esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Dataset  
We collected a fluoroscopic image dataset for our custom-made, 3D-printed heart model during a mock procedure in the catheterization lab at NewYork-Presbyterian Hospital. The custom model contained a heart that was transparent under X-ray and a metal spray-painted spine that was visible under X-ray, which is the same as in our previous publication [15]. In addition, some metal spheres were present as fiducial markers, but those were not used in this study. It should be noted that the 3D-printed heart model was within an acrylic box that produced additional artifacts seen in the image, but these artifacts will not be present in clinical images. Our dataset contained a total of 3408 JPEG images at 512 \(\times\) 512 spatial resolution, where the catheter was moved along the entire range of the image. In all images, the entire catheter tip was visible. The dataset included 62 paired images which were taken from different views at the same time. These 124 images were used to test the model in 3D space. Image contrast, clarity, and brightness varied across sets taken on different days.  
To prove that landmark detection can be achieved, we initially focused solely on the radiopaque marker band of the catheter tip. The marker band formed different shapes depending on the angle of the image, and, therefore, can be seen as a rectangle with varying width, a thin-walled circle, or an oval. The catheter can also take on different shapes in the image as it is curved, rotated, and translated by the user (Figure 1).  
The dataset (containing 3408 images) was randomly divided into three sets: training (2182 images), validation (546 images), and test (680 images). In addition, we had an additional set, the \"fixed-test\" set, which was based on removing any incorrectly predicted regions from the first stage of the 2-stage network architecture (to determine the best base accuracy of the second stage of the network). For deep learning model evaluation, 20% of the samples were set aside at the beginning as the test set. The training and validation sets consisted of the remaining 80% of the samples, which were trained and evaluated based on a 5-fold cross-validation technique. Therefore, in each iteration, 80% of the training and validation samples were dedicated to training the model and 20% of them were set as the validation set. The best parameters for the training process were chosen based on best practices and the assessment of the validation outcomes. The training parameters included the number of layers, neurons in each layer, and epochs, and the learning rate. The best values for these parameters were achieved by varying them and checking for the best accuracy in the outcome. The detailed architecture is discussed in Section B.
## 2 The Evolution and Trends of 6G Network Architecture
### Opportunities for Fundamental Change
#### 2.2.4 AI-Native Design Enabling Human-Machine Teaming  
Even though self-organizing networking (SON) and machine learning (ML) have been a part of 3 GPP since Release 8, the particular challenges of 6G, like high dynamics, spatial distribution requirements, low latency, and large data volumes, demand a fundamental rethinking of how AI is integrated into the telco ecosystem. Because of its strict design constraints, 6G offers the AI community an interesting new challenge.  
Different techniques such as transfer learning and ensemble methods must seamlessly integrate into the overall telecom architecture. Crucially, AI-driven decisions that impact consumers need to align with global consumer-facing policies, similar to General Data Protection Regulation (GDPR) Article 13. Disclosure of relevant information regarding the reasoning behind and effects of data processing is required for compliance. Consequently, 6G needs to incorporate novel paradigms like Explainable AI (XAI). Traditional deep learning AI operates like a \"black box\", making it challenging to explain decisions [12; 13], but XAI methods enable human understanding of solution results. XAI is essential not only for regulatory compliance but also for enhancing user trust in AI decision-making. Emerging XAI technology families, particularly those based on planning [14].  
Furthermore, AI plays a part in the design process as well as operational elements. Even if 6G might not completely comprehend this, cutting-edge AI/ML systems may design networks in the future. A disruptive approach would be to envision a time when AI can take telecom-related innovation from the Internet, turn it into code, self-validate, integrate it into a softwarized infrastructure, perform beta testing, and push it out globally in minutes as opposed to decades. This has the potential to be the core technology underpinning the next generation of industry platforms, known as Industry 5.0. All of these developments will create new design and operational paradigms that will enable unparalleled human-machine cooperation to maximize the capabilities of each
# Factors Affecting the Quality of Bacterial Genomes Assemblies by Canu after Nanopore Sequencing
## 2 Materials and Methods
### Basecalling and De Novo Assembly  
The basecalling of the raw signals from the three sequencing runs was performed singularly (B434, B530, B593) with Guppy v.5.0.11 (Oxford Nanopore Technologies) by the r9.4.1_450 bps_hac model on the ReCaS data center servers (University of Bari). Only the  
Figure 1: Workflow of the bioinformatic analysis performed.  
fastq files in the Guppy directory \"pass\", considered as high-quality reads, were used for further analyses, singularly and on aggregate (BT).  
The quality of the basecalled fastq files from the three sequencing runs has been measured by using NanoPlot v.1.39.0 [18].  
De novo genome assembly of basecalled reads was performed using Canu v.2.2; [12], with default or adjusted error correction parameters. Combining the DNA extraction methods and the Canu parameters adjustments, six different experimental settings were obtained, as specified in Table 1.  
The correctedErrorRate parameter was decreased from 0.144 (the default value) to 0.12 in S1, while more parameters were modified in S2. Specifically, corErrorRate was modified from 0.30 to 0.04, corMhapSensitivity was set to high, corMinCoverage was adjusted to 0, and corOutCoverage was increased to 100.  
The contigs from each assembly setting were polished with one round of Racon v.1.4.10 [19] and the final consensuses were obtained by Medaka v.1.4.4 (Oxford Nanopore Technologies). Nanopore systematic errors were removed with Homopolish v.0.3.3 [20].  
A comparative de novo assembly was performed using Flye v.2.8.3-b1695 [21] on the three separate basecalled reads (B434, B530, and B593) and their combination (BT).
## 4 Results and Discussion
### Regional-Scale Mapping and Monitoring of Rice Areas in Asia: A Way Forward
A collaborative effort across consortia to provide space agencies with the current best available rice extent data and rice cropping calendar data for Asia, so that the best systematic acquisition strategies can be developed alongside other acquisition needs. 2. Uniform coverage of the major rice-growing areas using high spatial resolution (5 m-20 m) SAR imagery with at least bimonthly frequency, with single or dual polarization and with incidence angles between 37\({}^{\circ}\) and 50\({}^{\circ}\). There are good examples of the benefits of regular, consistent, systematic acquisition planning for monitoring at the national and continental scale [35]. 3. The development of automated processing chains installed on cloud-based or cluster-based hardware solutions to meet the processing and storage requirements of this large amount of data. 4. Further research into the use of temporal feature descriptors to classify rice [25]. 5. The development of an open-access library of signatures for rice, and other crops, across multiple environments based on a range of SAR sensors, wavelengths, polarizations and incidence angles. This would complement comparative analyses at the field level [15]. 6. The evaluation of mobile devices for field data collection. These recommendations support the regular provision of SAR-based information services, not only for rice, and could emulate the highly successful MODIS project in terms of both application development and scientific advancement in Earth observation. The unintended consequences of short-term policies and panic responses, such as those that created the rice price crisis of 2008, led to significant food insecurity and poverty for millions across Asia. The provision of regular, unbiased and accurate information on the rice crop would be a significant contribution to policy support and would reduce the risk of similar events happening in the future. Such an endeavor would require coordinated acquisition plans and open data access policies across SAR providers, in-country capacity building for developers and users of the information and investments in research and policy institutes across the region. This is no minor task, but the societal benefits that would accrue from this could be substantial and directly attributable to remote sensing.
## 2 Principle
### Proposed Local Phase-Amplitude Joint Correction Method
The velocity curve of the center point of the free surface of the pressure bar measured by PDV is analyzed by the short-time Fourier transform method, and the propagation mode is obtained. After that, the amplitude correction coefficient can be solved according to the phase velocity corresponding to each order of the propagation mode given in theory. The combined use of phase correction (Equation (10)) and amplitude correction (Equation (19)) can correct waveform dispersion caused by lateral inertial effects. This paper proposes a local phase-amplitude joint correction method. The algorithm steps are as follows:  
Step 1: Perform a short-time Fourier transform on the axial velocity signal of the bar center to obtain its time-frequency spectrum;  
Step 2: Analyze the order of the stress wave propagation mode in the bar according to the time-frequency spectrum and the theoretical curve given in Figure 5;  
Step 3: Solve the phase correction coefficient matrix and amplitude correction coefficient matrix separately according to the propagation mode;  
Step 4: According to the arrival time of the stress wave of a specific frequency transmitted to the end surface of the bar, set the local range (twice the window length) around the frequency-time point in the phase correction coefficient matrix to be effective, and set the rest to 0, that is, the remaining parts do not correct the phase;  
Step 5: According to the given upper and lower frequency limits, set the part of the amplitude correction coefficient matrix that exceeds the range to 0, that is, filter out the noise that exceeds the effective frequency range;  
Step 6: Repeat steps (3) to (5), calculate the amplitude and phase correction coefficient matrix of all orders of propagation modes in the signal and superimpose and sum them up, then combine the amplitude and phase correction coefficient matrix into a complex amplitude correction matrix;  
The complex amplitude correction matrix is multiplied by the time-frequency spectrum of the velocity signal, and then the inverse Fourier transform is performed to obtain the average stress curve of the compression end of the pressure bar. In this method, at a certain time, only the signal within a small frequency range around the main frequency component that meets the dispersion characteristics is corrected, and the remaining non-dispersed frequency components retain the same phase, thus avoiding the error introduced by the correction of the non-dispersed components.
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
## 5 Result Analysis
### Fuzzy Test  
The fuzzy logic control uses the current produced in the solar panels by the sunlight to determine the timing for activating the motor to drive the solar panels to the correct position. For the fuzzy programming part, we assume that the output voltage of light intensity is 2.5 V to 4.5 V and the output current of the solar panels is 0.1 A to 1 A. The outputs of the fuzzy programming tests are recorded in Table 5. The highlighted part is required to start the motor. The results show that if the output value is greater than 0.5, the motor of the sun tracking system needs to be started; if it is less than 0.5, the solar panels maintain the original position.
## 2 Experiment Overview
### Model Loading and Test Procedure  
To ensure maximum stress at the wet joint location, structural influence lines are computed, indicating that loading at a distance of 1.96 m from the wet joint satisfies loading requirements, as shown in Figure 2c. The experiment utilizes four 300 t tension jacks to load the specimens through beam allocation. Initial loading is set at 75 kN, followed by incremental loading of 30 kN per level until reaching 255 kN, then reduced to 15 kN per level until structural failure. Crack width measurements are conducted at each loading level using crack gauges. To ensure precise operation of the four jacks simultaneously and minimize errors, pressure ring sensors for jacks are prepared to determine the load level and equipped with a hydraulic pump diverter for precise control. Before testing, corresponding sensors are tested to ensure their proper functioning.  
To ensure the normal operation of the jacks and apply the required force levels, holes are drilled at the bridge deck loading points to facilitate the passage of PSB830 hot-rolled threaded steel, and holes are drilled at the positions corresponding to the allocation beams and reaction beams to allow one end of the hot-rolled threaded steel to act on the allocation beam and the other end on the reaction beam, generating relative self-reactions at both ends of the hot-rolled threaded steel to achieve the loading objective, as illustrated in Figure 2a.  
Test procedures include monitoring of steel strain, concrete strain, structural vertical displacement, and crack propagation in the bridge deck. Strain measurement points are arranged at the wet joint interface at 1/2, 1/4, and end positions, as shown in positions JF1-JF5 in Figure 2a. Loading positions at 1/2, 1/4, and end positions are depicted in positions JY6-JY10 in Figure 2b. Variable-section positions at 1/2 and end positions are indicated by positions C1-C3 and JF11-JF13 in Figure 2b. Displacement measurement points are arranged at the bottom of the loading slab, as shown in Figure 2c. Steel strain distribution and panel side strain distribution are illustrated at positions R1-R3 and BC1-BC4 in Figure 2d,e. The on-site conditions of the components are depicted in Figure 2f.
# Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
## 3 Network Throughput under Backoff Modification
### Theoretical Network Throughput
We assume that the stations of both classes share the same duration of a successful transmission and the same duration of a collision. Thus, \(T_{slot}\) is:  
\[T_{slot}=(1-P_{tr})T_{s}+(n_{1}P_{s,1}+n_{2}P_{s,2})T_{t}+P_{c}T_{c} \tag{7}\]  
We consider \(T_{p}\) the payload information time duration in a successful transmission and we assume that all stations share the same \(T_{p}\). We define \(S_{i}\), the throughput ratio for station \(i\), as the fraction of time used by station \(i\) to successfully transmit payload bits. \(S_{i}\) is obtained as:  
\[S_{i}=\frac{P_{s,i}T_{p}}{T_{slot}}=\frac{P_{s,i}T_{p}}{(1-P_{tr})T_{s}+(n_{1} P_{s,1}+n_{2}P_{s,2})T_{t}+P_{c}T_{c}} \tag{8}\]  
In (8), we could use units of time for the magnitudes \(T_{p}\), \(T_{s}\), \(T_{t}\) and \(T_{c}\), or measure its length in bits, as long as the units are the same for the four parameters. Finally, the total network throughput, defined as the fraction of the time spent by all the stations transmitting successfully payload bits, is:  
\[S=\sum_{i=1}^{N}S_{i}=n_{1}S_{1}+n_{2}S_{2} \tag{9}\]  
The parameters \(T_{s}\), \(T_{t}\) and \(T_{c}\) are obtained from the 802.11 standard. \(T_{s}\) is the empty slot time. In case of using BA mechanism, we have [22]:  
\[\left\{ \begin{array}{l}T_{c}^{ba}=H+T_{p}+DIFS+\delta\\ T_{t}^{ba}=H+T_{p}+SIFS+\delta+ACK+DIFS+\delta\end{array} \right. \tag{10}\]where \(H\) is the total header transmission time (adding PHY and MAC layers headers), \(DIFS\) and \(SIFS\) are interframe spacing defined in the standard, \(ACK\) is the transmission time of an ACK and \(\delta\) is the propagation delay. We also consider that all payloads have the same size, whose transmission time is \(T_{p}\). In case of using RTS/CTS mechanism, we have [22]:  
\[\left\{ \begin{array}{l}T_{c}^{rts}=RTS+DIFS+\delta\\ T_{t}^{rts}=RTS+SIFS+\delta+CTS+SIFS+\delta+T_{t}^{ba}\end{array} \right. \tag{11}\]  
Comparing (10) and (11), we see that BA mechanism uses less time for a successful transmission, whereas the time spent in a collision depends on the payload size. Intuitively, in case of large payloads and a high collision probability, RTS/CTS could achieve a higher throughput, since less time is spent on retransmissions and that might compensate the longer time spent on transmitting. Indeed, this is observed in [22].
# Further Development of 3D Crack Growth Simulation Program to Include Contact Loading Situations
## 3 Simulation of 3D Structures with Contact Loading
The important values of the Forman-Mettu equation are provided in Table 1.  
\[\frac{da}{dN}=\frac{C_{\text{FM}}\cdot\left[\left(\frac{1-\gamma}{1-R}\right) \cdot\Delta K_{\text{I}}\right]^{n}\cdot\left(1-\frac{\Delta K_{\text{i,th}}}{ \Delta K_{\text{I}}}\right)^{p}}{\left(1-\frac{K_{\text{i,max}}}{K_{\text{IC}}} \right)^{q}} \tag{1}\]  
Figure 4: Simulation results of the clinched joints and initial crack: (**a**) The maximum principal stress distribution in the clinched joints; (**b**) The initial semi-circular crack at the location with the maximum value for principal normal stress. Figure 3: 3D model of clinched sheets prepared for crack growth simulation along with the boundary conditions: (**a**) The contact definition between the sheets (marked in red) and the boundary conditions on the punch-sided and die-sided sheets; (**b**) The contact definition between the sheets and the boundary conditions on the die-sided sheet in another view; (**c**) The loading situation on the punch-sided sheet. The results from the crack growth simulation are shown in Figure 5. The global model generated by Adapcrack3D with multiple parts, contact definitions, and initial crack is displayed in Figure 5a. The newly calculated crack fronts in the punch-sided sheet, including the initial crack, are shown in Figure 5b. Hereby, in every step, the crack growth increment is selected as 0.025 mm. The crack growth in the model was more or less in the Mode I direction. Adapcrack3D was able to successfully create global models and simulate crack growth in the model with contact definitions. Therefore, it can be concluded that the newly implemented feature of contact definition is successfully implemented and simulated by the program. In this example, the contact definition between crack surfaces, which is explained in the next section, is not considered.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 4 CNT-Based Antibacterial Applications  
It is well known that the wounded areas are more susceptible to bacterial infections. Depending upon the etiology and severity of the microbial invasion, the infections can cause minor to major damage to human life [78]. In the early stage of infection, the Gram-positive bacteria such as _Staphylococcus aureus_ and _Streptococcus pyogenes_ are more populated, whereas the Gram-negative bacteria, such as _Escherichia coli_ (_E. coli_) and _Pseudomonas aeruginosa_ (_P. aeruginosa_), are populated later nearby wounded areas [79]. Usually, infection is avoided by activating the immune system for abolishing the invading pathogens. In this process, macrophages initiate the migration to the wound site and subsequently perform phagocytosis of the pathogens (destroyed in a phagolysosome or by nitric acid production). In a later stage of infection, the immune response is performed by activating T-helper lymphocytes, which secrete interferon-\(\gamma\) and CD40 ligand to coordinate the immune adaptive and humoral response to kill and remove the invading bacteria [80]. In the passive stage of the immune system, infection occurs and causes the deterioration of granulation tissue, growth factors, and extracellular matrix components (collagen, elastin, and fibrin) and alters the normal wound-healing process [81; 82]. Therefore, it is necessary to develop wound dressing materials to prevent bacterial penetration into the wound or reduce the microorganism's growth [83].
# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 3 Theoretical Analysis
### Wavelength Selection  
The choice of wavelength is very important for NGM, and inappropriate wavelength combinations will make Equation (5) an Ill-Conditioned Equation (ICE) [25] whose solution is extremely unstable. The ICE problem will cause the solution of Equation (5) an huge error under a tiny interference in vector \(A\) or matrix \(\Theta\). Unfortunately, this interference often occurs in NGM, reducing the robustness of the measurement.  
In order to improve the accuracy and robustness of NGM, ICE should be avoided as much as possible. The solution is making the \(k(\Theta)\) in Equation (7) as large as possible [25].  
\[k(\Theta)=\left\|\Theta^{-1}\right\|\cdot\left\|\Theta\right\| \tag{7}\]  
\(k(\Theta)\) will be large enough, if the wavelength selection follows the following principle: \(\varepsilon_{j}(\lambda_{i})\) in \(\Theta\) should be large as possible if \(i=j\), otherwise it should be small. In other word, wavelength selection should ensure that the absorbance of components \(i\) is large at the wavelength of \(\lambda_{i}\) and should be small at other other wavelengths.  
Figure 1 shows the spectrum of glucose from 500 nm to 1000 nm, in which there is an absorption peak at 930 nm. In order to eliminate the interference of other components, the spectrum is obtained from aqueous glucose solution and the water background is subtracted. Other components of the skin tissue have a lower absorbance in the near-infrared region (780-2526 nm) [11]. Therefore, the wavelength \(\lambda_{0}\) used to measure glucose is selected as 930 nm. The change of blood volume, oxyhemoglobin (Oxy-Hb) and deoxyhemoglobin (Deoxy-Hb) can be measured by dual-wavelength of 850 nm and 660 nm [26]. The attenuation degree of water is greater and decreased at the wavelength of 730 nm and 490 nm respectively. Meanwhile, fat is the main component part that effects the tissue light attenuation at 490 nm, which can be used to measure the thickness of earlobe. The five different wavelengths (490 nm, 660 nm, 730 nm, 850 nm, and 930 nm) are selected for blood glucose estimation.