esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 3 Solution-Phase Ligand Exchange
### Improved Diffusion Length of Quantum Dot Film by Matrix Approach  
Recently, research on composition-matched matrices to improve carrier mobility has focused on CQDs [51]. Especially, metal halide perovskite materials exhibit well lattice matching matrices with CQDs, showing improved CQD emission performance [52]. Based on these results, researchers have deduced that the limitations in the diffusion length of photocarrier and deterioration of \(V_{\text{OC}}\) could be tackled by spatial changes in the matrix structure generating from the CQD film fabrications. It has been reported that photocarrier transfer at the CQD film is crucially affected by thickness of matrix in terms of electron tunneling and resonance energy transfer [52; 53; 41; 54]. Non-uniformity of the matrix results in the increase of structural and energy disturbances and the decrease of diffusion lengths within CQD thin films and devices [55; 41; 56; 57; 47].  
To solve this problem, [PERSON] et al. devised a 2D matrix strategy using the perovskites to prevent the formation of a thick matrix, which can be formed during film solidification [58]. It aimed to extend the diffusion length of photocarrier in PbS CQD film through the matrix refinement, thereby limiting the PbI\({}_{2}\) matrix components to 2D materials produced from a PbI\({}_{2}\) and hybrid amine coordination complex, which is a similar phenomenon in 2D perovskite synthesis [59; 60]. It suggests that the hybrid amine coordinate complex can produce high-quality 2D inorganic matrices that program inter-nanoparticle spacing on an atomic scale, thereby improving the robustness of the PbS CQD packing density and uniformity, leading to reduced energy disturbances.  
As a result, this study demonstrates a matrix engineering approach that can largely improve the diffusion length of photocarrier in PbS CQD solids and enhances the energetic disorder of densely-packed PbS CQD thin films, resulting in record-breaking performances on increasing the thickness of the PbS CQD thin films. This matrix-based CQD material engineering strategies can be extended to a wide range of PbS CQD application fields, including light-emitting diodes, photodetector, and field effect transistor.
Figure 1: Application of self-adhesive tapes to wall and vents (**a**); window and foundation (**b**); wall and window/wall and roof (**c**). During the formation of the adhesive joint, adhesion and cohesion forces are affected, and the mechanical properties of the self-adhesive products are characterised by the shear resistance, peeling resistance, and tackiness [28; 29; 30; 31]. The strength of the adhesive bond between the adhesive tape and the substrate is characterised by the peel adhesion. Adhesion is not a natural characteristic of tape, rather it depends on many factors such as temperature, peeling speed, adhesive thickness, pressure force, dwell time, and the surface on which the tape is glued to. The influence of the latter factor, i.e., the surface, on the performance of the adhesive connection between the construction tape and the surface is discussed only in a few works. According to the authors, the results of the research [26] encourage discussions about the possibility of using a standard surface in studies to assess the real bond between the base and the tape. [PERSON] et al. [27] found that the adhesion and shear of the same tapes glued on different surfaces have the same range of values; therefore, the influence of the surface may be difficult to unambiguously define. However, it is clear that the tightness of the sealing joints depends mainly on the type of substrate used [32; 33]. The worst self-adhesive tape tack was found when tape was glued onto a plastered surface; however, better tack results were obtained when the tape was glued onto chipboard, OSB, and a cement sawdust board [34]. [PERSON] et al. investigated the effect of OSB and plywood surface treatments on the adhesion of acrylic PSA tape; depending on the surface, differences in the load capacity were found [35]. When analysing research publications on the use of adhesive tapes for construction purposes, it was noted that many authors emphasise the lack of knowledge about the optimal application of tapes for sealing joints, so conducting such research remains very relevant. The analysis of the literature revealed that detailed research related to changes in the adhesive properties of tapes due to environmental factors should be conducted. It is clear that there is no clarity as to how those properties depend on the substrate of the building materials on which they are glued and how they evolve over time. The aim of this research is to investigate the peel adhesion of commercial single-sided self-adhesive tapes with different compositions to different construction surfaces and to determine the variation in this characteristic due to climatic effects. Systematised and analysed data would be useful for improving the sealing tape materials and building sealing technical solutions and would allow the better understanding of the significance and impact of different parameters during the development of an artificial aging model that would allow the reliable prediction of the service life of the tapes.
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 2 Lutein Sequestration Versus Structural Function
### Limits of Structural Lutein Content due to Its Function  
The nature of lutein as a structural carotenoid determines that its contents will be low, as required to fulfill photochemical functions. The storage capacity of lutein in the structural form was estimated as 4% versus 10-20% in the oil form. To sequestrate 2% DCW of lutein in the structural form, the algal cell needs to produce 50% DCW of photosynthetic protein for lutein storage. Consider the optimum protein content of different algal strains: _Chlorella_ spp. (50-60%, DCW), _Chlamydomonas_ spp. (48%, DCW), _Scenedesmus obliquus_ (50-56%, DCW) and _Dunaliella salina_ (57%, DCW) [64]. It is almost impossible for algal strains to synthesize over 50% DCW of photosynthetic proteins for the 2% DCW of lutein sequestration as a structural pigment. The nature of lutein as a structural carotenoid determines that its content will not be very high in fulfilling photochemical functions in microalgae. The data used to calculate lutein storage capacity in LHCs is based on that of higher plants. However, the content of LHCs protein to total protein varies in different algal strains. Considering the conserved structure of LHCs in algae and higher plants,using the above method to estimate the lutein storage capacity in LHCs and lipid droplets is assumed acceptable.  
Sequestering lutein in lipid offers a much greater potential, as microalgae could accumulate up to 40-60% DCW as lipids [65]. Based on the lutein storage capacity in the oil form, algal cells would only need to generate 10-20% DCW of lipids for the sequestration of 2% DCW of lutein. Therefore, lutein sequestration in \"oil form\" presents a significantly higher lutein storage upper limit than the \"structural form.\" Exploring the method to accumulate lutein in oil form could result in a higher lutein content in algal cells.
## 6 Results and Discussion
### Effectiveness Analysis Using Euclidean Distance  
The overall analysis of the recommended ABC-ROA-based privacy preservation model over the Ethereum network with three datasets in terms of Euclidean distance is given in Figure 5. From the analysis, dataset 2 gives a very low Euclidean distance than dataset 1 and 2. While using dataset 2, the developed ABC-ROA-based privacy preservation model gives improved Euclidean distance of 36.03%, 8.62%, 2.54%, and 8.77% over HHO, EF-HHO, BCO, and ROA. In the graph analysis, the developed ABC-ROA method is utilized to show effective performance. Here, the existing EF-HHO algorithm attains second-best performance. While considering all three datasets, the Euclidean distance of the proposed method shows better performance in dataset 2. Thus, the developed ABC-ROA-based data privacy preservation model over the Ethereum network gives higher effectiveness than the other heuristic algorithms.
# A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology
###### Abstract
Compared with traditional image segmentation techniques, the image segmentation algorithm based on deep learning [7; 8] can directly extract cracks from the original image in an end-to-end manner. Compared with traditional crack monitoring techniques, RFID sensing technology is wireless, thus avoiding complicated sensor wiring. Also, it can work in a passive manner, which requires little or even no external power. In addition, the cost of RFID-based sensors is generally relatively low. RFID sensing technology has the advantages of wireless operation and low cost, which makes it highly competitive in the field of structure health monitoring [9; 10; 11; 12; 13]. Therefore, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Several RFID-based crack monitoring and detection methods are systematically summarized. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail.
## 3 Results
### Atmospheric Investigation
#### 3.2.1 Ground Geomagnetic Field
The window length has been selected according to the band of the signal as two times longer than the higher cut-off period or 35 days for a more extended period (so they are: 2 h, 12 h, 2 days, 14 days, and 35 days). The window has been shifted of 1/5 of its length at any movement to be able to retrieve any possible anomalous signal. Only the window with more than 90% of good original samples has been considered. Furthermore, the correlation coefficient has been computed only for the samples acquired in quiet geomagnetic conditions ( \(\mid\) Dst \(\mid\)\(\leq\) 20 nT and ap \(\leq\) 10 nT). If the window has less than 10 samples obtained in quiet geomagnetic conditions, it is rejected. The estimated correlation coefficient in function of the time is represented in Figure S2 (in Supplementary Materials File S1). When it is equal to 1, it indicates that the two stations observe a perfect identical trend inside the window; when zero, it means the signals are uncorrelated, and when it is \(-1\), it means they are anti-correlated (so an opposite trend). As we want to search for differences in the two observatories, we further selected the uncorrelated windows defined with \(\mid\)r \(\mid\) \(<\) 3, and their cumulation over time is shown in Figure S3 (in  
Figure 8: Geomagnetic Y (East) component of geomagnetic field measured by GUI and TAM ground observatories. The signal has been filtered into 5 bands. Supplementary Materials File S1). This approach is similar to what [PERSON] et al. [78] applied to two geomagnetic observatories in Italy to study the preparation phase of the Central Italy seismic sequence 2016-2017. Here the approach has been improved, including several bands to investigate the signal from the geomagnetic station. From the cumulative graphs shown in Figure S3, the most interesting plots are the ones with a lower period of less than 1 h for X and Y, which shows a similar acceleration in the central period between -120 days to -55 days. In addition, the Y signal in the period band between 1 day and 1 week also shows anomalies between -150 days and -70 days. The other trends are almost linear, or without anomalies, so they are not interesting from this point of view.
## 2 Results
### Multivariate Chemometric Analyses  
#### 2.3.1 Approach 1  
Principal component analysis (PCA) was undertaken to visualize global data patterns using VOCs identified through both approach 1 and approach 2. Scores of the first principal component (PC 1) using the eight VOCs from approach 1 can be observed in Figure 3. PC 1 in this case accounts for 42.3% of the variance in the sample data. Significantly, PH clusters with control (\(p\)-value relative to Control = 0.33) and PL clusters with cancer (\(p\)-value relative to cancer weeks 1-3 = 0.47). Along the same lines, PL shows low \(p\)-values when compared to control samples (\(p\)-value = 0.0023). PH and control samples, on the other hand, demonstrate high statistical significance relative to cancer, with \(p\)-values < 0.001. Furthermore, VOCs are differentially expressed when comparing the PH treatment to the PL treatment, with a \(p\)-value equal to 0.0057. Differences in VOC expression are clearly seen as early as the first week of treatment, and no correlations with time were observed for this panel of VOCs when analyzed by PCA for all of the sample classes presented.  
Continuing with the VOCs highlighted from approach 1, forward feature selection coupled with linear discriminant analysis (LDA) was implemented to distinguish control and cancer week 1-3 samples. This method identified a panel of four VOCs (DCHM, 5M2H, DTB, and 2-HEX) with the ability to distinguish cancer from control with sensitivity = 91% and specificity = 100%. No significant trends over the course of three weeks were observed within the cancer samples. This model of four VOCs was then tested on the PH and PL samples. The first linear discriminant can be visualized for all sample classes (both training and testing) in Figure 4. PH samples displayed an intermediate response in between cancer weeks 1-3 and control samples in the LDA plot. PH trends towards healthy controls and showed significant differences when compared to cancer (\(p\)-value < 0.001) and is also statistically significantly different from PL (\(p\)-value = 0.015). No correlation over time was observed for the PH samples. However, PL is time-dependent, trending towards cancer and clusters with cancer week 1-3 samples by the third week (PL week 3 vs. cancer week 1-3; \(p\)-value = 0.42). This is observed because PL week 3 was significantly different from PL week 1 (\(p\)-value = 0.023). The PL samples throughout the three weeks also showed significant differences when compared to the control (\(p\)-value < 0.001). Multivariate analyses using approach 1 showed that both large and small panels of VOCs identified  
Figure 3: PC 1 scores in one dimension using the VOCs highlighted through approach 1 for all sample classes of interest (PH = pitavastatin high, PL = pitavastatin low, C = cancer). **\(p\)-value < 0.01, ***\(p\)-value < 0.001.  
that pita low samples were trending in the direction of cancer samples, whereas pita high samples displayed a response more similar to that of the control sample class.
# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
## 5 Conclusions  
This study addressed the challenge of detecting and localising abnormal PGs in parathyroid scintigraphy with 99 mTc-sesstamibi images using a multi-input DL method that successfully reads and extracts features from the early MIBI phase, the late MIBI phase, and the 99 mTc04 thyroid scan. Those images are processed simultaneously to achieve some level of comparison and reasoning. The suggested model has been evaluated with the Grad-CAM method, and the medical experts have assessed the indicated regions of abnormal findings. The agreement rating between the model and the human experts reached approximately 95% on a patient level and 76% on a PG level. With the absence of related works that use the same image source, this study is the first attempt to introduce DL approaches for localising PGs in parathyroid scintigraphy scans.  
Conceptualisation, D.J.A.; data curation, N.D.P. and T.S.; formal analysis, D.J.A.; methodology, I.D.A.; project administration, G.S.P.; resources, N.D.P.; software, I.D.A.; supervision, D.J.A.; validation, D.J.A., I.D.A. and T.S.; writing--original draft, D.J.A. and G.S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Research and Ethics Committee of the University General Hospital of Patras, Greece (protocol code 108/10-03-2022).  
Patient consent was waived by the Research and Ethics Committee of the University General Hospital of Patras, Greece for this retrospective study.  
The dataset is not publicly available due to ethical reasons. The dataset can be shared with fellow researchers upon a reasonable request.  
The authors declare no conflict of interest.
# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 2 Methods
### Chip Introduction  
The ZYNQ chip, developed by Xilinx, is a System-on-Chip (SoC) that integrates a high-performance programmable logic section (PL) with a dual-core ARM Cortex-A9 processor system (PS). This configuration endows the ZYNQ chip with high flexibility and performance in processing large-scale data and real-time tasks. The Xilinx ZYNQ-7000 XC7Z010 used in this paper can reach a main frequency of 666 MHz, and its two processor cores can operate independently or collaboratively to execute various applications and system tasks. The PS side's FPGA programmable logic offers approximately 28,000 logic cells (Look-Up Tables, LUTs) and 17,600 flip-flops, enabling the implementation of various custom digital logic functions. The Xilinx Zynq-7000 EPP chip series is a versatile processing platform that has been employed in diverse research fields [13]. [PERSON] and others optimized a complex optical flow algorithm in C language on both desktop PCs and Xilinx Zynq-7000, demonstrating competitive performance and significantly reduced energy consumption on Zynq devices [14]. [PERSON] and colleagues performed effective partial reconfiguration on their open-source controller ZyCAP on Xilinx Zynq, aimed at reducing reconfiguration overhead and enhancing hardware resource utilization [15]. Additionally, [PERSON] and others proposed a co-design method of software and hardware for OpenFlow switches on heterogeneous SoC platforms, achieving high throughput on Xilinx Zynq [16]. [PERSON] and colleagues analyzed the security features of the authentication protocol of Xilinx Zynq FPGA, emphasizing the use of hardware-embedded delay PUF to protect privacy [17]. [PERSON] and others introduced an efficient PUF design based on SR-Latch, validated on Xilinx Zynq SoC devices, showing improved area utilization for low-area designs [18]. [PERSON] and colleagues explored the implementation of PTP functionality on Xilinx Zynq-7000 SoC devices to achieve nanosecond-level synchronization accuracy in substation automation systems [19]. [PERSON] and others proposed a fully pipelined convolutional neural network FPGA accelerator, and implemented various CNN models on Xilinx Zynq-7020 and Virtex-7 690t FPGA platforms [20]. Lastly, [PERSON] and others implemented a predictive control scheme for PMSM drives based on Luenberger disturbance observers on Xilinx Zynq SoC, comparing area utilization and time consumption for FPGA implementation [21]. In these studies, the Xilinx Zynq platform is extensively used in various research areas due to its flexibility, performance, and energy efficiency in applications such as accelerators, authentication protocols, PTP functionality, and neural network accelerators.  
Figure 2 shows the physical connection diagram in this paper, illustrating the connections among the MPU6050, LoRa module, UART, and ZYNQ7010, demonstrating the feasibility of the experiment.
# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
###### Abstract  
Diabetic retinopathy (DR) is a complication of diabetes that affects the eyes. It occurs when high blood sugar levels damage the blood vessels in the retina, the light-sensitive tissue at the back of the eye. Therefore, there is a need to detect DR in the early stages to reduce the risk of blindness. Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for a new task. Transfer learning has been applied to diabetic retinopathy classification with promising results. Pre-trained models, such as convolutional neural networks (CNNs), can be fine-tuned on a new dataset of retinal images to classify diabetic retinopathy. This manuscript aims at developing an automated scheme for diagnosing and grading DR and HR. The retinal image classification has been performed using three phases that include preprocessing, segmentation and feature extraction techniques. The pre-processing methodology has been proposed for reducing the noise in retinal images. A-CLAHE, DNCNN and Wiener filter techniques have been applied for the enhancement of images. After pre-processing, blood vessel segmentation in retinal images has been performed utilizing OTSU thresholding and mathematical morphology. Feature extraction and classification have been performed using transfer learning models. The segmented images were then classified using Modified ResNet 101 architecture. The performance for enhanced images has been evaluated on PSNR and shows better results as compared to the existing literature. The network is trained on more than 6000 images from MESSIDOR and ODIR datasets and achieves the classification accuracy of 98.72%.  
diabetic retinopathy; image augmentation; convolutional neural network; HR grading +
Footnote †: journal: Medical Imaging  
0000-0001-9993-4300][PERSON] 2
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 4 Discussion
### Limitations of This Study  
One limitation is the use of non-individual BRIRs in both experiments. Based on the results of previous studies, the influence of RDE on externalization maybe more pronounced when using individual BRIRs, especially for frontal sound sources [9; 15]. However, various studies have investigated the effect of individualization on externalization, the results are still inconsistent [4; 5; 14; 24; 25]. Since most commercially available binaural rendering systems are based on non-individual HRTFs/BRIRs [11], the experimental results are valuable for the externalization study in common use cases of binaural listening.  
Furthermore, only two virtual rooms were tested in the experiment. An anechoic chamber is chosen as a special room for testing listeners' expectations of externalized binaural sounds. To validate the results of this study, other rooms with different sizes and acoustic properties should be further used.  
Finally, this study investigated perceived externalization of static binaural sounds, and the dynamic scenarios with head movements were not taken into account. Several studies have reported that the dynamic cues caused by head movements (with head tracking) play an important role in externalization, especially for frontal sound sources [18; 19; 20]. It can be hypothesized that the overall externalization rating of frontal sound sources should be enhanced when using a binaural rendering system coupled with a head tracking device. Hence, to generalize the outcomes of this study, future experiments should consider the dynamic conditions.