esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 3 Storage and Access of Remote Sensing Data in MongoDB  
### The Distributed Cluster Architecture  
MongoDB is an open-source NoSQL database management system with a feasible schema and powerful scalability, which can provide excellent storage and access capability, especially in the large data management field. Aiming at massive remote sensing data, including unstructured image data and structured metadata, a distributed storage method is proposed in this paper based on MongoDB. This research establishes the distributed storage and access architecture for remote sensing data, which is composed of several physical machines, and there is no duplication of data among the cluster.  
To ensure the high availability and consistency of the remote sensing data, this research takes advantage of the strategy of \"replica sets + sharding\" to construct the cluster. That is to say, the shard is also a replica set and consists of a group of mongod processes, while mongod is a process that is mainly used to handle data requests and manage data storage in the MongoDB database. Moreover, as a special member of the replica set, when the backup nodes cannot connect to the primary node, the arbiter takes part in the election of the new primary node, which does not store data and occupies fewer resources. Figure 2 shows the distributed cluster architecture for remote sensing data.  
Figure 1: Overall architecture of the MongoDB sharding cluster.
## 1 Introduction
[17] achieved the low-cost sorting and cleaning of granular agricultural products in vibrational environments, enhancing the automation and intelligence of grain processing systems. However, in practical applications, the acquisition and analysis of vibration signals present several challenges. One significant issue is signal aliasing, which compromises the generalizability of extracted features across different categories, thereby impeding the construction of accurate classification models. Additionally, hardware malfunctionsand degradation can adversely affect signal transmission quality. Therefore, appropriate compensation and optimization strategies are necessary to maintain system reliability and performance. Building upon the aforementioned research, this study introduces a novel grain drying apparatus integrated with a vibration-based piezoelectric detection system. The proposed system not only improves upon the mechanical design of conventional crop seed dryers but also enables the real-time monitoring of moisture variation in grains, thereby facilitating precise control of the drying process. By analyzing the time-domain response signals resulting from interactions among seeds, impurities, and the drying tray--and accounting for discontinuities due to vibration or hardware-related interference in the communication link--the system derives the outage probability (OP) and a progressive closed-form solution for signal transmission. This is modeled under a Rayleigh fading channel using the shortest routing protocol (SRP) to enhance signal reliability and data integrity throughout the drying process.
# A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
## 5 Conclusions  
This paper presents a low-power 60 GHz BiCMOS transceiver fully compatible with the IEEE 802.11 ad wireless standard. In contrast to the traditional mode, the proposed transceiver allows FDD operation with \(-22.78\) dB and \(-20.97\) dB and FD operation with \(-18.7\) dB and \(-17.05\) dB EVM at a 0.8 m communication distance for 16-QAM and QPSK, respectively. The maximum data rate is 7.04 Gb/s for both the transmitter and receiver. The FD operation mode in the proposed system works only in \(\pi\)/2-BPSK without a DSIC and works in 16-QAM with a DSIC. Moreover, with the 4 GHz and 3 dB bandwidth of the I/Q baseband DVGA, together with the 6 GHz and 3 dB bandwidth of the IF amplifier, the transceiver can also support the operation of the two-channel bonding receiver defined in IEEE 802.11 ay with a doubled data rate of 14.08 Gb/s.  
Conceptualization, Y.W., and B.K.T.; methodology, Y.W., B.K.T., and N.M.; formal analysis, F.M., and Y.H.; investigation, Y.W., B.K.T., and N.M.; writing--original draft preparation, Y.W., and K.S.Y.; writing--review and editing, Y.W., B.K.T., N.M., K.M., and K.S.Y.; supervision, K.M.; project administration, K.S.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are contained within the article.  
This research was supported by the Singapore National Research Foundation, award number NRF-CRP20-2017-0003 and project number CRP20-2017-0006. The authors would also like to thank the Tower Jazz Semiconductors Inc., Newport Beach, CA, USA, for providing fabrication service of the design.  
The authors declare no conflicts of interest.
## 3 Results
### Performance Comparison with Dual-, Triple-, and Mixed Dual-/Triple-Frequency Processing
However, despite the third frequency having a limited impact on the float solutions, its impact on the fixed solutions is more noticeable. Adding a third frequency to the dual-frequency fixed solutions improves performance, especially when it comes to the convergence of the solution. At the 100 th percentile for example, not only does the horizontal convergence time decrease from 9 min to 7.5 min, but the initial errors decrease as well from 60 cm to 36 cm on the first epoch. The same levels of improvement are noticed at lower percentiles for the horizontal error, with the convergence time decreasing from 7 min to 5.5 min at the 95 th percentile, and from 3.5 min to 2 min at the 67 th percentile. The initial horizontal error decreases from 55 cm to 32 cm at the 95 th percentile, and from 38 cm to 20 cm at the 67 th percentile. The improvements in the fixed solution with the addition of the third frequency are attributed to the fixing becoming more reliable. Indeed, the ambiguities from the same satellite are correlated, which means that adding a third ambiguity for a satellite would lead to more reliable and accurate fixing of the ambiguities, as was similarly discussed in [33]. The impact of the third frequency on the ambiguity success rate for one of the kinematic datasets will be discussed in Figure 10.  
In terms of the vertical component, the addition of the third frequency appears to have a limited effect on both the float and fixed solutions. For the float solutions, millimetre-level differences are seen between dual-frequency and mixed dual-/triple-frequency solutions. As described above for the horizontal solutions, this behaviour is attributed to the limited information provided by the third frequency to the state estimation. The improvements from the third frequency come from the addition of another ambiguity to fix, which is correlated to the first and second frequency ambiguities. However, its effect is very limited for the vertical component. The vertical convergence time at the 100 th percentile decreases from 11.5 min to 11 min, while decreasing from 9 min to 8.5 min at the 95 th percentile, and from 4 min to 3.5 min at the 67 th percentile. The limited impact of AR on the vertical component's convergence was also noticed in [19; 34]. However, there are still improvements that can be noticed in the initial epochs, as the third frequency helps  
Figure 6: Histogram of number of satellites being processed in dual-frequency, triple-frequency, and mixed dual-/triple-frequency processing. Graph is based on all epochs from all datasets. “dual” and “dual/triple” histograms are overlapping. reduce the vertical errors before convergence. For instance, at the 100 th percentile, the dual-frequency solution's 124 cm initial error decreases to 90 cm with the dual-/triple-frequency solution. At the 95 th percentile, the initial error decreases from 109 cm to 78 cm, while it decreases at the 67 th percentile from 62 cm to 40 cm.
## 3 Results and Discussion  
### Microstructure Examination  
The microstructural micrograph of Al-20%Mg\({}_{2}\)Si-10% B\({}_{4}\)C hybrid composite is shown in Figure 1a. As seen, the composite structure consists of \(\alpha\)-Al as matrix and Mg\({}_{2}\)Si and B\({}_{4}\)C as reinforcement particles. The corresponding elemental mapping as well as the XRD patterns of Al-20%Mg\({}_{2}\)Si-10% B\({}_{4}\)C hybrid composite is illustrated in Figures 1b and 2 respectively, which indicate the existence of the Al, Mg\({}_{2}\)Si and B\({}_{4}\)C in the HMMC.  
Figure 3a-d exhibits the SEM micrographs of Al-20%Mg\({}_{2}\)Si composite added with different B4C concentrations. As seen in Figure 3a, in the composite without B4C particles (base), primary Mg\({}_{2}\)Si particles are large and irregular and often in polyhedral shape surrounded with eutectic Mg\({}_{2}\)Si cells. With the addition of 2.5 wt% B4C into the Al-Mg\({}_{2}\)Si composite, the morphology of primary Mg\({}_{2}\)Si is still in polyhedral shape, as shown in Figure 3b. However, when the composite was treated with 5 wt% B4C, the morphology of primary Mg\({}_{2}\)Si slightly altered to polygonal as depicted in Figure 3c. Moreover, B4C particles are distributed homogeneously throughout the aluminium matrix. With further  
Figure 1: (**a**) SEM image of Al-20%Mg\({}_{2}\)Si composite with B4C addition with (**b**) corresponding elemental mapping analysis.  
addition of B4C particles until 10 wt%, the Mg\({}_{2}\)Si particles retain the polygonal morphology; however, some agglomeration of B4C particles are observed in the composite area as illustrated in Figure 3d. Similar behavior of produced agglomeration as a result of increased B4C reinforcement content in Al composites was reported by other researchers [25; 26; 27]. This phenomenon can be attributed to the insignificant wettability of ceramic particles with aluminum melt as the particles float on top of the melt surface owing to the surface tension and high interfacial energy of the reinforcing particles [28]. Besides, the lower density of B4C particles compared to the aluminum causes the B4C particles to float and leads to the non-uniformed dispersion where the B4C particles agglomerate and segregate at a particular place in the melt.  
Figure 3: SEM images of Al-20%Mg\({}_{2}\)Si composite with various additions of B4C: (**a**) 0 wt%, (**b**) 2.5 wt %, (**c**) 5 wt%, and (**d**) 10 wt%.  
Figure 2: XRD results of Al-20%Mg\({}_{2}\)Si in situ composite added with 10 wt. % B4C.
## 3 FPGA-Based Optimization of Deployment
### Optimization of the Quantization Calibration Set for Infrared Images  
A calibration set is a dataset used to evaluate and adjust quantization parameters during the quantization process. Quantization requires measuring the distribution of each feature map to determine the appropriate quantization scaling factor. The function of the calibration set is to use a portion of the dataset to represent the entire dataset to measure the distribution range of each feature map and to count the input and output data range of each layer as a reference for the feature map quantization.  
The relative Euclidean distance and cosine similarity are used as evaluation indicators, which are defined as  
\[dist=\frac{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}}{\sum_{i=1}^{n}(A_{i})^{2}} \tag{8}\]  
Figure 10: Using sequential-differential acceleration to merge the improved differential layer and adjacent convolutional layer: (**a**) original differential and convolution operation; (**b**) combined operation using sequential-differential acceleration.  
\[cos=\frac{\sum_{i=1}^{n}(A_{i}\times B_{i})^{2}}{\sqrt{\sum_{i=1}^{n}(A_{i})^{2} \times\sqrt{\sum_{i=1}^{n}(B_{i})^{2}}}} \tag{9}\]  
where \(A_{i}\) represents the output value of each network layer before quantization, \(B_{i}\) represents the output value of each network layer after quantization, and n represents the number of network layers. The relative Euclidean distance and cosine similarity indicate the difference between the model before and after quantization, which can reflect the effect of model quantization. The smaller the relative Euclidean distance, the better the quantization effect. Similarly, the closer the cosine similarity is to 1, the better the quantization effect.  
The selection of a calibration set affects quantization accuracy. For general target detection tasks, the calibration set should meet the following basic requirements:  
* Representativeness: the calibration set should well represent the data distribution of the real scene images;
* Diversity: the calibration set should cover different types of input data as much as possible;
* Scale: the calibration set should have enough scale. The larger the scale, the better the quantization effect will be, but more memory and time will be required for the quantization process.  
Besides the above, for an infrared image under a sky cloud background, the background mainly contains various clouds. Therefore, it is necessary to optimize the selection criteria for infrared images when used as a quantization calibration set. We further explore the effects of the dynamic range, variance, scale, and batch size of infrared images on the quantization effect. In the next chapter, the experiment results are compared and analyzed in detail.
## 3 Dual-Site Micro-Scale Diversity Scheme  
### Experimental Data-Bank  
Two identical EML ARG 100 rain gauges [41] have been deployed at the NTUA Campus in Athens, Greece (37.97\({}^{\circ}\) N, 23.78\({}^{\circ}\) E) measuring rainfall at a separation distance of about 386 m; one of them is placed at the old buildings belonging to the School of Electrical and Engineering while the other one at the new ones. The rain gauges used are of tipping bucket type and are pre-calibrated with a measurement resolution of 0.2 mm of rain per tip; the sampling frequency is set at 1 Hz. Using the recorded rainfall data, rain rate time series in mm/h with 1-min integration time window have been calculated using the methodology presented in [38]. The rain rate data availability for both rain gauges has been 100.00% for the whole 4-year time period examined in this paper (June 2012-June 2016).  
In Figure 2, the obtained rain rate time series with 1-min integration time are presented for both locations for the whole 4-year period. In Figure 3, the Complementary Cumulative Distribution Function (CCDF) of rain rate for each of the locations as well as the joint-minimum CCDF for both sites at NTUA Campus are presented.
# Fractal Dimension and Retinal Pathology: A Meta-Analysis
## 2 Methods
### Inclusion/Exclusion Criteria  
The goal of this review is to examine the scientific evidence comprehensively for a wide variety of retinal disorders rather than a select few. The eligibility criteria was determined using the PICO framework [18]. The study populations comprised of participants of any age, race, or gender that had been diagnosed with a retinal disorder. Interventions consisted of calculation of fractal dimensions on processed retinal vasculature images of subjects. Preferably, studies would state the model of the imaging device used, the region of interest which fractal analysis is conducted over, fractal analysis software packages used, and the types of fractal parameters that were calculated. Finally, studies had to compare fractal dimension in case subjects versus control subjects or contrast fractal dimension with disease progression from baseline to follow-ups.  
Case-control studies, cross-sectional studies, cohort studies, and case series were included. Studies that employed the following imaging techniques were acceptable:  
* Digital fundus photography
* Fundus fluorescein angiography
* Optical coherence tomography angiography (OCTA)
* Scanning laser ophthalmoscopy (SLO)  
Reviews and other types of articles without original research were not included. No restrictions on submission date or language were placed.
# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 4 Experiments
### Experimental Results  
#### 4.3.1 Experimental Results: Experiment A  
Table 5 shows the comparison of standard transfer learning (STL) and Hebbian transfer learning (HTL) in terms of top-1 and top-5 validation accuracies for experiment A, as described in Section 4.2.1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Source** & **Target** & **STL** & **HTL** & **Improvement** \\ \hline \multirow{8}{*}{Top-1 accuracy} & \multirow{8}{*}{S} & T\({}_{1}\) & 59.6\% & 60.1\% & +0.50\% \\ \cline{3-6}  & & T\({}_{2}\) & 69.9\% & 71.7\% & +1.80\% \\ \cline{3-6}  & & T\({}_{3}\) & 65.0\% & 65.5\% & +0.50\% \\ \cline{3-6}  & & T\({}_{4}\) & 75.0\% & 75.8\% & +0.80\% \\ \cline{3-6}  & & T\({}_{5}\) & 67.9\% & 70.2\% & +2.30\% \\ \cline{3-6}  & & T\({}_{6}\) & 63.0\% & 63.8\% & +0.80\% \\ \cline{3-6}  & & T\({}_{7}\) & 57.0\% & 57.3\% & +0.30\% \\ \cline{3-6}  & & T\({}_{8}\) & 51.6\% & 54.2\% & **+2.60\%** \\ \cline{3-6}  & & T\({}_{9}\) & 73.7\% & 74.5\% & +0.80\% \\ \cline{3-6}  & & T\({}_{10}\) & 74.4\% & 75.9\% & +1.50\% \\ \hline Average & \multirow{8}{*}{S} & \multirow{8}{*}{T\({}_{1}\)} & **65.71\%** & **66.90\%** & **+1.19\%** \\ \cline{3-6}  & & T\({}_{1}\) & 94.7\% & 95.1\% & +0.40\% \\ \cline{3-6}  & & T\({}_{2}\) & 97.3\% & 97.9\% & +0.60\% \\ \cline{3-6}  & & T\({}_{3}\) & 94.9\% & 96.0\% & +1.10\% \\ \cline{3-6}  & & T\({}_{4}\) & 97.8\% & 98.0\% & +0.20\% \\ \cline{3-6}  & & T\({}_{5}\) & 95.0\% & 95.3\% & +0.30\% \\ \cline{3-6}  & & T\({}_{6}\) & 94.0\% & 94.4\% & +0.40\% \\ \cline{3-6}  & & T\({}_{7}\) & 91.0\% & 91.1\% & +0.10\% \\ \cline{3-6}  & & T\({}_{8}\) & 95.5\% & 96.4\% & +0.90\% \\ \cline{3-6}  & & T\({}_{9}\) & 99.0\% & 99.4\% & +0.40\% \\The results show that in all cases, the HTL outperforms STL. In particular, top-1 accuracy for target dataset T\({}_{8}\) shows lowest accuracy but highest improvement of +2.60% with HTL, which implies that the HTL is more effective for transfer learning between heterogeneous source and target.  
In average, the improvement of top-1 accuracy with HTL is +1.19%, and the improvement of top-5 accuracy with HTL is +0.48%.
## 4 Discussion
The implanted MSCs do not survive long at the implantation site but initiate the bone regeneration process by recruiting host MSCs and osteoprogenitors, resolving inflammation by modulating the immune response and initiating reparatory M2 macrophage polarization [50; 51]. MSCs also suppress Th1 and cytotoxic T cell proliferation and function, favoring Th2 and Treg cell polarization, inhibit NK cell cytotoxicity and induce tolerogenic DC generation. This immunosuppressive effect is crucial for terminating the inflammatory phase and moving to the regenerative phase, thereby avoiding chronic inflammation, which is detrimental to any tissue regeneration [44]. On the other hand, M2 macrophages, and osteoclasts generated by phagocytosis and creating an acidic environment, lead to the resorption of calcium phosphate materials and release of calcium and phosphate ions [52], which contribute to MSC osteogenic differentiation and mineralization [53]. Previous studies have shown that macrophage recruitment to an implanted calcium phosphate is significantly enhanced when the calcium phosphate scaffold is seeded with MSCs [44; 51; 54]. Based on these studies and our results, we can consider surface modification of \(\beta\)-TCP by CAP as a way to increase the adhesion and interaction of MSCs with the material, as well as the surrounding host tissue and cells, leading to the transition from a pro-inflammatory to a pro-reparatory microenvironment.
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 5 Conclusions  
This article proposed a reliable system for lung nodule detection and segmentation. The system contains two deep learning models. Firstly, the Optimized Faster R-CNN model [39] trained with lung CT scan images was used for detecting the nodule region in a CT image as an initial step. Secondly, a segmentation model, AWEU-Net, was proposed for segmenting the nodule boundaries of the detected nodule region. The proposed segmentation model, AWEU-Net, includes PAWE and CAWE blocks to improve the segmentation performance. Compared to the state-of-the-art models, the proposed AWEU-Net model yields the best segmentation accuracy with DSC and IoU scores of 89.79%, 90.35%, and 82.34%, 83.21% on the LUNA16 and LIDC-IDRI datasets, respectively. Although the proposed method provided promising nodule segmentation results, the number of parameters of the segmentation model is a bit high (around 34.5 million). Thus, it is not appropriate for computing devices with limited resources. Consequently, ongoing work will aim at developing a lightweight nodule segmentation model. In future work, we will develop a comprehensive nodule segmentation system, and it will be able to classify and grade nodule malignancy.  
Conceptualization, S.F.B. and M.M.K.S.; methodology, S.F.B. and M.M.K.S.; software, S.F.B. and M.M.K.S.; validation, S.F.B. and M.M.K.S.; formal analysis, S.F.B. and M.M.K.S.; investigation, S.F.B. and M.M.K.S.; resources, S.F.B. and M.M.K.S.; data curation, S.F.B. and M.M.K.S.; writing--original draft preparation, S.F.B. and M.M.K.S.; writing--review and editing, S.F.B., M.M.K.S., M.A.-N. and H.A.R.; visualization, S.F.B. and M.M.K.S.; supervision, M.A.-N., D.P. and H.A.R.; project administration, M.A.-N., D.P. and H.A.R.; funding acquisition, M.A.-N., D.P. and H.A.R.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was approved by URV.  
Figure 11: Examples of segmentation results by different state-of-the-art models. (**a**) Segmentation results on the LIDC-IDRI dataset and (**b**) segmentation results on the LUNA16 dataset. The colors of the segmentation visualization results are presented as follows: TP (orange), TN (background), FP (green), and FN (red).
## 5 Results and Performance Analysis
### Throughput  
Figure 4a-d depict network performance in terms of average throughput in bits per second (bps). The proposed E-OLSR is compared with AODV, DSR, GRP, and OLSR in different simulation scenarios, varying the number of UAVs, speed, altitude, and data rate. Here, the \(x\)-axis indicates the simulation times, while they-axis represents the throughput in bps. From Figure 4, it is observed that the performance of throughput depends on the number of UAVs. The throughput and network performance increase with an increasing number of UAVs in a fixed network area of 1000 m \(\times\) 1000 m. In all scenarios, E-OLSR outperforms other existing routing protocols. While the throughput of AODV is better than others in high data rate, which is almost identical to E-OLSR in Figure 4b,d. On the other hand DSR shows the lowest throughput because of the extra routing overhead. Moreover, the proposed E-OLSR has achieved superior performance in all routing protocols.  
Figure 3: Node model of UAVs.