esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Knowledge-Defined Networking
## 9 Conclusions, Recommendations, and Future Research
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Research Area** & **Organization** & **Site Address** \\ \hline \multirow{3}{*}{Stratum project} & Programmable data plane, intent-based networking, network automation, network security, network telemetry & \multirow{3}{*}{ONF} & [[https://opennetworking.org/stratum/](https://opennetworking.org/stratum/)]([https://opennetworking.org/stratum/](https://opennetworking.org/stratum/)) (accessed on 8 April 2023) \\ \hline \multirow{3}{*}{LSO Sonata} & AI/ML network service orchestration, network slicing, intent-based networking, edge computing & \multirow{3}{*}{MEF} & [[https://www.mef.net/service-automation/iso-apis/inter-provider-apis/iso-sonata/](https://www.mef.net/service-automation/iso-apis/inter-provider-apis/iso-sonata/)]([https://www.mef.net/service-automation/iso-apis/inter-provider-apis/iso-sonata/](https://www.mef.net/service-automation/iso-apis/inter-provider-apis/iso-sonata/)) (accessed on 8 April 2023) \\ \hline \multirow{3}{*}{ITU-T group 13} & Next-generation networks, future networks and emerging technologies, cloud computing & ITU-T & [[https://www.itu.int/en/ITU-T/about/groups/Pages/sg13.aspx](https://www.itu.int/en/ITU-T/about/groups/Pages/sg13.aspx)]([https://www.itu.int/en/ITU-T/about/groups/Pages/sg13.aspx](https://www.itu.int/en/ITU-T/about/groups/Pages/sg13.aspx)) (accessed on 8 April 2023) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._* Explainable machine learning is recommended to make sure that the ML models are accountable and explainable for the decisions they make and to aid in identifying the root causes of issues when such models make errors;
* Transfer learning and active learning are recommended to fine-tune the machine learning models' training. Quantum machine learning is currently generating a surge of interest among researchers. It can be applied to KDNs to improve their performance, especially their computational performance, with the aid of quantum computing. By assisting quantum algorithms in KDN, this can open directions for new research and the development of innovative applications due to its performance advantage. Furthermore, future research can involve developing techniques for secure federated learning of machine learning models in a complex KDN environment. Conceptualization--P.A.D.S.N.W. ; software--P.A.D.S.N.W. ; validation--P.A.D.S.N.W. ; formal analysis--P.A.D.S.N.W. ; investigation--P.A.D.S.N.W. ; resources--P.A.D.S.N.W. ; writing original draft preparation--P.A.D.S.N.W. ; writing review and editing--S.G.; visualization--P.A.D.S.N.W. and S.G.; supervision--S.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This research is part of a research project that principal and corresponding author Patikiri Arachchige Don Shehan Nilmantha Wijesekara is pursuing for a Degree of the University of Ruhuna. The authors declare no conflict of interest.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 3 Types of LiDARs
### Full-Waveform LiDAR  
Full-waveform LiDAR is a type of LiDAR that records the complete backscattered energy profile of a laser pulse as it interacts with surface and vegetation targets. Unlike the classical altimeter LiDAR systems, which only capture a limited number of return points, full-waveform LiDAR provides a continuous record of the vertical distribution of targets within the laser footprint, enabling detailed characterization of the target such a forest canopy structure, sub-canopy layers, and ground elevation [100; 101].
# Selection of Grid Road Networks Based on Raster Data
## 3 Method for Multiscale Representation of High-Density Transportation Networks  
The proposed ARWS method for road networks uses the hierarchical representation of dense raster datasets across varying scales in three main steps: the multilevel division of raster road networks based on the Linear Spectral Clustering (LSC) method; weighting the multilevel superpixels by considering the semantic characteristics of the roads, such as the structure, connectivity, and centrality; identifying roads through the prioritization of superpixels by weight and the removal of extraneous pixels at road junctions. Each step has different technical support, such as neighborhood analysis, graph theory, and minimum spanning trees.
# Recent Developments of Solar Cells from PbS Colloidal Quantum Dots
## 4 PbS Quantum Dot Sensitized Solar Cells
They synthesized these quantum dots by air-stable thiourea, stabilized by Cd post-treatment via cation exchange, and found an increased performance of solar cells sensitized with these quantum dots, as compared to devices produced with quantum dots synthesized from common bis(trimethylsilyl) sulfide [78]. Core/shell PbS/CdS quantum dots were applied as photosensizers on TiO\({}_{2}\) to avoid charge recombination inside the quantum dots and at the quantum dot/TiO\({}_{2}\)/electrolyte interfaces, resulting in a significant increase of the power conversion efficiency in comparison with pristine PbS-based quantum dot sensitized solar cells [79]. [PERSON] et al., who synthesized PbS quantum dots with embedded CuS, reported even higher efficiencies, which were explained by reduced charge carrier recombination and improved absorptivity [80]. Figure 7: UV/Vis absorption spectra after soaking identical TiO2 layers in solutions of oleic acid capped CdSe colloidal quantum dots. From [74], originally published under a CC-BY license. Going beyond the pure function of PbS quantum dots as sensitizers, [PERSON] et al. also used them as carrier transporters. They prepared a heterostructure from PbS quantum dots as absorbers and hole carriers with ZnO nanowires as electron carriers, and showed efficient charge collection along this hybrid structure, which was attributed to band bending in the ZnO dealing as collector, resulting in efficient charge separation and thus large hole transportation distances [81]. Generally, PbS quantum dots show highly interesting sensitizing properties due to the different possibilities to modify their absorption spectra. In addition, they can even be used as charge carriers, allowing for combining two purposes in one material layer. After this overview on PbS quantum dots as sensitizers in quantum dot sensitized solar cells, the next section will report on further ideas beyond Schottky type and DSSC type solar cells, often combining different physical and chemical properties of PbS quantum dots.
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model  
[PERSON]  
1 Department of Geoinformatics, University of Seoul, Seoul 02504, Korea; [EMAIL_ADDRESS] (S.-J.H.); [EMAIL_ADDRESS] (W.-K.B.)  
2  
[PERSON]  
1 Department of Geoinformatics, University of Seoul, Seoul 02504, Korea; [EMAIL_ADDRESS] (S.-J.H.); [EMAIL_ADDRESS] (W.-K.B.)  
2  
[PERSON]  
1 Department of Geoinformatics, University of Seoul, Seoul 02504, Korea; [EMAIL_ADDRESS] (S.-J.H.); [EMAIL_ADDRESS] (W.-K.B.)  
2  
Footnote 1: email: {keong-jae.hong2020  
be easily identified by a person. However, owing to the requirement of sunlight, images can only be captured by an optical sensor during daytime. Further, high-quality images cannot be captured if the weather conditions are bad, even during daytime. In contrast, because the SAR sensor generates an image by emitting a signal from the sensor and detecting the reflected signal from an object, images can be generated even at night [2,6]. In addition, because the emitted signal for ship detection is not affected by weather conditions, it is useful for ship detection research [2,7,8].  
Recently, various ship detection studies focused on the deep learning model structure rather than improving the quality of images [9, 10, 11, 12]. In contrast, the present study focuses on the preprocessing technique of SAR images to improve ship detection performance. First, the value unit of the SAR images was converted to decibels to present a normal distribution of the values. Then, noise in the images that can interfere with ship identification was reduced by using a multi-looking technique and a median filter [13]. Moreover, for better contrast between the ship and the sea, images were created with only sea surface image data remaining. First, a median filter was used having a size sufficiently larger than the size of the ship in the images [13]. Noise in the entire image was reduced, and the contrast between the sea and the ship was increased by separating the filtered images from the existing SAR images. In addition to the increased contrast, it is expected that ships can be detected more easily by changing the state of the sea surface in all images. The preprocessed images were converted into training data and test data through visual analysis and comparison using Google Earth.  
The purpose of this study is to detect ships in SAR images using deep learning. We used an M2 Det [14] deep learning model for detecting ships in SAR images. To enhance detection accuracy, we proposed a preprocessing method for SAR images and compared its detection results to others from different preprocessed images. The ship detection performance was expressed in terms of precision, recall, and average precision (AP) [15, 16, 17]. By using the proposed preprocessing method, the trained M2 Det deep learning model could achieve highest accuracy (95.55% AP).
# Electrode Kinetics of Ion Jelly and Ion Sol-Gel Redox Materials on Screen-Printed Electrodes
## 3 Results and Discussion
### Electrochemical Characterization of IJ and ISG
They covalently bonded the IL to a TMOS silica network by using an alkylimidazolium cation with an alkoxide group, which participated in the creation of the silicate matrix. Kinetic studies using ferrocyanide in a tin oxide electrode with this sol-gel showed intense peak currents and a voltammogram typical of a reversible process [57, 58]. Regarding the determination of k\({}^{0}\), SPE-IJ had \(\Delta\)Ep < 200 mV for all scan rates tested, so [PERSON]'s method was enough to calculate the kinetic parameter \(\Psi\). Both SPE-ISG and SPE had peak separations outside [PERSON]'s working curve (Figure 5), so the suitable equations were used to cover the whole range of scan rates [59]. From the slopes of \(\Psi\)-\(\
u\)-1/2 plots [59], values for k\({}^{0}\) were obtained for each of the electrode types (Table 2). The charge transfer coefficient for electrodes featuring reversible and quasi-reversible behavior (SPE and SPE-IJ) was considered 0.5, in accordance with the criteria of [PERSON]'s working curve and some \(\alpha\) values found in the literature for the Fe(CN)\({}_{6}\)\({}^{3-}\)/Fe(CN)\({}_{6}\)\({}^{4-}\) redox pair [60]. With k\({}^{0}\) in the order of 10\({}^{-3}\) and 10\({}^{-2}\) cm\(\cdot\)s\({}^{-1}\), the redox reactions in the SPE and SPE-IJ fell into quasi-reversible kinetics, with the process that occurs with ion jelly being closer to the reversibility limit, which was in agreement with the initial observations of cyclic voltammograms. In SPE-ISG, the k\({}^{0}\) of 10\({}^{-6}\) cm\(\cdot\)s\({}^{-1}\) fell \begin{table}
\begin{tabular}{c c c c} \hline
**Electrode Type** & **D\({}_{\text{O}}\) (cm\({}^{2}\)\(\cdot\)s\({}^{-1}\))** & **D\({}_{\text{R}}\) (cm\({}^{2}\)\(\cdot\)s\({}^{-1}\))** & **k\({}^{0}\) (cm\(\cdot\)s\({}^{-1}\))** \\ \hline SPE & \((1.57\pm 0.14)\times 10^{-5}\) & \((1.38\pm 0.01)\times 10^{-5}\) & \((1.03\pm 0.03)\times 10^{-3}\) \\ SPE-IJ & \((3.07\pm 0.56)\times 10^{-7}\) & \((2.29\pm 0.62)\times 10^{-7}\) & \((1.54\pm 0.38)\times 10^{-2}\) \\ SPE-ISG & \((1.73\pm 0.35)\times 10^{-9}\) & \((4.79\pm 1.37)\times 10^{-10}\) & \((7.53\pm 0.11)\times 10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Diffusion coefficients and electron transfer rate constants for the three electrode types used. Values are averages of all cycles and errors are the standard deviation. Figure 4: Anodic and cathodic peak currents as a function of the square root of the scan rate. Values are averages from the three cycles and error bars are the standard deviation: (**A**) SPE, (**B**) IJ-modified SPE and (**C**) ISG-modified SPE. squarely within the irreversible region, validating the initial assumption made. Although the ISG material was found to have a lower diffusion and rate transfer constant when compared with IJ and other materials, that was not a deterrent to its use in real applications, such as drug delivery.
## 3 The AgroTRACE Infrastructure
### Event Capturing and IoT Application Platform
#### 3.1.1 Event Capturing Applications  
The event capturing applications deal with visual reading by utilizing smart devices for:  
* Monitoring the route from the field to the packaging plant (see also Figure 2). During harvest, the numbers of the perishable products, i.e., vegetables and fruits, are scanned via the GRAI application and imprinted in barcodes or QR codes on pallets.  
Figure 3: Simplified graph of the infrastructure of AgroTRACE system showing its main three elements and the involved IoTs.  
The same batch numbers are scanned during entering the packaging plant and, in particular, at the sorting line and at the temporary storage point. These numbers are linked to the corresponding field's and packing GLN numbers as well as through the sorting line systems by using the GTIN numbers of the available products and SSCC numbers of the pallets. Concerning the former, the extended form, namely GTIN-128, is used which contains information of different batches.
* Management purposes in the retail point. The customer is able to scan the number of the fresh product by using the GTIN-128 application. The numbers are imprinted in QR codes or data bars, thus allowing for access to its history. Thus, the product can be tracked after the shelf of store, until the refrigerator of the consumer.
## 4 Proposed Deep Learning-Based Patient Load Prediction Model
### Decentralized Patient Load Prediction System
#### 4.2.1 Data Collection  
In the centralized system above, the Cc predicts the patient load based on collected patient flow patterns of all health facilities. However, in the decentralized system, the Cc has limited capabilities, such that each facility does not transfer all the raw patient flow data to the Cc. Here, each facility performs some pre-processing of the raw patient flow data and sends limited/less information to the Cc, hence reducing the computational and signaling overheads of the Cc. Each facility \(i\) captures the patient load \(ps^{i}_{k}\) of the previous time interval and also separately captures the relayed patient load \(PL^{i}_{rel}\) and the regular patient load from its catchment area as \(PL^{i}_{reg}\) of the last \(N\) time slots. Using \(PL^{i}_{reg}\) as the input, each facility \(i\) predicts the future regular patient load \(psf^{i}_{reg_{k+1}}\) of the next time slot. The training and prediction process is conducted in the training phase. Thereafter, each facility forwards the obtained \(psf^{i}_{k+1}\) and the recorded patient load of the previous time slot \(ps^{i}_{k}\) to the Cc. The received information from all health facilities is then constructed by the Cc as the training data \(ps_{k}\) and \(ps_{reg_{k}}\).
## 3 Results
### FDTD EM Simulation: \(\text{B}_{1}^{+}\) Mapping  
Figure 4 and Table 2 present the FDTD EM \(\text{B}_{1}^{+}\) simulation results. In case 1, the uniformity of the \(\text{B}_{1}^{+}\) map of the fat phantom in the ROI was 85.0%. In case 2, the uniformity of the \(\text{B}_{1}^{+}\) map of the fat phantom in the ROI using body coil transmission and reception with the 16-leg \({}^{13}\)C PFT birdcage RF coil, containing the CPSCs, was 84.9%; the uniformity reduction between cases 1 and 2 was 0.10%. In case 3, the uniformity of the \(\text{B}_{1}^{+}\) map of the fat phantom in the ROI using body coil transmission and reception with the 16-leg \({}^{13}\)C birdcage RF coil without CPSCs was 75.3%, indicating a significant uniformity reduction of 9.70% when compared to that in case 2.  
In cases 1, 2, and 3, the average intensities of the \(\text{B}_{1}^{+}\) field of the fat phantom in the ROI were 0.417 uT, 0.415 uT, and 0.148 uT, respectively. Case 2 demonstrated an intensity reduction of 0.43% compared to case 1. However, case 3 demonstrated a significant intensity reduction of 64.5% when compared with case 1, as shown in Table 2.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 5 Discussion  
### Green Tide Traceability  
Due to the influences of temporal and spatial resolution, cloud, rain, fog, and other weather factors, the earliest time range for green tide monitoring by RS is generally from mid-late May to early June [25; 39], and the traceable range is generally limited to the Subei shoal along the Jiangsu coast. To obtain an earlier time range for monitoring and a wider range of green tide traceability locations, the newly proposed green tide extraction algorithm was used to extract data on the Yellow Sea green tide from 2008 to 2022 through multi-source high-resolution RS images, and the source of the Yellow Sea green tide was successfully traced. The main data of the high-resolution RS satellites used in this study and their in-orbit operation times are shown in Table 9. HY-1C and GF-1 advanced the source tracing time to early April (as shown in Table 9), and the overall tracing time of the multi-source satellite images was advanced to early May. We used multi-source high-resolution RS data to trace the source of the Yellow Sea green tide as far as possible, with a spatial resolution between 16 m and 50 m. This resolution range can monitor part of sporadic floating _U. prolifera_, as shown in Figure 8. Even if we cannot monitor the very early process of the green tide, at least we will be able to monitor the green tide when it starts to take shape.  
After extracting the earliest distribution data of the Yellow Sea green tide in all years (2008-2022), the central location was defined as the source location of the green tide, as shown in Figure 15a. The source locations were mainly clustered in two regions, and the k-means clustering algorithm was used to classify the central locations. The k-means clustering is a method of cluster analysis that is aimed at partitioning the data into k clusters, where each datum belongs to the cluster with the nearest mean [40]. This algorithm is widely used in spatial clustering analysis [41]. The source locations were classified into two categories using the k-means clustering algorithm (as shown in Figure 15b). Based on the tracing results, a source was also found in the southeastern Yellow Sea (blue box), in addition to the Subei shoal along the Jiangsu coast (the most commonly attributed source; red box). The sources of the Yellow Sea green tide were further clarified, which may guide the early prevention, control, and targeted management of the Yellow Sea green tide.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**High-Resolution RS Data Source** & **In-Orbit Operation Time** \\ \hline Landsat5 & March 1984–June 2013 \\ HJ-1 & September 2008–Present \\ Landsat8 & February 2013–Present \\ GF-1 & April 2013–Present \\ HY-1C & September 2018–Present \\ \hline \hline \end{tabular}
\end{table}
Table 9: In-orbit operation times of high-resolution RS satellites.
## 2 Related Work
### Vehicle Detection
#### 2.1.1 Feature Based Method  
Prior to convolutional neural networks (CNNs) [10], feature-based vehicle detection was widely used as a method to detect objects based on abstractions of image data and a type-based judging system. A popular method for classifying road users is optical flow [11]. The method looks for consecutive matches between image pixels to calculate displacement vectors. Additionally, support vector machines (SVM) [12], histograms of oriented gradients (HOG) [13], and scale-invariant feature transforms (SIFT) [14] can be used.  
It is generally accepted that feature-based detection models are classic methods based on relatively simple architecture and that their performance, efficiency, and robustness can not be compared with the more recent deep learning models.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)
### Principles of Convolutional Neural Networks (CNN)  
One deep learning model that is particularly good at handling images and spatiotemporal data is the Convolutional Neural Network (CNN). Fundamentally, it uses convolutional procedures to extract local characteristics from the input data. The input layer, convolutional layer, pooling layer, fully connected layer, and output layer are the components of a basic CNN. Each element of the input layer's a m \(\times\) n matrix has a feature value, and the input data is shown as a 2D feature map. The three main parts of a CNN are the convolutional layer, the pooling layer, and the fully connected layer. Multiple convolutional kernels are present in the convolutional layer. A set of convolutional kernels can be used to capture local representations between slippery-slope feature vectors, and additional convolutional layers can be employed to represent more complex features and iteratively learn from low-level features. In contrast, the pooling layer uses downsampling to reduce the quantity of feature information and the dimensions of the feature map, which enhances the model's computational efficiency and lowers the possibility of overfitting. The final classification or regression operation is carried out by the fully connected layer, which combines the local features extracted by the convolutional and pooling layers into a global feature representation. The fully connected layer enhances the model's performance by capturing the intricate patterns and correlations of the input data through the learning of weights and biases.  
Rich spatial information, including topography, climate, soil characteristics, etc., is typically present in the raw data used for landslide prediction. CNN uses a convolution technique to extract valuable spatial characteristics from the raw data. Let \(X\in R^{H\times W\times C}\) be the input data, where H, W, and C stand for the data's height, width, and number of channels, respectively. The following formula can be used to define the output feature map of the convolution operation \(Y\in R^{H^{\prime}\times W^{\prime}\times K}\):  
\[Y_{i,j,k}=\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}\sum_{c=0}^{C-1}X_{i+m,j+n,c}\cdot W _{m,n,c,k}+b_{k} \tag{1}\]where \(Y_{i,j,k}\) is the output features following the convolution process, \(W\) is the convolution kernel, and \(b_{k}\) is the bias term. The CNN can efficiently capture the local spatial properties of the input data and produce more insightful inputs for time series modeling by using multi-layer convolution and pooling operations.