esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Satellite Data  
Satellite images acquired by the Multi-Spectral Instrument (MSI) on board the Sentinel-2A/B constellation of tile 30 SY) for location 1 and 30 STG for location 2 were used, following the [PERSON] et al. [48] protocol. The product level was 2A, which provides surface reflectance. Images were downloaded using the Google Earth Engine (GEE) code editor. All the available spectral bands at a 10 m and 20 m spatial resolution were studied; the main characteristics of each band are shown in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Spectral Band Name** & **Wavelength (nm)** & **Spatial Resolution (m)** \\ \hline B02—Blue & 458–523 & 10 \\ B03—Green & 543–578 & 10 \\ B04—Red & 650–680 & 10 \\ B05—Vegetation Red Edge 1 & 698–713 & 20 \\ B06—Vegetation Red Edge 2 & 733–748 & 20 \\ B07—Vegetation Red Edge 3 & 773–793 & 20 \\ B08—NIR & 785–899 & 10 \\ B8A—NIR narrow & 855–875 & 20 \\ B11—SWIR 1 & 1565–1655 & 20 \\ B12—SWIR 2 & 2100–2280 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The study’s main characteristics are the Sentinel-2 spectral bands (name, wavelength, and spatial resolution).  
Figure 2: Example of postprocessing yield map; (**a**) is the original yield map acquired by the yield monitor, and (**b**) is the postprocessed yield map obtained after applying the protocol proposed in [PERSON] et al. [58].  
Satellite data were merged with the postprocessed yield maps using the spatial resolution of 10 m. Thus, a 10 m reference pixel was composed of a single yield value and the reflectance of each spectral band, being the reflectance of the spectral bands at 20 m repeated every four pixels. The negative buffer at each field boundary was increased to 20 m to avoid the edge effect. Only cloud-free images from sowing to harvest were considered, with visual verification of each image. All selected images were free of clouds. The availability of cloud-free images based on Days After Sowing (DAS) for each year is indicated in Table 3.  
In addition to the surface reflectance in the spectral bands, these were combined into the Vegetation Indices (VIs) normalized and most commonly used in the literature. VIs were selected based on the normalization function; thus, distinguishing from the reflectance values, a normalized VI is interpreted with relative values between \(-1\) and \(1\). Non-normalized functions can hamper interpretability, and in previous studies [48; 55], we examined other VIs and discussed their interpretability. In Table 4, the name and equations of each VI are shown.
## 3 CGST Codec Simulation for Processing Images
### CGST Algorithm Modification to Reduce Distortions  
As stated earlier, reducing the brightness level of darker parts of an image to zero (\"zeroing\") during compression of RGB channels in the correlation matrix by 3 dB or more can make it impossible for the decoder to restore the original information with the appropriate quality. When the compressed image contains completely dark areas in all three channels that do not appear in the original image, it is crucial to retain information about the level ratio of the original signal and the signal from the matrix output. This can be achieved by fixing their positive difference value. Therefore, we made modifications to  
Figure 3: Low-resolution images of satellite remote sensing during some steps of the CGST-based compression.  
the original CGST algorithm. Before the modifications, the difference signal for the random channel at the coder output was determined using the following function:  
\[e=\left\{\begin{aligned} & u-\widetilde{u},u>\widetilde{u},\\ & 0,u<\widetilde{u}.\end{aligned}\right. \tag{4}\]  
To avoid errors caused by \"zeroing\", according to Equation (5), we modified the CSGT algorithm in the simulation as follows:  
\[e=\left\{\begin{aligned} & u-\widetilde{u},u>\widetilde{u},\\ &|u-\widetilde{u}|\cdot k_{red},u<\widetilde{u},\end{aligned}\right. \tag{5}\]  
where \(k_{red}\) is the required coefficient of dynamic range compression, assumed to be 0.5 in the simulation. This modification to CGST ensures that the pixel brightness is not negative.  
After passing through the extrapolation block and having a transformation (5) applied to it, the brightness of the areas near an object's edges in encoded images significantly increases. On the other hand, the areas far from the borders remain at a lower brightness level. These signs indicate an increase in the image's spatial frequency. To test this assumption, we calculated the channel-averaged spatial frequency for the original image and one that passed through the codec, which turned out to be higher for the latter. Section 3.2 offers a more detailed analysis of this issue.
## 2 Materials and Methods
#### 3.1.3 TY Dye Removal Efficiency  
During the EC process, 10 mL of the TY solution was sampled every 5-10 min. After centrifugation, the dye concentration was determined spectrophotometrically. The dye removal efficiency (\(R\)) was determined using the following expression:  
\[R=\frac{c_{0}-c_{\mathrm{t}}}{c_{0}}\times 100 \tag{7}\]where \(c_{0}\) represents the initial concentration (0.05 mmol L\({}^{-1}\)), and \(c_{t}\) is the concentration at sampling time. As EC progresses, the removal efficiency of TY dye increases from zero to very high values (Figure 3). The growth dynamics and the final value of \(R\) depend on the experimental conditions (\(i\), initial pH, and \(t\)).  
This process is accompanied by a decrease in the intensity of the yellow colour, which is particularly pronounced at higher current densities and lower pH values. At a current density of 0.008 A cm\({}^{-2}\), for example, the visible colour intensity began to change after 20 min (at \(R\approx 50\%\), which at the end of the process reached \(\approx\)75 (i.e., after 32 min). At 0.024 A cm\({}^{-2}\), the colour change was already visible after 10 min, and after 40 min, the solution was completely colourless, with an \(R\) of about 99.40%.  
A similar effect can be observed when the initial pH is lowered. The solution with a pH of 3.0 showed the best results and became colourless after only 15 min (removal efficiency of 99.40%). The solution with a pH of 4.8 became colourless after 30 min and showed similar removal efficiency, while the solution with a pH of 7.0 lost its colour after 40 min, with \(R\approx 99.40\%\).
# Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity
## 4 Potential Indicators to Measure Process Modularity and Process Complexity
### Selection of the Indicator to Measure Process Complexity  
The four complexity indicators are summarised below for the purpose of selecting the most suitable one from them.  
[PERSON] et al. [43] developed a formula to measure the complexity of manufacturing systems that is expressed as follows:  
\[H_{s1}=\log m^{2}nr \tag{5}\]  
where \(n\) is the number of parts, \(m\) is the number of operations, while \(r\) is the number of machines.  
The second indicator is based on the concept of [PERSON]'s information entropy, and it is formulated through the following equation [52]:  
\[H_{s2}=-\sum_{j=1}^{M}\sum_{i=1}^{S}p_{ij}\cdot\log_{2}p_{ij} \tag{6}\]  
where \(M\) is the number of machines, \(S\) stands for the number of possible planned states of the machine \(j\) can be in, \(p_{ij}\) is probability that the machine \(j\) is in state \(i\).  
The third one, so-called modified flow complexity (\(MFC\)) indicator enumerates all tiers, nodes, and links weighted with determined \(\alpha\), \(\beta\), and \(\gamma\) coefficients. This indicator is enumerated using formula [53]:  
\[MFC=\alpha\cdot T+\beta\cdot N+\gamma\cdot L \tag{7}\]  
where \(\alpha\) is multi-tier coefficient (\(\alpha\geq 0\)), \(\beta\) reflects network nodes (\(\beta\geq 0\)), \(\gamma\) is manufacturing network links coefficient (\(\gamma\geq 0\)); \(N\) is the number of nodes; \(L\) is number of links; and \(T\) is the number of tiers.  
The fourth indicator is devoted to measuring operational complexity of manufacturing systems. This indicator is calculated using the following equation [54]:  
\[PCI=-\sum_{i=1}^{M}\sum_{j=1}^{P}\sum_{k=1}^{O}p_{ijk}\cdot\log_{2}p_{ijk} \tag{8}\]  
where \(p_{ijk}\) is probability that part \(j\) is being proceeded due to operation \(k\) by individual machine \(i\) according to scheduling order, \(O\) is the number of operations, \(P\) means the number of parts produced in manufacturing process, \(M\) is the number of all machines in manufacturing process.  
Based on the recent comparisons of the alternative complexity measures [54], indicator \(PCI\) was selected for measuring the operational process complexity.
# Modern MultiPort Converter Technologies: A Systematic Review
## 2 General Classification and Operation Principles
### Non-Isolated Multiport Converters
The topology possesses the capacity to interface three different voltages with the help of just three semiconductor switching devices. The proposed topology is examined for all operating modes in this work, and the voltage and current equations for every component are also computed. Power System Computer Aided Design (PSCAD)/Electromagnetic Transient Design and Control (EMTDC) simulation and experimental results are obtained and presented for the suggested non-isolated multiport converter.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
###### Abstract  
There is an increasing number of devices available for the Internet of Multimedia Things (IoMT). The demands these ever-more complex devices make are also increasing in terms of energy efficiency, reliability, quality-of-service guarantees, higher data transfer rates, and general security. The IoMT itself faces challenges when processing and storing massive amounts of data, transmitting it over low bandwidths, bringing constrained resources to bear and keeping power consumption under check. This paper's research focuses on an efficient video compression technique to reduce that communication load, potentially generated by diverse camera sensors, and also improve bit-rates, while ensuring accuracy of representation and completeness of video data. The proposed method applies a video content-based solution, which, depending on the motion present between consecutive frames, decides on whether to send only motion information or no frame information at all. The method is efficient in terms of limiting the data transmitted, potentially conserving device energy, and reducing latencies by means of negotiable processing overheads. Data are also encrypted in the interests of confidentiality. Video quality measurements, along with a good number of Quality-of-Service measurements demonstrated the value of the load reduction, as is also apparent from a comparison with other related methods.  
IoMT; load reduction; motion detection; Region of Motion; video compression +
Footnote †: journal: Applied Sciences
## 4 The Proposed Methodology
### The Classifier Space
#### 4.3.2 Support Vector Machines  
This is a non-probabilistic classifier. The support vector machines (SVM) are based on the linear training machines with margins [57; 58]. The linear discriminant function has the general form  
\[g(\mathbf{x})\ =\ \mathbf{w}^{\mathsf{T}}\mathbf{x}+w_{0}, \tag{10}\]  
where \(\mathbf{w}\) is a weight vector, and \(w_{0}\) is the bias or threshold weight.  
Figure 4: Block diagram of a deep DenseNet. The C within a yellow circle represents the concatenation operation.  
Figure 5: Sketch of the \(K\)-NN classifier space using five neighbors. The red star represents the test point. The black stars, squares, and diamonds are the training points.  
In the SVM methodology, each pattern \(\mathbf{x}_{j}\ =\ [S_{F},S_{CNN}]\) is transformed by  
\[\mathbf{y}_{j}=\varphi\big{(}\mathbf{x}_{j}\big{)}, \tag{11}\]  
where \(j=1,2,\ldots,N\). With an appropriate nonlinear mapping \(\varphi\), the data can always be separated by a hyperspace, Figure 6. For each of these \(N\) patterns, let be \(z_{j}=\pm 1\). A linear discriminant in an augmented \(\mathbf{y}\) space is given by  
\[g(\mathbf{y})\ =\ \mathbf{a}^{T}\mathbf{y}, \tag{12}\]  
where both the weight vector and the transformed pattern vector are augmented (compares it with Equation (10)). The first step to train an SVM is to choose the nonlinear function \(\varphi\). The goal is to minimize the magnitude of the weight vector. The problem was rewritten as maximizing the distance of the hyperplane by the Kuhn-Tucker construction,  
\[\mathcal{L}(\mathbf{\alpha}) = \sum_{i=1}^{M}\alpha_{i}-\frac{1}{2}\sum_{i=1}^{M}\sum_{j=1}^{M} \alpha_{i}\alpha_{j}z_{i}z_{j}\mathbf{y}_{i}^{T}\mathbf{y}_{j}, \tag{13}\] \[\sum_{i=1}^{M}\alpha_{i}z_{i} = 0,\quad\alpha_{i}\geq 0. \tag{14}\]  
Let \(\mathbf{K}\) a \(M\times M\) matrix with elements  
\[K(i,j)=\mathbf{y}_{i}^{T}\mathbf{y}_{j}=<\mathbf{y}_{i},\mathbf{y}_{j}>=< \varphi(\mathbf{x}_{i}),\varphi(\mathbf{x}_{j})>, \tag{15}\]  
using Equation (11). In Equation (13) is not required to know the mapping \(\varphi(\mathbf{x}_{i})\), it is enough to know the associated kernel \(K(i,j)\). Two of the most common kernels are the linear kernel (SVM-L)  
\[K(i,j)=\mathbf{y}_{i}^{T}\mathbf{y}_{j}, \tag{16}\]  
and the Gaussian kernel (SVM-G)  
\[K(i,j)=e^{-\frac{\|\mathbf{y}_{i}-\mathbf{y}_{j}\|^{2}}{2\sigma^{2}}}, \tag{17}\]  
where \(\sigma\) is the standard deviation.  
Figure 6: Sketch of the linear machine with margins. The black star and black circles represent the support vectors.
## 2 Method of Detection
It can be used as the response to a sounding signal only when the PCL input band is wide enough to cover frequencies of both the sounding signal and its second harmonic. A similar situation takes place when two-tone excitation is chosen. The only useful signal of the second-order is the summary sideband, i.e., \(\mathrm{f_{1}}+\mathrm{f_{2}}\), so the same PCL band restrictions are present. The main issue in this approach is the question of IMD generation at the input side of the LNA amplifier. The rise of an IMD product at the output of an amplifier in response to the two-tone excitation at the input is known and deeply described in the literature. There is a number of widely used parameters dedicated to describe this phenomenon, especially in telecommunication. Examples can be named as intercept point IP2, intercept point IP3, spurious-free dynamic range (SFDR) and spectral regrowth. However, all of these common parameters are dedicated to the characterization of nonlinear products in the signal spectrum at the output of the LNA. In the case of the C-PCL system, according to the proposed scheme, the key factor is to obtain the IMD products arising at the input side of the amplifier. It means that the input port, i.e., the gate-source junction in the case of field-effect transistor (FET) devices, is the origin of the IMD generation. Then, the whole C-PCL system relies on the following mechanism (depicted in Figure 4):  
* Reception of the set of sounding signals (preferably two-tone, \(\mathrm{f_{1}}\) and \(\mathrm{f_{2}}\) with equal powers) by the PCL input antenna;
* Set of sounding signals reach the LNA input;
* LNA is excited by sounding signals and produces IMD spectral components;
* IMD signals are emitted back through the PCL input antenna. Efficiency of the proposed method relies on the power of IMD products which are emitted back from the PCL front-end. This issue was verified experimentally and is shown in the further sections of the paper.
###### Abstract  
Exosomes are 30-150 nm extracellular vesicles that play crucial roles in intercellular communication and hold significant potential as biomarkers for non-invasive liquid biopsy. However, the current isolation methods have limitations including being time-consuming, producing low yields, and having high costs. This study presents a novel automated exosome isolation method using EGCG-modified magnetic beads (EGCG@T) optimized for diverse biofluids including plasma, serum, urine, and saliva. We systematically investigated the optimal EGCG:T-Fe\({}_{3}\)O\({}_{4}\) ratio (0.1:1), binding time, elution parameters, and extraction buffer composition for each biofluid type. The developed protocol was successfully integrated into an automated workflow using the Nextractor(r) NX-Junior platform, combining exosome isolation and protein extraction into a single step. Western blot and ELISA analyses confirmed that the EGCG@T method yielded a significantly higher recovery of exosomal markers (CD9, CD63, CD81, TSG101, and ALIX) compared to conventional PEG precipitation, with the efficiency varying depending on the biofluid. Notably, CD63-positive exosomes were isolated with approximately two-fold higher efficiency from urine and 1.3-fold higher efficiency from saliva using the EGCG@T method. Our findings demonstrated that biofluid-specific optimization is essential for effective exosome isolation, as exosome subpopulations exhibited distinct physicochemical properties across different sample types. This automated, rapid, and efficient exosome isolation method provides a valuable platform for future clinical applications in non-invasive disease diagnosis and monitoring through liquid biopsy.  
exosome isolation; functionalized magnetic beads; epigallocatechin gallate; automated platform; diverse biofluids +
Footnote †: journal: Applied Sciences
# Embedded Deep Learning for Ship Detection and Recognition
## 2 Related Work
### Vehicle Identification  
There are many state-of-the-art algorithms that can be used for vehicle detection, such as RCNN [9] YOLO [10], which have high real-time performance, but the accuracy is not high for ship recognition. In [12], [PERSON] proposed a vehicle real-time detection algorithm based on YOLOv2. It optimized important parameters of the model, and improved the number and size of anchors in the model, which can achieve both real-time and high accuracy for vehicle detection. It tested by a home-made dataset, which showed higher accuracy and ran faster than YOLOv2 [11] and RCNN. However, the real-time performance is based on high-performance equipment, which is not suitable for us.  
Plate recognition is the most typical application for vehicle identification. [PERSON] et al. [13] proposed CognMem, which used a neural-network chip to recognize license plates. The CogniMem combined a video image processing module with a neural network module by using an equalized image processing algorithm and network classification algorithm. It contained processes of license location, character segmentation and character recognition. CogniMem can recognize car plates with low error; however, it required that the plates have a fixed character position and limited character type and numbers. [PERSON] [14] proposed a method named ALPR to detect and recognize the characters in the plate region of an image. The approach is not applicable to the situation in which new targets emerge that are not annotated in its database.
# Prism-Based Spatial Heterodyne Spectrometer with a Fixed Fringe Localization Plane
## 4 Conclusions  
In this paper, we address the problem in current designs of prism-based SHSs: the FLP moves with changes in wavelength. We propose a design of a prism-based TSHs that maintains a constant FLP location and has a dual output system, utilizing a prism-bimirror-mirror structure. The theoretical spectral resolving power is higher than 1300 in the spectral range from 10,000 cm\({}^{-1}\) to 25,641 cm\({}^{-1}\) and higher than 10,000 in the spectral range from 20,000 cm\({}^{-1}\) to 25,641 cm\({}^{-1}\), based on an example. Additionally, we design an  
Figure 7: Interference curve of the on-axis rays (solid line) and the limiting off-axis rays (dashed line).  
orthogonal TSHS in the form of a Mach-Zehnder interferometer to simplify the motion control system. Given its ability to retain the advantages of prism-based SHSs as well as to eliminate the need to calculate the FLP location and move the imaging device based on the predicted wavelength of incident light during use, this optical implementation shows good potential for the further development and practical application of SHS, particularly when a broadband spectrum is required. Moreover, methods to improve the FOV and spectral resolving power of prism-based SHSs need further research.  
Conceptualization, Z.L. and D.Z.; methodology, Z.L. and D.Z.; software, Z.L.; validation, Z.L. and C.H.; formal analysis, Z.L., H.Y. and D.Z.; investigation, Z.L. and D.Z.; resources, H.Y. and C.H.; data curation, C.H. and H.Y.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L. and D.Z.; visualization, H.Y. and Z.L.; supervision, C.H.; project administration, D.Z.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) project (No.41405021) and China Manned Space Engineering Project (No.GE1215).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution
## 5 Conclusions  
In this paper, we propose convolutional-autoencoder-network-based underwater acoustic FBMC communications, which uses an encoder and decoder to partially replace the functional modules of the transmitter and receiver in original communication system. In particular, dense connection is adopted between Conv1D layers in the decoder to further improve the detection capacity, which inherits the features of CNN, such as feature learning, fast training convergence, feature reuse to enhance feature extraction, etc. The end-to-end structures can perform the modulation and demodulation tasks jointly. The simulation results show that the proposed method exhibits better system performance than that of the conventional method under the same conditions. The deep-learning-based model effectively optimizes the system structure and achieves more efficient and reliable transmission, which is of great significance to the future development of underwater communication.  
Conceptualization, F.X.; methodology, F.X. and B.W.; software, Y.Z.; validation, F.X., Y.Z. and W.W.; formal analysis, B.W.; investigation, P.J.; resources, Y.Z.; data curation, F.X.; writing\(-\)original draft preparation, F.X.; writing\(-\)review and editing, Y.Z. and B.W.; visualization, F.X.; supervision, F.X.; project administration, Y.Z.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 52071164 and in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant SJCX22_1893.  
The data presented in this paper are available after contacting the corresponding author.  
The authors would like to thank the anonymous reviewers for their careful reading and valuable comments.  
The authors declare no conflict of interest.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 6 Measurements
### Photodetector  
Thorlabs PDA36A-EC was used as a receiving element. It was a Si PIN photodetector with switchable gain operating at 350 to 1100 nm. It offered a bandwidth of up to 10 MHz and a peak sensitivity at 970 nm. Various lens tubes could be mounted onto the thread coupler to direct light at the receiving chip itself. However, during the measurements, the lenses were not used, as it was necessary to simulate the receiving elements (such as mobile phones).