  
The Norwegian oil-on-water experiment in 2011 provided polarimetric SAR acquisition with verified oil spill and biogenic slicks on one scene of Radarsat-2 data. More quad-pol SAR data samples are being further collected to derive more detailed and convincing results in the near future studies.  
The key findings of this comparative study can be summarized as follows:  
* Polarimetric SAR features can be input into supervised algorithms to achieve reliable oil spill classification. For this dataset, a feature set with four features is sufficient for most polarimetric features based oil spill classifications. They are: pedestal height, correlation coefficient, standard deviation of CPD and alpha angle.
* Among all of the compact polarimetric SAR modes, \(\pi/2\) mode has the best performance among all of the dual- and compact polarimetric SAR modes, for its sensitivity to different scattering mechanisms caused by mineral oil and biogenic look-alikes.  
* Among all of the supervised classifiers, SVM outperforms other classifiers when sufficient polarimetric information can be obtained, such as quad-pol mode. ML performs better than other supervised classifiers when only incomplete polarimetric information is available, such as traditional dual-pol and \(\pi\)/4 mode.  
The reasons for the unreliable results in feature reduction experiments may be attributed to insufficient data sampling when computing feature maps. The understanding of oil in the characteristics of polarimetric SAR imagery is key to optimize the processing procedures of automatic oil spill detection and classification algorithms.  
In the near future, there will be more compact polarimetric SAR data available for marine surveillance applications. The polarimetric observation capabilities of these sensors will largely improve the efficiency and reliability of oil spill detection and any future classifications applications based on SAR data.  
The SAR data of Radarsat-2 is highly appreciated. This research is jointly supported the \"2015 Innovation Programs for Research and Entrepreneurship Teams in Jiangsu Province, China\", the National Key Research and Development Program of China (2016 YFC1402003) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
[PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] performed the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
# Diagnosing and Balancing Approaches of Bowed Rotating Systems: A Review
## 2 Materials and Methods
### Balancing/Correcting  
While the existence of some faults, such as a crack in the rotating system, means the defected component should be replaced, some other defects, such as unbalance, misalignment, and bowed shaft, if diagnosed early, can be resolved.  
Although both a bent shaft and an imbalance produce synchronous whirls, these movements and the consequent balancing procedures are different. Differences in the force effects created by these faults could be the cause of these discrepancies. The effect of the force produced by an initial bend does not change with shaft speed and is a function of the shaft stiffness matrix and the initial bow vector, while the effect of the force produced by an imbalance is proportional to the square of the angular speed of the shaft [65].  
For balancing a bowed rotor system, two main procedures have been used by scholars: influence coefficient and modal balancing method. In the following, the related investigations are referenced.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 1 Introduction
Our contributions can be summarized as follows:  
* The proposed scheme embeds the watermark information in a spread spectrum manner. It does not require a pre-training embedding network or a decoder structure, and therefore, the network is shown to be more lightweight. * The noisy feature representation of the latent vector is explored to hide the watermark message. Benefiting from the reversible property of diffusion models, the watermarked  
Figure 1: Image regeneration attack. image can be produced without compromising the perceptual quality. Additionally, the VAE encoder acts as a robust feature extractor, which also provides a sweet spot to accommodate the watermark. Furthermore, the diffusion operated on the lower dimensional vector yielded by the VAE-encoder is computationally efficient. * Experimental results show that our framework performs robustly under common attacks like JPEG compression, brightness adjustment, and blurring. More importantly, it is resistant to image regeneration and image editing.
## 2 Materials and Methods  
### Data Acquisition  
The dataset utilized in this study is a publicly accessible one sourced from Roboflow [16], featuring images captured from diverse real-world construction site scenarios. These images span from sparsely populated scenes to highly crowded ones and encompass various colors of safety helmets and reflective clothing. The initial dataset comprised 4311 images. To bolster the data and emulate potential challenges encountered with camera equipment in different construction sites, data augmentation techniques were employed. These encompassed random horizontal rotations of images, as well as adjustments to saturation, exposure, and brightness. Post-augmentation, the total image count swelled to 10,449.  
The dataset is categorized into four distinct classes: \"no helmet\", \"no jacket\", \"safe\", and \"unsafe\". The specific classification criteria are defined as follows: images displaying both a safety helmet and reflective clothing are designated as \"safe\"; those with reflective clothing but no safety helmet are labeled \"no helmet\"; images featuring a safety helmet but lacking reflective clothing are categorized as \"no jacket\"; and those devoid of both are marked as \"unsafe\". A visual reference for this classification system is presented in Figure 1.  
Figure 1: \(Cont.\)  
The dataset was partitioned into three sets: 9207 images for training, 464 for testing, and 778 for validation. While this dataset comprehensively covers most photographic types, it falls short in capturing images from nighttime environments, a limitation that will be addressed in future endeavors.
# The Role of Artificial Intelligence in Sports Analytics:
## 4 Discussion
### Data Quality, Granularity, and Ethical Constraints  
Beyond algorithmic design, the validity and fairness of AI systems in sports heavily depend on the underlying data. Studies included in this review varied widely in sample size--from datasets with fewer than 20 athletes to large-scale tracking logs involving thousands of player-hours. Small sample sizes or highly specialized populations (e.g., Olympic-level athletes) may limit model generalizability.  
Additionally, the capture frequency (e.g., 10 Hz vs. 100 Hz), sensor placement, and environmental context (lab vs. in-game) significantly influence data quality. High-frequency GPS or IMU data can enhance model precision, but also raise issues of data privacy, particularly when combined with biometric indicators.  
The lack of reporting standards regarding data resolution, preprocessing steps, and recording environments further hampers reproducibility. Ethical use of such detailed data--often collected without longitudinal consent frameworks--raises concerns around surveillance, autonomy, and data ownership in athlete populations. Future AI applications must not only prioritize performance, but also respect privacy and ensure informed use of sensitive data streams.
## 4 DASH Live Broadcast Traffic Model
### Time-Bound Leaky Bucket Traffic Model  
The leaky bucket receives the data sent from the token bucket and sends the data at a CBR. The proposed leaky bucket has a CBR service denoted as \(LC\). The leaky bucket size is denoted as \(LB\). The amount of data in the leaky bucket at time \(T_{i}\) is denoted as \(LB(i)\), and the amount of data received from the token bucket is denoted as \(R(i)\). Thus, the bandwidth capacity bound of the leaky bucket satisfies Equation (6):  
\[\sum_{i=1}^{C}MIN(R(i)+LB(i),\ LC)\leq LC\cdot C\cdot\rho. \tag{6}\]  
Equation (6) calculates the amount of data transmitted by the leaky bucket, transmitting data within the time limit. At this time, \(C\) indicates how many times data are received from the token bucket. There are data in the leaky bucket after transmitting. The amount of data remaining in the leaky bucket satisfies the following Equation (7):  
\[\sum_{i=1}^{C}(R(i)+LB(i)-\ LC\cdot\rho)\leq LB. \tag{7}\]  
Equation (7) reveals that the amount of data remaining in the leaky bucket is smaller than the leaky bucket size, \(LB\), after the data are transmitted. In addition, the amount of data \(LB(i+1)\) remaining in the leaky bucket at time \(T_{i+1}\) satisfies Equation (8):  
\[LB(i+1)=\max(0,\ R(i)+LB(i)-LC\cdot\rho). \tag{8}\]  
The delay is determined according to the amount of data \(LB(i+1)\) in the leaky bucket, and the maximum buffering delay of the leaky bucket can be obtained by \(LB/LC\).