esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Weak Radar Return Signals Based on Pseudo-Time Domain Algorithm
## 2 Introduction to Denoising Methods
For the \(i\)-th wavelet coefficients, the layer-specific threshold is defined as \(\lambda_{i}\). \[\sigma_{i}=\frac{\text{median}(|d_{i}|)}{a} \tag{2}\]  
\[\lambda_{i}=\frac{\sigma_{i}^{2}}{\sqrt{\max\left(\sigma_{i}^{2}-\sigma_{i}^{ 2},0\right)}} \tag{3}\]  
\[d_{i2,k}=\text{sign}(d_{i1,k})\cdot\max(\left|d_{i1,k}\right|-\lambda_{i},0) \tag{4}\]  
**Step 4:** Perform an inverse transform on the updated wavelet coefficients to obtain the denoised reconstructed signal, followed by filtering. **Step 5:** Determine the decision threshold for the reconstructed signals of pseudo-time domain signals A and B. Detect the reconstructed signal of input pseudo-time domain signal C, and determine the presence of a signal. This process yields the detection accuracy of the pseudo-time domain signal C for the SNR range in question.
## 4 Simulation Experiment Design
### Simulation Parameter Configurations and Analysis Criteria  
#### 4.3.1 V2X Performance Comparison  
This study primarily aims to investigate how 802.11 bd enhances the key V2X performance metrics compared to 802.11p. These metrics encompass the data transfer rate, transmission delay, data throughput, and reliability. Notably, improvements in the data transfer rate and the corresponding transmission delay can be directly computed through theoretical analysis, eliminating the need for simulation experiments. From Table 1, it is evident that 802.11 bd achieves over a 125% higher data transfer rate than 802.11p, using the same MCS and R on the 20 MHz NGV channel compared to the 10 MHz channel. This substantial increase in the data transfer rate results in a reduction of over 56% in the transmission delay. To compare the data throughput and transmission reliability, this research utilizes the two scenarios constructed in the previous section. The simulation parameters of this research are shown in Table 3.  
Table 3 indicates that all RSUs are deployed at or near intersections. This is because intelligent traffic management targeting this area can significantly improve the traffic  
Figure 6: Flowchart of the processing of OBUs receiving data packets.  
efficiency [36; 37; 38], and this deployment also helps to increase the sample size and enhance the simulation's credibility.  
In the simulation related to data throughput, it is evident that the adjusted geometric road scenario is significantly more complex in its configuration compared to the grid road scenario. To evaluate the improvements in data throughput, we employ the macroscopic data dissemination effectiveness as the primary metric in these scenarios.  
In the simulation regarding transmission reliability, we set the OBU broadcast times to 0, i.e., the OBUs only attempt to receive packets without sending any. This design aims to assess the number of vehicles that successfully receive data packets following a single broadcast from the RSUs, thereby mitigating the impact of the asynchronous wireless environment caused by the higher data transfer rate of 802.11 bd. This ensures a performance comparison specific to the transmission reliability while compensating for the loss in data volume in a single broadcast through the strategic placement of dual RSUs (the adequate wireless signal coverages of the dual RSUs in these scenarios do not overlap). In this simulation, we adopt the number of vehicles that successfully receive data packets as the analysis criterion to evaluate the improvement in transmission reliability.
# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 2 Related Works
### Diabetic Retinopathy Lesion Detection
In this, a relation transformer block was incorporated into attention mechanisms in two levels. Another new model was used to detect these same four lesions by the authors in [51]. It was called faster region-based CNN (RCNN) and was a combination of a regional proposal network (RPN) and fast-RCNN. Another new model known as deeply-supervised multiscale attention U-Net (Mult-Attn-U-Net) was used by the authors of [52] for detecting MA, EX, and HM. It was based on the U-Net architecture. Dual-input attentive RefineNet (DARNet) was used by the authors in [53] to detect four DR lesions including HE, SE, HM, and MA. DARNet included a global image encoder, local image encoder, and attention refinement decoder. The same authors used a cascade attentive RefineNet for detecting the same four DR lesions in another study [54]. CARNet included a global image encoder, a patch image encoder, and an attention refinement decoder. A YOLOv5 model along with a genetic algorithm was used by the authors in [55] to detect MA, SE, HE, and HM. Another study by the authorsof [56] detected MA, EX, and HM with the help of a CNN along with a SVM. Prior to this, they used a U-Net to segment OD and retinal blood vessels for feature extraction. Three studies among the reviewed works, focused on the segmentation of the DR lesion called HM. [PERSON] et al. [57] made use of a U-Net architecture for detecting the presence of HM. [PERSON] et al. [58] utilized a novel CNN model known as HE network (HemNet) for HM detection. Quality enhancement, seed points extraction, image calibration, and smart window-based adaptive thresholding were performed prior to training this model. A 3D CNN deep learning framework with feature fusion was used to perform HM detection by the authors in [59]. A variation of the traditional CLAHE method was for pre-processing to achieve better edge detection.
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at Network/Communication Layer
#### 4.3.2 Energy Efficient Routing Techniques
If network running time is \(T\), traffic generation rate at time \(t\) is \(f^{i,s,t}\), traffic arrival rate at destination node \(i\)\(f^{i,d,t}\) and duration of traffic flow is \(dt\), then traffic load at node \(i\) is formulated as:  
\[\int_{0}^{T}f^{i,s,t}dt_{i}=L^{i0}+L^{i1}+\ldots+L^{iN}=\sum_{0\leq j\leq N}L^ {ij} \tag{16}\]  
\[\int_{0}^{T}f^{i,d,t}dt_{i}=L^{i0}+L^{i1}+\ldots+L^{iN}=\sum_{0\leq j\leq N}L^ {ii} \tag{17}\]  
ER-RPL supports generic traffic patterns, network scalability and robustness in different wireless channel condition. It discovers energy efficient and reliable routing paths, but is not suitable in a dynamic environment. An Efficient Routing Protocol for Emergency Response Internet of Things (ERGID) [47] has been suggested on the basis of global information decision to enhance reliability, to minimize delay and to provide load balancing in the network. In ERGID, a delay iterative method is used for optimizing delay estimation and residual energy probability choice approach is utilized for balancing the network load. The delay iterative method is based on a Dijkstra algorithm for finding the shortest path. The delay between two nodes x and y is calculated as:  
\[T^{xy}=\frac{(T^{x}-TS)}{2} \tag{18}\]  
where \(T^{x}\) and \(TS\) are the current time of node \(x\) and time stamp, respectively, and the iterative delay is calculated as:  
\[T^{M-sink}=\min_{x\in RM}\left\{T^{Mx}+T^{x-sink}\right\} \tag{19}\]  
According to the residual energy probability choice approach, if \(E^{x}\) is the residual energy of node \(x\) and CN is the set of nodes having a delay to the sink smaller than the residual time, then the forwarding probability for every candidate node is formulated as:  
\[P^{x}=\frac{E^{x}}{\sum_{j\in CN}E^{j},\ x\in CN} \tag{20}\]  
ERGID is limited to small-scale emergency response IoT due to its energy consumption, and not focused on large-scale applications. A summary of related literatures on energy efficient routing techniques is given in Tables 10 and 11, which comprises characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and research limitations of each paper reviewed. In Table 11, the characteristics of each paper reviewed are categorized into three classes including protocol/author, approach, and metrics. The protocol/author column comprises abbreviations of techniques and reference. The approach column is categorized into five groups namely, SR, PA, PSS, CA and QoS. The last column represents metrics including DL, EE, LQ, ETX, NL, HC, PLR, PDR and CO. Considering Table 11, the approaches in columns two and five demonstrate that more contributions have been made by PA and QoS based techniques, followed by SR, PSS and CA techniques. Further, the metrics column shows that EE is the most considered metric, followed by DL, then LQ, NL, HC and PDR for measuring performance of energy efficient routing techniques.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
###### Abstract  
The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment. Time-interleaved ADC (TIADC) technology can increase the sampling rate without changing the resolution. However, channel mismatch severely degrades the dynamic performance of the TIADC system. For the channel mismatch problem of TIADC, most of the current solutions have preconditions, such as eliminating only some kind of error or increasing the complexity of the hardware. A few methods can estimate multiple errors without changing the hardware circuit. To improve the dynamic performance of the TIADC system, on the basis of an in-depth study of the channel mismatch error of TIADC, according to the system identification theory, an identification model is designed to characterize the frequency characteristics of TIADC. Using the system observation data, the transfer function parameters of the system are recursively estimated. By constructing and verifying the identification model of the TIADC system, and then through the frequency domain correction method, a digital compensation filter is established to complete the error correction of the system. The test results of the four-channel TIADC high-speed data acquisition system show that the actual input and output characteristics of the test system are consistent with the nature of the identification model. The four channels of the TIADC system are provided by four sub-channels of two AD9653 chips, and the highest sampling rate of a single channel is 125 MSPS. For sinusoidal input signals from 20 MHz to 150 MHz, the sampling system can achieve a signal-to-noise ratio (SNR) above 56.8 dB and spurious free dynamic range (SFDR) above 69.7 dB. The dynamic performance of the sampling system is nearly equivalent to that of its sub-ADC; the feasibility of the model identification method and the effectiveness of error correction are verified in simulation and experiment.  
data acquisition system; time-interleaved ADC; channel mismatch error; system identification; frequency characteristics; parameter estimation; frequency domain correction method +
Footnote †: journal: Applied Sciences
## 4 Discussion
#### Limitations of the Study  
The current study has its limitations. Firstly, only 60 volunteers were enrolled, so individual age sub-groups could not consist of more than 15 people. Normative data for each test were therefore impossible to achieve. However, the study provides an overview of the trunk muscle strengths and muscular endurance performances that might be expected in the healthy population's various age and sex groups in the socioeconomic background. Secondly, all participants were hospital employees, so this was not a heterogeneous group of people according to education, as 80% of volunteers had graduated from university.  
On the other hand, heterogeneity existed in terms of the nature of their work (sedentary/manual). Thirdly, only intra-rater reliability of strength testing procedures using HHD was established, as there was only one experienced examiner available for this study. Therefore, one should be aware of the possibility of a \"ceiling effect\" that may occur in powerful individuals when isometric lower back extensor strength is measured using an HHD in the prone position. Another limitation of the study is that we did not record pain throughout the assessment. This might be because exclusion criteria were defined as existing acute and chronic low back pain. It needs to be pointed out that all volunteers were highly motivated to participate in the study and did not complain concerning any pain throughout the assessments. While there is a possibility that pain elicited during the test may interfere with measurements, especially in patients with low back pain, this needs to be recorded, taken into account, and considered in the interpretation of the results. Further studies could include pain-detecting protocols and questionnaires at the enrollment and during the procedures to record whether any tests trigger pain.
# A Rapid Method for Information Extraction from Borehole Log Images
## 4 Corner-Based Cell Segmentation
### Table Line Extraction Based on the Hough Transform  
The Hough transform is a reliable method of detecting straight lines in image processing. Its purpose is to transform the curves in the original image space into points in parameter space by using the symmetry of points and lines. This makes the linear detection problem in the original image transformed into the peak detection problem in the parameter space [21].  
The Hough transform converts the linear detection problems in the image space into cumulative calculations of points in the parameter space according to the mapping relationship. As shown in Figure 4a, in a slope-intercept form, if the line is perpendicular to the X-axis, and the values of k and b are close to infinity, then the calculation will increase [22], thus, a polar coordinate equation is used: \(r=x\cos\theta+y\sin\theta\), where r is the shortest distance from the origin to the line and the \(\theta\) is the angle of the normal of line and X-axis. As shown in Figure 4b, the peak of the line intersection is converted to the peak of the curve intersection in the polar coordinate system.  
The borehole log can be divided into two parts from the content: table lines and text. Table lines directly extracted from the Hough transform may be table lines or segments formed by text, so the main problem with table line extraction is filtering out the disruptive lines produced by the text.  
The most important feature of the table document is that it is a table composed of a vertical crossline intersection, where the segment must be a straight line rather than a curve. The compression (or other reasons) will cause information loss, so there will be interruptions occasionally between the lines. The break between the table lines can be tolerated, but the break between the text has zero tolerance. When the length of a segment is less than the threshold LINE_GAP, it is not recognized as a table line.  
The Hough transform can identify a line at any angle in the image. The most important feature of the table document is the table composed of lines intersecting vertically. The borehole log is derived  
Figure 4: Polar coordinate parameter space. (**a**) r and \(\theta\) in polar coordinates; (**b**) the curve intersection in the parametric space.  
from the standard format export, with only vertical and horizontal lines. Segments in other directions are mostly formed by text interference, so the linear detection here involves only two angles of 0 and 90 degrees. The identification results are shown in Figure 5.
## 3 Results
### Transcriptome-Wide Identification of TPS Genes Using Phylogenetic Tree Analysis  
In the search for putative _TPS_ genes in _C. keiskei_, _Arabidopsis TPS_ genes were initially used as a query. Subsequently, a _C. keiskei TPS_ gene was identified and used as an additional query to find more _C. keiskei TPS_ genes. As a result, twenty-nine genes were further identified. These genes were analyzed for complete open reading frames (ORF) and conserved domains, with an average length of ~566 amino acids (Supplementary Figure S5). The systematic analysis of the identified _TPS_ genes revealed that they can be categorized into five classes. Among these, four unigenes were in the TPS-a class, three in TPS-b, two in TPS-c, one in TPS-e/f and ten in TPS-g classes. Additionally, six sesquiterpene-involved TPS genes were identified (Figure 2). The TPS-c, TPS-e and TPS-f classes are known to be involved in the biosynthesis of di-terpenes, mono-terpenes, and some sesquiterpenenes. Out of the different TPS classes identified, only 15 unigenes had complete ORFs compared to the other retained unigenes, and they exhibited conserved motifs (Supplementary Figure S6).  
Figure 1: KEGG annotations of the MVA and MEP pathways and their volatile compounds in _C. keiskei_ (KEGG pathway map reference 00900 and 00909).
## 1 Introduction  
Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia owing to its significant morbidity and mortality, affecting more than 30 million individuals worldwide [1; 2; 3]. AF patients not only suffer from irregular heartbeats but also are at risk for various associated complications, such as stroke, heart failure, and coronary syndrome [4].  
Radiofrequency catheter ablation (RFCA) is a promising and safe medical modality to ease or eliminate AF-induced burden with the application of radiofrequency (RF) power. It entails irreversible necrosis of cardiac tissue performed by an ablation catheter that is delivered percutaneously to the target site [5; 6]. For decades, circumferential pulmonary vein isolation (PVI) has been the cornerstone for AF treatment [7]. As a traditional method to carry out PVI, point-by-point ablation is widely used [8; 9].  
However, according to reports, its long-term efficiency is unsatisfactory because the procedure is complicated and time-consuming. In addition, contiguous and transmural tissue damage is difficult for a single-tip catheter to achieve [10]. Therefore, circumferential multipolar catheters (CMCs) such as the pulmonary vein ablation catheter (PVAC, Medtronic Inc., Minneapolis, MN, USA) [11] and the multipolar irrigated radiofrequency ablation catheter (nMARQ, Biosense Webster, Inc., Diamond Bar, CA, USA) [12] have been created to achieve complete PVI through a single application of RFCA. These so-called single-shot catheters need to be tailored to fit the anatomy of the antral part of PVs for ablation activity. Therefore, they are configured with a circular geometry.  
The effectiveness of CMC for PVI has been proven in previous studies [13; 14; 15; 16; 17]. With multiple active electrodes, both PVAC and nMARQ enabled the delivery of RF power in the unipolar and bipolar modes [10]. However, those studies focused on examining the clinical accuracy and success rate only. There are few detailed studies that explicitly analyze lesion characteristics and the differences arising from unipolar and bipolar modes of CMC.  
Considering its unique configuration, the CMC should exhibit distinct ablation performance compared to conventional single-tip and multipolar catheters in a linear structure. The electrical and thermal properties of these two catheters have been studied theoretically via computational simulation, which is a time-efficient and cost-effective technique [18; 19; 20]. The role of the simulation study is to provide the theoretical basis of ablation behavior and to clinically assist with personalized therapeutic schedules for individual patients [18]. Accordingly, the objective of this study is to explore the detailed ablation behavior of the multielectrode unipolar (MEU) and multielectrode bipolar (MEB) modes of CMC using a three-dimensional computational model of RFCA and the finite element method. The ablation model was validated by comparing the ablation results of the experiment with the computational simulation.
## 3 Results and Discussion
### Effect of the Preheating Phase  
The regeneration process consists of two phases in series. The first phase is that of preheating, the initial temperature of the filter (523 K in our simulations) being lower than the inlet gas temperature. The second phase is that of true soot consumption. Therefore, the (total) time for filter regeneration, \(\mathrm{t_{reg}}\), is the sum of two contributions, the time for preheating and the time for soot consumption.  
In Table 4, the time for preheating, \(\mathrm{t_{preheat}}\), is given, along with \(\mathrm{t_{reg}}\) and the \(\mathrm{t_{preheat}}\)/\(\mathrm{t_{reg}}\) ratio, as calculated at \(\mathrm{T_{in}}\) = \(\mathrm{T_{in\_optimal}}\) for different values of \(k\). \(\mathrm{t_{preheat}}\) was assumed as the time at which the volume-averaged conversion of the soot trapped inside the wall of the filter reaches 10% (whereas, \(\mathrm{t_{reg}}\) was assumed as the time at which the volume-averaged soot conversion reaches 95%).  
\(\mathrm{t_{preheat}}\) is a significant part (up to-65%) of \(\mathrm{t_{reg}}\). In order to assess the effect of the preheating phase on the regeneration dynamics of the filter, computations were run at higher initial temperatures (>523 K). In particular, at each value of \(k\), the initial temperature was set equal to the inlet gas temperature (= \(\mathrm{T_{in\_optimal}}\)), thus avoiding the phase of preheating. The operating map of Figure 16 shows the comparison between the results obtained with (full circles) and without (empty circles) preheating.  
Figure 16: Operating map of the filter in the plane \(\mathrm{T_{max}}\) versus \(\mathrm{t_{reg}}\): results at \(\mathrm{T_{in}}\) = \(\mathrm{T_{in\_optimal}}\) with (full circles) and without (empty circles) preheating for different values of \(k\). The lower branch of the curve at \(\mathrm{V_{in}}\) = 1 m/s of Figure 10 is also shown.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(k\) [-] & \(\mathrm{t_{preheat}}\) [s] & \(\mathrm{t_{reg}}\) [s] & \(\mathrm{t_{preheat}}\)/\(\mathrm{t_{reg}}\) [-] \\ \hline
**35** & 37.5 & 60.0 & 0.625 \\
**50** & 37.5 & 57.5 & 0.652 \\
**100** & 35.0 & 52.5 & 0.667 \\
**200** & 32.5 & 52.5 & 0.619 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time for preheating, \(\mathrm{t_{preheat}}\), time for filter regeneration, \(\mathrm{t_{reg}}\), and \(\mathrm{t_{preheat}}\)/\(\mathrm{t_{reg}}\) ratio, as calculated at \(\mathrm{T_{in}}\) = \(\mathrm{T_{in\_optimal}}\) for different values of \(k\).  
At each value of \(k\), when avoiding the preheating of the filter, regeneration becomes much faster, but the peak temperature is slightly affected.