esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1
## 1 Introduction  
DNA sequences of living organisms contain information that create proteins. An important step in the creation of proteins from DNA is the transcription step. Transcription is the process of copying the information of a gene's DNA strand into a messenger RNA (mRNA) molecule [1].  
An enzyme called RNA polymerase and proteins called transcription factors (TFs) carry out the transcription process. When a gene is to be transcribed, the enzyme must bind itself to the DNA of the gene at a specific sequence called the \"promoter\" sequence. This is done with the help of the transcription factors. Both the enzyme and the transcription factors start the transcription process after binding to the promoter site and terminate the transcription once the mRNA strand is completed. The generated mRNA copy serves as a blueprint for protein synthesis [1; 2; 3].  
Most of the transcription factor binding sites are close to the gene's promoter. However, due to the repetitive nature of DNA sequences, the binding sites could also be found at a further point or at multiple locations in the DNA, while still affecting the transcription of that gene. Accurate prediction of binding sites for TFs is a known problem that is still challenging in the computational biology field due to its sequence variation.  
Transcription factor binding sites play a key role in gene expression. Therefore, to understand gene regulation networks, it is obligatory to understand transcription factor binding at the genome scale. Moreover, transcription factor binding sites play an important role in drug design.  
The problem targeted in this paper concentrates on the transcription factor binding sites of SP1 on human chromosome 1. SP1 is a transcription factor protein coding gene. This gene is responsible for a number of diseases, among which is Huntington Disease and Embryonal Carcinoma.  
Supervised machine learning techniques could be used to extract knowledge from known binding and non-binding sequences to predict whether a given new sequence is also a binding site or not. This is considered as a binary classification problem, in which there are only two possible classes for the result. In the following section, a brief background about machine learning techniques is given, followed by related work in the prediction of transcription factor binding sites.
## 5 Simulation Results and Discussions
### Impact of CBR Connections
(**c**) Impact of CBR connections on routing efficiency. (**d**) Impact of CBR connections on throughput. As illustrated in Figure 6d, the overall throughput increases steadily with the number of CBR communication pairs. This growth is attributed to the elevated traffic activity and improved resource utilization across the network. Throughout the process, Traj-Q-GPSR consistently achieves higher link utilization efficiency by leveraging future trajectory awareness and Q-learning-based routing decisions, particularly under high-traffic conditions. In contrast, GPSR exhibits significantly lower throughput. When the number of communication pairs reaches 50, the throughput of Traj-Q-GPSR reaches approximately \(4.3\times 10^{5}\) bps, which is double that of GPSR at only \(2.14\times 10^{5}\) bps. The performance gap widens as the traffic load increases, confirming the superior adaptability and scheduling efficiency of Traj-Q-GPSR in complex communication scenarios.
# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 2 Related Works
### Current Issues in Object Detection for Fisheye Images  
Currently, object detection in fisheye images still faces many challenges. For example, there are fewer publicly available fisheye image datasets in real scenes, and object distortions and discontinuities in fisheye images suffer from the detection performance of object detectors. In this paper, the following improvements are made to address these challenges:  
1. The UAV-360 dataset is captured using a UAV-mounted fisheye camera, which contains 2045 equirectangular projection images converted from fisheye images. This dataset is beneficial to enhance the practical study of object detection in fisheye images.
2. Standard convolution makes it challenging to recognize the distorted and discontinuous objects in fisheye images, so two novel downsampling modules are proposed for the characteristics of fisheye images. The Max Pooling and Ghost's Downsampling (MPGD) module effectively extracts the essential feature information of distorted and discontinuous objects. Meanwhile, the Average Pooling and Ghost's Downsampling (APGD) module obtains rich global features and reduces the feature loss of distorted and discontinuous objects. In addition, compared to the convolutional layers in the downsampling stage of the original YOLOv8s, the two downsampling modules slightly reduce the parameters and computation of the model.
3. The complex background information in fisheye images degrades object detection performance in fisheye images. The proposed C2 fs module uses the Squeeze-and-Excitation (SE) block to model the interdependencies between channels and to obtain richer information about the feature gradient flow. The C2 fs module provides a better understanding of the contextual information in fisheye images. Meanwhile, the SPPFSE module adds an SE block after Spatial Pyramid Pooling Fast (SPPF). It uses global information to selectively emphasize basic features, thus improving the model's ability to capture distorted and discontinuous features.
4. The proposed PGDS-YOLOv8s model is a lightweight network structure that applies the above-proposed modules to the YOLOv8s model. The PGDS-YOLOv8s model effectively solves the problem of missed and wrong detection of distorted and discontinuous objects and obtains excellent object detection performance in fisheye images.
## 2 Materials and Methods
### Imaging System
The Telemed SmartUS works with EchoWave II scanning software. The system can be automated using the windows WM_COPYDATA messages, while the UR-3 robot can communicate with the software in several different ways. In the test system, communication through the XML-RPC server has been used. XML-RPC is remote procedure call and allows calling the function implemented on the controller side and exchange data with the robot. Using these two methods of communication, the UR-3 Robot Program has been written, so the robot could communicate with the XML-RPC server and call proper functions that allowed it to initialize the system, collect data from the user, and acquire images. The use of this solution is convenient in terms of synchronization, as the robot waits until the function called via the server is finished. The XML-RPC server can be implemented in many modern programming languages. The Python program has been created to control and synchronize the Telemed EchoWave II Imaging software and the UR-3 robot using WM_COPYDATA and XML-RPC capabilities. Software communication is schematically shown in Figure 4.  
The program has been designed to allow for controlling the position of the probe with respect to the center of the system geometry, the angle of the probe relative to the \(Z\)-axis, and the number of images. The UR-3 Robot program has been written so the robot could communicate with the XML-RPC server to call proper functions on the server side (Python program). This allows us to initialize system with the number of scans to be acquired (_numOfScans_()), angle of which the probe is adjusted (_angle_()), and offset of the probe from the center (_offset_()). Data gathered from the user are received by the robot being returned by those functions. The image in a certain position is acquired by calling the _position()_ function by the robot, which sends a message to the EchoWave II to save the current image to a file, and the function returns the next position of the robot. Robot payload initialization has to be performed manually by the user before measurement starts. Figure 4: UR-3 robot and EchoWave II control software communication scheme. Figure 3: The ultrasonic probe of a conventional B-mode ultrasound scanner attached to the robotic arm.
## 1 Introduction
Based on these previous studies on GCPs, it is understood that the GCP distribution strategy is effective in improving localization accuracy in various mapping and surveying applications using aerial [16; 17; 29] or satellite imagery [30; 31]. GCPs play an important role in minimizing errors originating from the GNSS receiver, topographic changes, and the sensor. Since marking a large number of GCPs requires high labor, cost-effective solutions need to be created to determine the appropriate distribution and number. On the other hand, further studies are needed to investigate the impact of GCP deployment on UAV-based photogrammetry applications in large urban areas. This study investigates the integration of GCP distributions with different positioning techniques for UAV-based photogrammetry. It also investigates the accuracy of the RTK, PPK, and PPP-AR techniques in terms of producing orthomosaic and DEM. GCP distribution models were analyzed in the urban area based on six different scenarios. These three direct georeferencing methods are compared with the indirect georeferencing method. The contribution of the study is enhanced by the establishment of distribution models,especially in urban areas, the comprehensive evaluation of the selection of appropriate GCPs, and the integration of precise positioning techniques and GCPs.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 3 Results
### Comparison with Other Satellites
#### 3.3.1 Comparison with Earth Observation Satellites
Figure 12: TVDI drought classification map calculated by FY-4A and Landsat-8 in Huanghuai region: FY-4A (**e**–h); Landsat-8 (**a**–**d). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Num of Images** & **Standard Deviation** & **RMSE (with FY-4A)** & **Value Range** & **Processing Time (min)** \\ \hline MODIS & 4/2 days & 0.15 & 0.26 & [0.13, 0.97] & 5.56 \\ Landsat-8 & 35/16 days & 0.35 & 0.13 & [0.08, 0.93] & 17.31 \\ FY-4A & 4/h & 0.26 & - & [0.02, 0.98] & 3.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical results of the TVDI produced by FY-4A, MODIS, and Landsat-8.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 3 Wavelet-Based, Blur-Aware Decoupled Network  
### Network Overview  
As shown in Figure 1, given a sequence of blurred video frames (\(\{l_{t}^{Blur}\}_{t=1}^{N}\)), our goal is to reconstruct the corresponding sharp video frames (\(\{l_{t}^{Sharp}\}_{t=1}^{N}\)). The proposed network architecture consists of two main branches: a preprocessing branch and a feature reconstruction branch. The preprocessing branch estimates inter-frame optical flow and sharp maps to provide prior information for subsequent deblurring. In contrast, the feature reconstruction branch employs a wavelet transform-based design to recover structure and detail information from low-frequency and high-frequency sub-bands, respectively.  
In the preprocessing branch, we first utilize an optical flow estimation network [43] to compute motion information between adjacent frames in a video sequence. Specifically, for any frame (\(I_{i}^{Blur}\)), we calculate the optical flow (\(\{O_{i\to t}\}_{t=1,t\
eq i}^{N}\)) between this frame and others. Considering that motion is the primary cause of blur, with higher motion corresponding to increased blur, we use optical flow between adjacent frames to estimate the blur map (\(B_{i}\)) for video frame \(I_{i}^{Blur}\):  
\[B_{i}=(O_{i\to i+1})^{2}+(O_{i\to i-1})^{2} \tag{1}\]  
For the first and last frames of the video sequence, we set \(B_{1}=2(O_{1\to 2})^{2}\) and \(B_{N}=2(O_{N\to N-1})^{2}\), respectively. Subsequently, we normalize the blur map (\(B_{i}\)) to the \([0,1]\) interval to obtain the normalized blur map (\(B_{i}^{\prime}\)) and corresponding sharp map (\(S_{i}\)):  
\[B_{i}^{\prime}=\frac{B_{i}-\min(B_{i})}{\max(B_{i})-\min(B_{i})} \tag{2}\]  
\[S_{i}=1-B_{i}^{\prime} \tag{3}\]  
In the feature reconstruction branch, we first extract shallow features (\(\{F_{t}\}_{t=1}^{N}\)) from video frames through several residual blocks. We then use wavelet transform to decompose these features into one low-frequency sub-band (\(\{F_{t}^{LL}\}_{t=1}^{N}\)) and three high-frequency sub-bands (\(\{F_{t}^{HL}\}_{t=1}^{N}\), \(\{F_{t}^{LH}\}_{t=1}^{N}\), and \(\{F_{t}^{HH}\}_{t=1}^{N}\)). To achieve multi-scale feature representation, we further decompose the low-frequency sub-band (\(\{F_{t}^{LL}\}_{t=1}^{N}\)) into corresponding low-and high-frequency sub-bands.  
We design specialized processing modules based on the characteristics of different frequency band features. For low-frequency features, we develop the multi-scale progressive fusion (MSPF) module to handle structural reconstruction. For high-frequency features, we create the blur-aware detail enhancement (BADE) module to focus on detail recovery. This decoupled design enables the network to process frame features at different scales more specifically.
## 5 Conclusions
### Limitations  
This study has several limitations that warrant further discussion in subsequent research: (1) Mountain settlements represent a complex three-dimensional system, and the spatial morphology of these settlements holds research value in the vertical dimension as well. Some scholars have employed three-dimensional models to calculate fractal dimensions for analyzing spatial morphology in this context. However, this study exhibits shortcomings in its exploration along the z-axis; future research will establish three-dimensional models of settlements and compute their fractal dimensions for analysis. (2) The development of villages is a protracted process, and changes in settlement spatial morphology over time, as well as the influence of terrain on this morphology, should be considered temporally. Future studies will sample different significant morphological nodes during the historical formation process of settlements and employ Geographically Weighted Temporal Regression (GTWR) for analysis [44]. (3) The analysis conducted herein regarding the fractal dimensions of subspaces within a settlement is limited to basic visualizations without delving into multifractal analyses. As a function, multifractals can provide deeper insights into the spatial organizational patterns of such systems [45]. Subsequent research will focus on analyzing multifractal functions to achieve a more profound understanding of settlement spatial morphology. (4) The current analysis is somewhat constrained by its limited scope; to summarize mountain settlement spatial morphologies comprehensively, it is essential to increase the sample size for further investigation. (5) The spatial morphology of mountainous settlements cannot be solely attributed to topographical factors. Future research should incorporate considerations of social factors and other natural factors to provide a more comprehensive analysis. This approach would help to uncover the dynamic balance between geographical constraints and socio-economic factors, thereby offering deeper insights into the interplay between natural and human influences.  
Conceptualization, S.P. and J.W.; methodology, S.P.; software, S.P.; formal analysis, S.P.; investigation, S.P. and J.W.; resources, S.P.; writing--original draft preparation, S.P. and W.W.; writing--review and editing, S.P., W.W. and J.W.; visualization, S.P. and W.W.; supervision, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Social Science Fund is a special project for unpopular research, grant number 23 VJXG030, and the Central Government Guides Local Science and Technology Development Fund Projects, grant number YDZJSX2021A017.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Some of the research materials and data used in this paper are available on the internet and through numerous public channels. Additionally, for more detailed information and data, please contact the author at [EMAIL_ADDRESS].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Simulation and Performance Evaluation
### Comparison with VGDRA
#### 4.3.1 Source Nodes Are Distributed Unevenly in the Network  
In this part, the application scenario in which source nodes are distributed in a small area of network is set. The parameters we set is shown as follow: (1) the number of nodes varies from 200 to 600; (2) \(r_{e}\) is 30 m; (3) \(Th\) is 70%; (4) the event area changes by period, and the period is 800 s; (5) one source node produces one data packet in one second; (6) the velocity of sink is 5 m/s. Figure 20a shows the impact of nodes' number on the lifetime of network. As expected, the lifetime of our strategy is far greater than VGDRA's. That is because the sink in our strategy can move toward the event area. However, in VGDRA, sink always moves along the fixed trajectory no matter where the event area is. The network of our strategy gets the maximum value when nodes number is 500. With the increase of nodes' number, the lifetime of VGDRA is almost unchanged. Hence in this application scenario, the number of nodes has little impact on VGDRA's lifetime. Figure 20b shows the impact of nodes' number on average residual energy. Our strategy's average residual energy is lower than VGDRA's. Our strategy can make the network have a higher use ratio in this application scenario. Figure 20c shows the impact of nodes' number on residual energy variance. Our strategy's variance is little higher than VGDRA's. According to the data of VGDRA's network lifetime, VGDRA's lower variance is due to that most nodes have not started working yet when the first node runs out of its energy. So our strategy has a better performance in this application scenario.  
Figure 19: Two application scenarios (**a**) Source nodes are distributed in a local area; (**b**) Source nodes are evenly distributed.
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 3 Results
### Study Period  
To be able to detect a bloom, it is necessary to determine the baseline condition of any phytoplankton property to detect deviations from the baseline taking into account climatic variability [15]. The baseline can be calculated from a compilation of years of historical data that define an average of past performance, or from a rapid measurement of current production before initiating a change [15]. Keeping that in mind, we analysed the study period of each study, and Table 5 summarizes the results. Most of the analysed publications conducted short time studies, under a year (43%), usually focused on a specific bloom event. Only 1% of publications studied a period longer than 30 years.
## 3 Results
### EBM/PBF Additive Manufacturing
#### 3.3.3 Magnetic Properties  
The number of EBM/PBF alnico samples allowed for magnetic properties to be studied (Figure 18; Figure 19) for various MA temperatures and hold times. The MA, which is responsible for establishing the initial nanostructure, was varied between 830 \({}^{\circ}\)C, 850 \({}^{\circ}\)C and 870 \({}^{\circ}\)C and for 1, 4 and 8 min. The FHT process further increases the magnetic properties regardless of the MA process and has been studied in detail elsewhere [28]. It should be noted that Figure 18; Figure 19 are plotted with respect to the MA temperature or hold time.  
The highest coercivity was achieved by MA at 870 \({}^{\circ}\)C for 8 min, while all the times at 870 \({}^{\circ}\)C gave the lowest remanence values--a tradeoff common in permanent magnet materials. The highest remanence values were seen at the lower temperatures of 830 \({}^{\circ}\)C and 850 \({}^{\circ}\)C, almost independent of time. The sintered specimens of this composition showed optimal magnetic properties for 8 min at 830 \({}^{\circ}\)C, confirming that the FHT for the EBM/PBF samples must be independently optimized from their sintered and cast counterparts.  
Figure 17: Orientation data from region in Figure 16 with PFs of {100}, {110}, and {111}-type planes projected in the sample X and Y directions (**top**) and IPFs for X, Y, and Z sample directions (**bottom**). Note minor texturing is evident.  
Figure 18: Coercivity and remanence of the EBM/PBF run#5 samples as a function of temperature for different MA hold times. FHT were kept identical between all samples.
## 4 QoS-Aware Multimetric Routing Protocol for Video-Streaming Services over MANETs
### Dynamic Source Routing (DSR)  
Dynamic source routing (DSR) [23] is a distance-based routing protocol where a source node establishes an end-to-end forwarding path from source to destination. Nodes periodically interchange small hello messages (also named beacons) to announce its presence to their neighboring nodes in transmission range, i.e., to their one-hop neighbors. With this information, every node builds a one-hop neighbors' table, to be used by the routing protocol to make forwarding decisions. In DSR, the forwarding path is formed by those nodes which form the shortest path to destination. DSR is the routing engine used by our proposal MMDSR to find out all the available forwarding routes from source to destination. Then, MMDSR selects the best path to transmit the video frames according to a QoS-aware forwarding algorithm which is described in the next subsection.
## 4 Traditional MPPT Techniques
### Incremental Conductance Algorithm  
The Incremental Conductance (IC) MPPT algorithm is an advanced technique designed to optimize the power output of PV systems by analyzing the rate of change in the power output with respect to voltage. The algorithm calculates the incremental conductance (the change in current divided by the change in voltage) and compares it to the instantaneous conductance (current divided by voltage) to determine whether to increase or decrease the voltage to reach the MPP [43; 44; 45; 46]. One of the key advantages of the IC algorithm is its ability to accurately track the MPP even under rapidly changing environmental conditions. However, IC may still exhibit some oscillation around the MPP in highly dynamic environments [47]. Figure 6 illustrates the flowchart of the IC MPPT, where \(dP\) is the power variation, \(dV\) is the voltage variation, \(dl\) is the current variation, and _Vref_ is the reference voltage. The following equations are used to explain the IC method.  
\[\frac{dP}{dV}=\frac{d(I\times V)}{dV}=\mathrm{I}+\mathrm{V}\times\frac{dI}{dV} \tag{1}\]  
\[\frac{dP}{dV}=0\;\mathrm{or}\;\frac{dP}{dV}>0\;\mathrm{or}\;\frac{dP}{dV}<0 \tag{2}\]
## 4 Implementation
### Proposed Scheme
#### 4.4.1 Oversampling  
When the frame rate of the received camera is at least twice as large as the transmitter light source, oversampling occurs. On the receiver side, each package is captured at least twice and is duplicated, leading to confusion in the packet merger function; therefore, the SN is added to each data sub-packet (DS) to have DSs with the same payload and SN. Based on the SN, the receiver camera can select compatible DSs and remove redundant DSs caused by oversampling. The receiver camera will remove the duplicated packet and choose the packet with an increasing SN (n, n + 1, n + 2) to merge.