esa-sceva/satcom-chunk-collection huggingface dataset:
# Cloud-Based Single-Frequency Snapshot RTK Positioning
###### Abstract  
With great potential for being applied to Internet of Things (IoT) applications, the concept of cloud-based Snapshot Real Time Kinematics (SRTK) was proposed and its feasibility under zero-baseline configuration was confirmed recently by the authors. This article first introduces the general workflow of the SRTK engine, as well as a discussion on the challenges of achieving an SRTK fix using actual snapshot data. This work also describes a novel solution to ensure a nanosecond level absolute timing accuracy in order to compute highly precise satellite coordinates, which is required for SRTK. Parameters such as signal bandwidth, integration time and baseline distances have an impact on the SRTK performance. To characterize this impact, different combinations of these settings are analyzed through experimental tests. The results show that the use of higher signal bandwidths and longer integration times result in higher SRTK fix rates, while the more significant impact on the performance comes from the baseline distance. The results also show that the SRTK fix rate can reach more than 93% by using snapshots with a data size as small as 255 kB. The positioning accuracy is at centimeter level when phase ambiguities are resolved at a baseline distance less or equal to 15 km.  
snapshot RTK; cloud-based positioning; coarse time navigation; satellite-based navigation; precise positioning; integer ambiguity resolution +
Footnote †: journal: Sensors
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 6 System Testing and Applications  
### System Latency Testing  
To evaluate the end-to-end delay from sensor data acquisition to monitoring platform reception, multi-dimensional measurement methods combined with real-time data capture techniques are employed. First, the timestamp synchronization method records the sensor acquisition time \(T_{1}\) and platform reception time \(T_{2}\), with single-trial delay defined as \(\triangle T=T_{2}-T_{1}\). Statistical characteristics are calculated through 100 consecutive samples. Supplementary ICMP round-trip time (RTT) testing analyzes 4G network transmission delay, where one-way delay is taken as half of the RTT. A pressure testing method is also used to simulate 3\(\times\) normal data traffic and verify delay stability under high load.  
The experimental results show that the system achieves an average delay of 215 ms under no-load conditions, meeting the real-time requirement (<500 ms) for highway slope monitoring and early warning. Although delay significantly increases under load scenarios, it remains within the early warning threshold. The proposed methods provide a quantitative basis for evaluating and optimizing the system's real-time performance. The specific measurement dimensions, methods, instruments, and delay results are shown in Table 4.
## 3 Experimental Results and Analysis  
### Experimental Setup for Object Detection  
The experimental setup for object detection in this study was as follows: an Nvidia GeForce RTX 2080 Ti GPU with 16 GB of memory running on the Windows 10 operating system. The programming language used was Python 3.8.18, with CUDA version 11.3. The YOLOv8n model was implemented using the Ultralytics library (version 8.1.9). The initial learning rate was set to 0.01, with three warm-up epochs. Data augmentation was disabled during the final 10 training epochs. The detailed training parameters are summarized in Table 4.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 6 Performance Evaluation  
In this section, we first describe and compare ED-MAC with UWAN-MAC [17] and T-Lohi [41] protocols qualitatively. We then discuss the implementation of our protocol, ED-MAC, in the Aqua-Sim underwater simulation [49]. We next specify the metrics used in our performance study to evaluate the performance of ED-MAC protocol against those of two protocols from the same category recently reported in the literature. We finally present and analyse the simulation results.
# Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
## 2 Methodology
### Dataset  
In this study, the classification experiments are conducted based on the APTOS 2019 dataset, downloaded from the kaggle website [17]. The dataset is provided by Aravind Eye Hospital in India, and includes 3662 images of a high resolution with each sample attached, diagnosed by highly trained doctors. According to the diagnosis, these images have been divided into 5 grades from 0 to 4 to present the severity level of diabetic retinopathy, where grade 0 includes 1805 normal images, indicating no diabetic retinopathy; grade 1, includes 370 images, indicating mild no-proliferative diabetic retinopathy; grade 2 includes 999 images, indicating moderate no-proliferative; grade 3, which includes 193 images, showing severe no-proliferative diabetic retinopathy, and grade 4 with 295 images of proliferative diabetic retinopathy.  
It is evident that the distribution of the 5 grades is severely uneven, which will be further discussed below. Furthermore, Figure 2 shows the retinal images with grades ranging from 0 to 4.  
Figure 1: The pipeline of the DR diagnostic model.
## 5 Simulation Models  
Simulation models are developed to evaluate the proposed approaches compared to the cases of pure MTO and MTS, using discrete event models. The flow line tested consists of three production stages, and each stage includes two identical parallel machines. To test and highlight the only effect of the proposed method, the three production stages have the same processing time, which follows the normal distribution with mean 25 and standard deviation of 2.5, and one type of product is considered. The focus of the research is the comparison between the proposed model and the two benchmarks (MTO and MTS systems), and the data are then used to evaluate different conditions.  
The models are tested for three inter-arrival orders with exponential parameters 12, 13, and 14 to evaluate different inter-arrival order levels. The simulations have been conducted for different buffer sizes from 1 to 5 with a step of one. In the case of periodic review, the three periodic time points considered are 100, 50, and 25. From the customer point of view, the waiting time is assigned as \(\text{Tnow}\) + 25 \(\ast\) UNIFORM [3; 5].  
Considering the MTS (5 \(\times\) 3 cases), MTO (3 cases), dynamic model under periodic review (5 \(\times\) 3 \(\times\) 3 cases), and dynamic model under continuous review (5 \(\times\) 3 cases), the total cases simulated are 78 cases.  
The performance measures considered to evaluate the proposed approaches are the following:  
Total customer orders satisfied over the simulation horizon (throughput).  
Total tardiness of the customer orders over the simulation horizon (tardiness). This index is an indicator of the costs of penalty for the orders delivered in delay.  
Total earliness of the customer orders over the simulation horizon (earliness). This index is used to support the dynamic decoupling policy.  
The average time that the customers wait for the orders (average time in system).  
The standard deviation of the average waiting time of the customers (st. dev.).  
The average work in process of the manufacturing systems (WIP). This is an index of the costs of the items in the buffers of the manufacturing system.
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## 2 Preliminaries
### Contributions  
In this paper, we introduce results for the approximation of the spectrum of structured graphs. We focus on families of graphs that not only have good combinatorial properties allowing for a _good_ and _efficient_ approximation of their spectrum but are also relevant for practical applications such as spectral sums, e.g., log determinant, trace of inverse, or spectral histogram approximation. The major contributions of this work are the following:  
* We provide an overview of families of graphs that accept good graph separators, i.e., graphs that can be separated into roughly equal subgraphs. Also, we discuss the characteristics of such families and how they relate to practical applications.
* Restricting ourselves to such graph families with good recursive graph separators, we propose a hierarchical decomposition for such families of graphs for approximate graph spectrum estimation.
* We propose a conquer mechanism to _stitch back together_ the pieces of the hierarchical decomposition for approximate graph mode estimation.
* We provide a theoretical analysis, leveraging current results on graph spectrum similarity, for the proposed hierarchical decomposition. We derive asymptotic bounds for both the accuracy of the graph spectrum approximation and the computational complexity of the proposed method using the hierarchical decomposition. Through numerical simulations, we show that in practice the approximation of the graph spectrum, using such a decomposition, obeys the derived bounds.
* We employ our results to applications commonly found in the field of graph signal processing. In particular, we derive bounds for the accuracy of approximate graph filtering using the proposed decomposition for the considered families of graphs. Despite that this result shows that a straightforward application of decomposition does not provide an efficient approximation, it sheds light on the reach of such an approach. In addition, we show that the cumulative spectral density of a given graph, commonly employed for the design of graph filter banks, can be properly approximated using the proposed decomposition. This differs from other approaches used in practice which do not provide any approximation guarantees.
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 5 Lowering Footprint  
There is a significant lateral mismatch between a surface grating coupler patch which is a multimode waveguide about 10-11 \(\upmu\)m wide and that of a single mode Si waveguide (about 500 nm wide). The simplest way of addressing this mismatch is by way of incorporating an adiabatic linear taper between the grating patch and the wire waveguide. However, near perfect mode conversion would entail a taper of 700 \(\upmu\)m length that would significantly increase device footprint. In the following section we shall discuss two ways of addressing the footprint problem i.e., by using either focusing gratings or compact tapers.
## 4 Design and Analysis of Magnetic Fields
### Simplified Model of the Magnetic Circuit
248-251), the relationship between the magnetic field intensity \(H(I)\) of the working gap of the MRF and the current \(I\) of the coil is  
\[H(I)=\frac{NI}{\pi\eta\mu_{0}\mu_{r}R_{\text{M}}\big{(}r_{2}^{2}-r_{3}^{2} \big{)}} \tag{33}\]  
where \(N\) is the number of turns of the excitation coil and \(\mu_{0}\) is the vacuum permeability. Taking \(\mu_{0}=4\pi\times 10^{-7}\) N/A\({}^{2}\), \(\mu_{r}\) is the relative permeability of the MRF, which is related to the volume fraction, diameter size and temperature of the magnetic powder particles in the MRF, and which can be measured experimentally [21,22]. Combined with Equations (19) and (33), the change to the output angular speed is positively correlated with the increase to the current when the load torque is constant, and the MR drive output speed is at its maximum when the current increases to the point of magnetic saturation of the MRF [23]. Define the distance between the yielded and unyielded partition interface of the magnetorheological fluid to the input disk as the yielded thickness \(h_{\tau}\). The working gap thickness of MRF \(h\) is set to 1.0 mm, and the maximum transferable torque is 130 N\(\cdot\)m. From Equation (13), it can be known that when the transmission is at full load and the speed difference \(\Delta\omega\) is 0 rad/s, the required minimum MRF shear yield stress \(\tau_{\text{min}}\) can be expressed as  
\[\tau_{\text{min}}=\frac{3T_{L}}{2\pi\big{(}r_{2}^{3}-r_{3}^{3}\big{)}} \tag{34}\]  
when the MRF is in a non-yielding state, \(z_{y}=0\), \(\tau_{\text{min}}=37.54\) kPa. Therefore, when the magnetic shear yield stress is 38 kPa, the load torque \(T_{L}\) and the yield thickness of MRF \(h_{\tau}\) are shown in Figure 5.  
As can be seen from Figure 5, when the load torque is 136.1-138.2 N\(\cdot\)m, the MRF transitions from a rigid-plastic flow state to shear yield flow state, and the yield thickness of the MRF multiplies from 0.18 mm to 1 mm; the MRF is wholly in shear yield at \(T_{L}=138.2\) N\(\cdot\)m.  
Figure 5: Relationship between yield thickness and load torque. The partial yielding flow of the MRF occurs in a narrow range as a transition between the unyielding and fully yielding states.
## 3 Results and Discussion
### Identification of AFB\({}_{1}\) Degradation
[PERSON] and [PERSON] [42] reported that _Aspergillus niger_, _Mucor ambigius_, and _Trichoderma viride_ could reduce the carbonyl group of 2-cyclopentanone at the toxic site of AFB\({}_{1}\) to form AFL. In addition, the structures and cleavage pathways of nine fragment ions were identified for the degradation molecule P6, and the cleavage pathways of fragment ion \(m/z\) 231.1015 and fragment ion \(m/z\) 217.0857 are shown in Figure 3. P6. The degradation molecule P6 was identified as aflatoxin D\({}_{1}\), and the cleavage pathways of the fragment ions of the other four degradation molecules were mainly related to charge resonance, H rearrangement, and RDA. The degradation molecule P8 was identified as aflatoxin D\({}_{2}\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Compound**} & **Retention** & \multirow{2}{*}{**Formula**} & \multirow{2}{*}{**Adduct**} & **Theoretical** & **Measured** & **Mass** & & \\  & **Time** & & **Value of the** & **Value of the** & **Value of the** & **Error** & **MS/MS Fragment** \\  & **(min)** & & & **Adduct (m/z)** & **Adduct (m/z)** & **(ppm)** & & \\ \hline \multirow{4}{*}{P1} & \multirow{4}{*}{32.71} & \multirow{4}{*}{C\({}_{19}\)H\({}_{18}\)O\({}_{7}\)} & \multirow{4}{*}{[M+H]\({}^{*}\)} & \multirow{4}{*}{359.1125} & \multirow{4}{*}{359.1117} & \multirow{4}{*}{\(-\)2.228} & \multirow{4}{*}{228} & \multirow{4}{*}{340.2591, 322.2438, 313.0702, 287.0910, 259.0959, 245.0804, 227.0703, 206.0575, 179.0700, 151.0757, 137.0606, 123.0808, 109.0921, 950.5054, 90.0345, 95.0189 & \multirow{4}{*}{**MS/MS Fragment**} \\  & & & & & & 252.8723, 226.8932, 208.8886, 180.8886, 161.0797, 143.0700, 133.0858, 114.0915, 96.0801, 89.6000, 884.0812, 886.075, 270.0519, 260.0736, 257.0802, 226.8934, 212.0704, 201.1230, 159.0440, 121.0651, 910.4051, 910.4051, 87.0445, 84.0811, 61.0114 \\  & & & & & 271.0954, 256.263, 243.2419, 227.1754, 271.0594, 271.0594, 270.0914, 270.0914, 270.0954, 270.0912, 271.0594, 271.0594, 270.0912, 272.1754, 74.0971, 270.0706, 270.0914, 270.0971, 270.0706, 271.0594, 270.0971, 270.0706, 2
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Applications
#### 4.3.4 Meta-Skin FSSs  
In recent times, liquid metal has been incorporated as active components to realize the stretchable and flexible electronic devices. When the liquid metal is injected into a pattern, it can be shaped in form of stretchable devices, such as electrical interconnects, probes, antennas, artificial skin sensors, micro-electrodes based micro-fluidic devices, and switchable MM micro-fluidic structures [182].  
Conventional stealth technologies are used to reduce the backscattering phenomenon (power reflected to probing radar), in contrast, recently, a different meta-skin technology has been introduced to fill the space between the single stretchable meta-atom to large-scale MMs [183]. The possibility of tuning the resonance of the planar MMs and cloaking effect, the curved MMs is being investigated. In [182], a meta-skin with properties of flexibility and stretchability at microwave regime is presented along with its frequency selectivity and cloaking effects. An array of liquid metal split ring resonator is encased inside an Elastomer. A high performance tunable FSS with a wide tuning range is demonstrated by stretching the multiple layers of meta-skins alongside their surfaces in a planar direction and also by altering the distance between the meta-skin sheet in a vertical direction, as shown in Figure 21a. More importantly, it can be wrapped around an object with an arbitrary shape (e.g., curved dielectric material) and a considerable scattering suppression (cloaking effect) is examined along different directions in a designed frequency band through experiment.
# Indoor-Guided Navigation for People Who Are Blind: Crowdsourcing for Route Mapping and Assistance
## 4 Bringing Forth Current R&D Challenges
### Crowdsourcing for Route Mapping  
We use the state-of-the-art Visual SLAM algorithm Orb-slam-3 [31] for creating the trajectories that are later used to guide visually impaired users. We use camera poses returned by the SLAM algorithm as the points along the trajectory, see Figure 7. Loop closing implemented within the SLAM algorithm ensures that trajectory drifts are corrected once the volunteers visit the places where they have been before. The recorded trajectories are overlaid on the 2D building floor plans and navigational instructions are built, see Figure 8.  
Figure 7: Point of clouds (see on the right) and route of such clouds (see on the left) generated with the SLAM algorithm and depicted from the screen.  
Seeing the recorded trajectories on the 2D plan the volunteers can verify that all of the places in the building have been visited. The redundant trajectories are generated when the volunteer returns to the place that has been visited before. This process is very important for loop closing and is part of the reliable workflow. On the other hand, this will generate redundant trajectories close to each other. The generated trajectories are converted to a pose graph and we use post processing to merge graph nodes and edges that are spatially close to each other. The processed graph is then used for path planning. At the moment we do not have any mechanism to remove faulty parts of the trajectory graph. If the volunteer sees that created graph has some faulty trajectories, they have to rerecord the floor map again to obtain the correct pose graph. In the future, we plan to add a GUI tool that would allow the volunteer to modify the trajectories after they have been recorded.
## 4 Results
### Testing Processes  
#### 4.2.1 Robustness and Accuracy with Different Learning Rates  
Firstly, we tested the robustness of the method with different learning rates. Nine different learning rates were applied to both the supervised learning (SL) and supervised contrastive learning (SCL). Details of the validation results are shown in Table 2 and Figure 4. As shown in Table 2 and Figure 4, the standard deviation of the SL was 4.11, while that of the SCL was 0.15. Therefore, the robustness of the SCL with different learning rates was better than the SL. Moreover, the mean accuracy of the SL was 80.79, while that of the SCL was 86.64. The SCL was more stable and had better performance than the SL with different learning rates. Additionally, other learning rates such as 0.3, 0.5, or 0.00001 were also tested. However, the models had either overfitting or underfitting. Therefore, the results of these learning rates were not considered in robustness measurement. The receiver operator characteristic (ROC) curve was plotted to comprehensively evaluate the performance of these classification models. The ROC curve showed the tradeoff between the sensitivity (true positive rate) and the specificity (1-false positive rate). The classification model that had a curve closer to the top-left corner indicated a better performance. In order to compare the different classification models, the area under the ROC curve (AUC), which summarizes the performance of curve into a single value, was proposed. The results of the ROC curves and AUC values in Figure 5 indicate that the classification model with the SCL was more robust and powerful than the classification model with the SL for different learning rates. For a given threshold, the sensitivity and specificity of the model can be calculated. The sensitivity of the SL for different learning rates was 76.25% \(\pm\) 10.09%, while that for the SCL, for different learning rates, was 86.24% \(\pm\) 0.56%. The specificity of the SL for different learning rates was 76.42% \(\pm\) 9.48%, while that for the SCL, for different learning rates, was 88.00% \(\pm\) 1.32%.  
Figure 3: Flow chart of the diabetic retinopathy classification model based on the contrastive learning method.
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 5 Conclusions
This research merely establishes a model to quickly obtain the moisture content of long-grain rice in drying sites, providing a relatively new approach to grain moisture detection. This article has the following potential impacts on the grain industry:  
(1) Improve quality control during storage and transportation of grains: accurate moisture measurement is crucial for controlling grain quality, preventing mold growth, and extending shelf life. (2) Optimize the drying process: it can help grain warehouse managers or farmers to dry grains more quickly and effectively, saving energy and reducing the risks of over-drying or under-drying. (3) Increase grain processing efficiency: The moisture content of grains affects processing quality and costs. A faster and more accurate detection method can optimize the processing flow and improve the quality of the final products. (4) Promote industry automation: it helps simplify the moisture measurement process, providing automation for the grain industry, thereby reducing labor costs and improving efficiency. In future work, in terms of parameter optimization, it may be beneficial to explore a broader or more detailed parameter space and experiment with different search strategies (such as random search or Bayesian optimization) to potentially find more optimal parameter combinations. In model selection, trying out different kernel functions could be considered to see if there are better choices for the current dataset. For data processing, using data augmentation techniques (such as adding noise to data or utilizing data generation techniques) to increase the diversity of training data can help improve the model's generalization ability. In evaluation methods, considering other performance metrics (such as model computational efficiency, memory usage, etc.) as optimization objectives could strike a balance between performance and efficiency. Conceptualization, W.C. and G.L. ; methodology, G.L. ; software, G.L. and B.Q. ; validation, H.S., B.Q. and Z.L. ; investigation, Z.L. ; resources, H.S. ; writing--original draft preparation, G.L. ; writing--review and editing, W.C.; supervision, W.C. All authors have read and agreed to the published version of the manuscript. This work is supported by the Education Department of Jilin Province for its financial support through grant number JIKH20220757 CY, and the Jilin Province Science and Technology Development Project for the grant number 20220204138 YY. The data presented in this study are available in article. The authors declare no conflicts of interest.