esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Green Mold Pathogens on Lemons Using Hyperspectral Images
###### Abstract  
Hyperspectral images in the spectral wavelength range of 500 nm to 650 nm are used to detect green mold pathogens, which are parasitic on the surface of lemons. The images reveal that the spectral range of 500 nm to 560 nm is appropriate for detecting the early stage of development of the pathogen in the lemon, because the spectral intensity is proportional to the infection degree. Within the range, it was found that the dominant spectral wavelengths of the fresh lemon and the green mold pathogen are 580 nm and 550 nm, respectively, with the 550 nm being the most sensitive in detecting the pathogen with spectral imaging. The spectral intensity ratio of the infected lemon to the fresh one in the spectral range of 500 nm to 560 nm increases with the increasing degree of the infection. Therefore, the ratio can be used to effectively estimate the degree of lemons infecting by the green mold pathogens. It also shows that the sudden decrease of the spectral intensity corresponding to the dominant spectral wavelength of the fresh lemon, together with the neighboring spectral wavelengths can be used to classify fresh and contaminated lemons. The spectral intensity ratio of discriminating the fresh lemon from the infected one is calculated as 1.15.  
healthy and infected lemons; Hyperspectral image; _Penicillium digitatum_ pathogen; lemon skin; dominant spectral wavelength; spectral intensity ratio +
Footnote †: journal: Applied Physics and Chemistry
## 4 ML Applications for RIS-Assisted Communications
### ML Applications for Signal Decoding  
The optimal decoding of the signal at the UE device improves the transmission error and enhances the overall efficiency of an RIS-assisted system. In [104], the authors proposed demodulating an OFDM signal for an RIS MIMO system based on a CNN. The transmitted data at the BS were used to test the model, which was trained to generate synthetic channel realizations. The framework considered the Saleh-Vanenzuela model to generate the Rayleigh fading channels. For the architecture, the framework considered a CNN with two hidden layers, the Adam optimization, and a ReLU activation function, using 32 filters of size 3 \(\times\) 3. The output layer used a softmax activation function to determine the outputs. The BER and symbol error rate (SER) performance were evaluated [104].  
In [105], the authors developed a signal detection scheme for RIS-assisted communication systems using a hybrid CNN-GRU architecture [105]. The combined network was trained offline by simulating the OFDM data over the channel. The BER and SER metrics were evaluated for that model. For the CNN, the architecture consisted of 64 filters of size 3 \(\times\) 3 using a pooling layer. The GRU network consisted of four layers, where the hidden layers used a _Tanh_ activation while a sigmoid activation was applied at the output layer. The output layer showed the probability of belonging to one of the classes [105]. The architecture applied a cross-entropy loss and the Adam optimization.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
###### Abstract
The main contributions are as follows:  
Firstly, motion estimation is accomplished using the semi-direct method, which realizes the mutual advantage compensation of the feature-based method and the direct method, that is, the former accurately tracks keyframes, and extracts point and line features for back-end nonlinear optimization and loop-closure detection, while the latter rapidly tracks non-keyframes through direct image alignment. Secondly, the sliding window mechanism is adopted to effectively combine visual and inertial information, and an improved marginalization method is used to decompose the high-dimension matrix corresponding to the cost function, which binds the computational complexity and improves the computational efficiency. Finally, experiments are conducted to compare SDPL-VIO and the other state-of-the-art VIO methods. The results of the European Robotics Challenge (EuRoC) datasets show that SDPL-VIO can consider both speed and accuracy. The rest of this paper is organized as follows. In Section 2, the mathematical formulation is given. Next, the proposed system implementation is described in Section 3. The experimental results and analysis are shown in Section 4. Finally, a conclusion is given in Section 5.
## 4 Performance Analysis for Different ERSR Radius Sets
### Second-Stage Coverage Ratio Analysis  
As an important parameter describing multicast performance, multicast coverage ratio is defined as the ratio of total SU to MG users. Under the proposed relay selection scheme RDUR, we analyze the second-stage coverage ratio performance of COM-NOMA in this section.  
From the analysis in Section 4.1, for USU, as long as there is a UR in his/her ERSR, successful reception can be guaranteed. When USU is in the annular area \(A_{n}\) with ERSR radius \(r_{n}\). The probability of having a UR is given by (25).  
\[\begin{split}& P_{U\Omega_{1}}^{A_{n}}\\ =&\frac{1}{2\pi}\int\limits_{\frac{D_{1}^{4}\pi^{2}}{2}} ^{\frac{D_{2}^{4}\pi^{2}}{2}}\arcsin\left(\frac{r_{n}}{\tau_{n}}\right)\frac{4 \tau_{7}\pi r_{n}}{R^{2}}\frac{2\tau_{n}}{R^{2}}\mathrm{d}\tau_{n}\\ =&\frac{2r_{n}}{3\pi R^{4}}\left\{\frac{r_{n}}{4} \left[D_{\alpha\alpha}^{I_{n}}\sqrt{\left(D_{\alpha\alpha}^{I_{n}}\right)^{2} -4{r_{n}}^{2}}-D_{in}^{I_{n}}\sqrt{\left(D_{\alpha\alpha}^{I_{n}}\right)^{2}- 4{r_{n}}^{2}}\right]+\frac{\left(D_{\alpha\alpha}^{I_{n}}\right)^{3}\arcsin \frac{2\tau_{n}}{D_{\alpha\alpha}^{I_{n}}}-\left(D_{in}^{I_{n}}\right)^{3} \arcsin\frac{2\tau_{n}}{D_{\alpha\alpha}^{I_{n}}}}{4}\right.\\ &-{r_{n}}^{3}\left[\ln\left(\frac{D_{\alpha\alpha}^{I_{n}}}{2} \left(1+\sqrt{1-\frac{4{r_{n}}^{2}}{\left(D_{\alpha\alpha}^{I_{n}}\right)^{2} }}\right)\right)-\ln\left(\frac{D_{\alpha\alpha}^{I_{n}}}{2}\left(1+\sqrt{1- \frac{4{r_{n}}^{2}}{\left(D_{\alpha\alpha}^{I_{n}}\right)^{2}}}\right)\right) \right]\right\}.\end{split} \tag{25}\]  
In \(\mathcal{A}\), the sum probability that USU is served by one UR is  
\[P_{U\Omega_{1}}=\sum\limits_{n=1}^{N}P_{U\Omega_{1}}^{A_{n}}. \tag{26}\]  
After RDUR scheme, the probability that USU can successfully receive is given by  
\[P_{u\to s}=\left(1-\mathrm{C}_{1}\right)\left[1-\left(1-P_{U\Omega_{1}} \right)^{\mathrm{C}_{1}\left(M_{1}+M_{2}\right)}\right], \tag{27}\]  
which also is the coverage ratio of the second stage.
## 5 Conclusions  
This study presents the first systematic evaluation of using a smartphone as an RM with AP technologies in a manikin-based VTT paradigm, which enables objective assessment of transmission factors (i.e., SNR) and sound characteristics (i.e., noise type) while controlling for human variability. The results demonstrated better transcription accuracy in speech-shaped noise compared to babble noise at higher SNRs and in RM versus baseline (non-RM) conditions. In addition, the AP (NC mode)/iPhone (LL feature) configuration yielded comparable VTT performance to the clinically available Roger system in challenging noise environments, suggesting its potential to work as a hearing technology when properly configured.  
However, two challenges must be addressed before the smartphone/AP RM system can be adopted as a solution for people with hearing difficulties in noisy environments: the current lack of human performance data and the presence of Bluetooth transmission delays. Translating these promising findings into practice will require both behavioral validation with human participants and engineering advancements to reduce signal transmission latency. Given the widespread availability and cost-effectiveness of smartphone platforms, overcoming these limitations could lead to more direct access to hearing assistance.  
Conceptualization, L.T.; methodology, L.T.; formal analysis, S.Q.; investigation, S.Q.; data curation, S.Q.; writing--original draft preparation, S.Q.; writing--review and editing, S.Q. and L.T.; visualization, S.Q.; supervision, L.T.; project administration, L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw VTT data reported in this study are available in Appendix A: Table A1. Raw transcription accuracy data. Further inquiries can be directed to the corresponding author.  
The authors want to thank Phonak for providing the Roger equipment for the study. During the preparation of this manuscript/study, the first author used [DeepSeek-V3 and GPT-4o] for the purposes of sentence refinement based on the authors' original wording. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 8 Discussion
### Limitations  
To perform a good analysis of the result in Section 7, it is important to understand the limitations and shortcomings of the study. Firstly, the sample size of \(n=8\) participants, of which four were clinically diagnosed USN patients, is relatively small. The statistical relevance of the results and the conclusions arising from them should be interpreted with a critical mind. Additional tests with clinical patients and non-clinical test users are required to further support the findings presented in this section. Secondly, the data collected during the tests solely focus on measuring the head direction and the task performance. Thirdly, the VR environment and the xylophone are currently abstract visualisations and can be extended in the future to create a more realistic experience. Furthermore, general prior knowledge of the participants is limited to the severity of USN. Therefore, recognising whether the recorded behaviour is induced by the USN or another underlying condition is impossible, e.g., the diagnosis of apraxia in Patient 4 could also impact the collected data. In this discussion, the assumption is adopted that any behaviour found in a patient is mainly the result of their USN diagnosis.
# Role of 4-Hexylresorcinol in the Field of Tissue Engineering
## 3 4-HR Application in the Tissue Engineering
### Application of 4-HR in Vascular Regeneration  
4-HR induces VEGF and angiogenic expression in macrophages [34]. It has angiogenic and anti-thrombotic effects and has been used as a component of vascular patch material in vascular surgery [12,36]. A 4-HR-incorporated silk vascular patch was used in angioplasty, and was comparable as a patch material to the polytetrafluoroethylene (PTFE) commercial product [12]. The 4-HR silk vascular patch was applied in a rat carotid artery and showed stable artery patency and flow in angiography. A histology study showed that a new endothelium was regenerated around the silk mat with no thrombus formation [12]. Moreover, the von Willebrand factor (vWF) was highly expressed in the regenerated endothelium (Figure 4) [12]. 4-HR with antimicrobial and pro-angiogenic ability promotes endothelium wound healing in carotid artery defects and is successfully used as a component of silk vascular patches.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Day 7** & **Day 14** \\ \hline Control group & 0.24 \(\pm\) 0.84 mm & 1.98 \(\pm\) 1.12 mm \\ Experimental Group A & 0.92 \(\pm\) 1.00 mm & 2.63 \(\pm\) 0.68 mm \\ Experimental Group B & 0.89 \(\pm\) 0.61 mm & 2.90 \(\pm\) 0.42 mm * \\ \hline \hline \end{tabular}  
*: \(P<0.05\), comparison between control and group B.  
\end{table}
Table 2: The distance of tooth movement. Reproduced with permission from [PERSON] [43].  
Figure 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Control** & **4-HR-Incorporating SFM** & **Conventional SFM** \\ \hline Total new bone (\%) & 37.84 \(\pm\) 8.30 & 56.64 \(\pm\) 15.74 * & 53.35 \(\pm\) 10.52 * \\ Residual membrane (\%) & & 75.08 \(\pm\) 10.52 & 92.23 \(\pm\) 5.46 \\ \hline \hline \end{tabular}  
*: SFM: silk fabric membrane; *: \(P<0.05\) compared to uncovered control.  
\end{table}
Table 1: Histomorphometric analysis. Reproduced with permission from [PERSON] [41].
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 3 Procedures and Methodologies
### SAR Sentinel-1 Data Processing  
Many authors have used various algorithms for the detection of non-permanent water surfaces. Among these approaches, we can highlight the supervised classification [49], automatic non-supervised classification [46; 50], RGB combination [38; 51], or the detection or calibration threshold technique [41; 52; 53; 54; 55; 56; 57]. In this study, the methodologies used were RGB composition and the calibrated thresholds technique. Before the application of any techniques and to perform a multitemporal analysis, it is necessary to unify in a single file the different bands of \(\sigma_{0}\) of the images before and after the event using a stack.  
The RGB composition is an appropriate method to visualize multi-temporal modifications and facilitates the detection of changes on the terrain surface through a temporal color image [58]. It is a method based on the differences between the pre- and post-event images, and in which a combination of bands is set for these differences to become visually remarkable. These differences are defined by the change in the intensity value for the same pixel on different dates. The result is a multitemporal image to which a band is assigned to each of the primary colors to form an image of RGB composition [38].  
The other methodology used was the calibration threshold technique, also called binarization. It is a simple and quick process that differentiates flooded areas from those that are not by generating histograms. The use of this methodology is not recommended for fast flash flooding, where mapping is urgently needed, and the presence of noise causes some uncertainty [59]. Knowing that the backscatter values in permanent waters and in flooded areas are generally the most negative and differ markedly from the following radiometric values caused by other physical changes, such as, for example, the moisture content of the soil, it is possible to establish any limit [40]. It is necessary to emphasize that an equitable distribution of water and land is necessary so that the histogram has two clear distributions to more precisely limit the threshold. Through the characteristic polarization histogram, we obtained the number of pixels in the image for certain values of intensity or backscatter. In this way, the threshold could be set manually between flooded areas and those that were not. Later, using a mathematical equation of bands, a new bi-colour image separating both zones was achieved [54].
## 4 Discussion
AE monitoring is very sensitive to the small change of displacement [27; 28; 29; 33], which can detect the weak internal activity of the slope. AE parameters have obvious change characteristics, and have the potential to identify the critical sliding state in advance. Thus, AE technology may realize the early warning of a landslide, and play a significant role in disaster prevention and mitigation [25; 28; 29; 34]. As for the mechanical measurement part, the ring dynamometer measures the axial tension within an anchor cable. According to the mechanical analysis [38], the determined relationship between the axial tension within an anchor cable and the sliding force of slope can be denoted by mathematical formula, and then the sliding force of the landslide can be roughly calculated. According to the increment of sliding force, the warning level can be divided to realize the monitoring and warning based on mechanical parameters. Due to the limitation of the experimental equipment, the actual deformation of the landslide test does not reach a large deformation (i.e., \(>\)500 mm). The model test of the monitoring device has some differences compared to in-site landslide conditions and hence the significance of the experimental results should be further investigated in terms of the practical applicability. In addition, the accuracy and sensitivity of axial tension measurement are not high enough. The maximum velocity in the experiment is more than 4 mm/s which is beyond the velocity scale of catastrophic failure [42], and the effectiveness for an early warning system need to be further demonstrated. Moreover, a different deformation pattern of the rubber tube may occur regarding different depth of the tube buried below the sliding surface, which may lead to different AE behaviour and could affect the interpretation of the AE data for potential slope instability. AE was generated by shear deformation and the need to be recorded continuously in the case of long-term monitoring in order to enable proper information on the displacement magnitude. This operation may place some stress on the data recording and transmission system. However, this problem can be addressed through wake-up technology for an AE recording module triggering by slight subsurface deformation. All in all, there are still some problems to be explored in future research for the general application of the proposed flexible monitoring device. Figure 11: Schematic diagram for the change of flexible device during landslide process.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 2 The Proposed Method for Cable Force Determination
### Image Data Processing and Analysis
#### 2.2.2 Determination of Tracking Target  
Since targets on stay cables and the fixed structure cannot be easily distinguished, targets were sorted according to their x coordinates. As shown in Figure 5, the first two targets are stay cable targets P\({}_{1}\) and P\({}_{2}\), and the last two targets are fixed structure targets P\({}_{3}\) and P\({}_{4}\). The slopes of P\({}_{1}\)P\({}_{2}\), P\({}_{1}\)P\({}_{3}\), P\({}_{1}\)P\({}_{4}\), P\({}_{2}\)P\({}_{3}\) and P\({}_{2}\)P\({}_{4}\) are first determined, based on which the intersection angles of them are calculated using Equation (3).  
\[\tan\theta=\left|\frac{k_{1}-k_{2}}{1+k_{1}k_{2}}\right| \tag{3}\]  
In the equation, \(\theta\) is the intersection angle. \(k_{1}\) and \(k_{2}\) are the slopes of two intersecting lines. As specified in Figure 5, the intersection angles of P\({}_{1}\)P\({}_{2}\) and P\({}_{1}\)P\({}_{3}\), P\({}_{1}\)P\({}_{2}\) and P\({}_{1}\)P\({}_{4}\), P\({}_{2}\)P\({}_{1}\) and P\({}_{2}\)P\({}_{3}\) and the supplementary angle of \(\angle\)P\({}_{1}\)P\({}_{2}\)P\({}_{4}\) are marked as \(\theta_{13}\), \(\theta_{14}\), \(\theta_{23}\) and \(\theta_{24}\), respectively (the angle range is 0-90\({}^{\circ}\)). The cable vibration (noted as \(x\)) is in the direction  
Figure 4: Image data processing procedure.  
perpendicular to P1P2, and the components of vibration in the direction of P1P3, P1P4, P2P3 and P2P4 can be expressed as \(x\sin\theta_{13}\), \(x\sin\theta_{14}\), \(x\sin\theta_{23}\) and \(x\sin\theta_{24}\), respectively. Therefore, in order to obtain the maximum displacement of the target cable, the tracking targets are chosen according to the maximum angle among \(\theta_{13}\), \(\theta_{14}\), \(\theta_{23}\) and \(\theta_{24}\). For example, in the case of Figure 5 where \(\theta_{23}\) is the maximum angle, P2 and P3 are targeted for tracking.
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 2 Related Works
### DTN Forwarding Strategies
Additionally, the GeoSpray allows each node to forward the bundle copy to another node that can take the data closer to the destination. Therefore, this protocol controls flooding through the settlement of an upper bound on the number of replicas per bundle, while minimizing the transmission overload and resource consumption. The Geographical Routing scheme with Angle-Based Decision [32] is a geographic awareness routing protocol for message delivery in delay-tolerant networks (DTNs). This scheme can select relay nodes in geographic proximity to perform message delivery towards a destination. It consists of three design functions: (1) inclusion of the advantages of Spray and Wait; (2) consideration of the moving direction, velocity, and angle of each relay node; and (3) the selection of an appropriate relay node which is moving toward the destination. By exploiting geographic locality information, this protocol is able to select an appropriate relay node which is moving towards the destination, and iteratively hands over message copies to relay nodes in a network. The Velocity and Direction (VeloSent) routing protocol [33] operates in three consecutive phases. First, an analysis of the environment is performed to detect neighboring nodes and their respective contexts. Then, the neighbors that met the destination more recently are considered using the context information about time, location, and velocity of the destination node. Secondly, from this information, an estimation of the new location of the destination node is obtained (a straight line route is considered). In the third phase, the estimated location of the destination is used in order to determine which of the neighboring nodes will most likely meet the destination node. This process is possible by mathematically estimating the timing and the location of a likely encounter between them. Delegation Query (DelQue) [34] focuses on sources initializing interest-based queries and select relays by considering their capabilities for both query and response. The chosen relays take charge of both querying the relevant interest data and returning it to the demander. DelQue uses geo-community and mobility prediction in its algorithm. The Geo-community concept is used to explore location information from a community, thereby taking advantage of its geolocation. Mobility prediction is used when the source has mobile properties. Spatio-temporal prediction is also used, in order to exploit the information that some nodes obtain at a given location and at a given interval of time. In order to give a more general view about the trade-offs between the aforementioned routing protocols and our proposed forwarding strategies (Contact-Based Controlled Replicationwith Neighborhood Classification and Mobility-Based Controlled Replication with Neighborhood Classification), Table 3 summarizes the differences and similarities between them.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 4 Simulation Results
### Batch Size  
This article also simulates and analyzes the training speed of the FDNN model. We set the batch size as 10, 100, and 1000 symbols. Figure 5 shows the training loss curves under different batch sizes.  
Figure 4: \(\Delta_{SNR}=7\) dB for three types: (**a**) type 1; (**b**) type 2; (**c**) type 3.  
Figure 5: Performance of FDNN under different batch sizes.  
Figure 3: \(\Delta_{SNR}=5\) dB for three types: (**a**) type 1; (**b**) type 2; (**c**) type 3.  
With increasing training epochs, the training loss consistently decreases and converges to a small value. The convergence loss for batch sizes of 10 and 100 samples is notably lower than that for a batch size of 1000 samples. In earlier training rounds, the loss for a batch size of 10 samples decreased more rapidly than that for 1000 samples and slightly faster than 100 samples. However, considering computational complexity and the final convergence loss, a batch size of 100 samples is deemed more suitable.  
Moreover, the training error rapidly decreases to a low value within the initial 10 rounds when employing a batch size of 100 samples, suggesting that the FDNN model can achieve quick convergence with lower computational demands. This characteristic is advantageous for practical applications where low latency is essential.