esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evolution of Magnetic Nanoparticle Resistance in _Escherichia coli_
## 2 Materials and Methods
### Genomic Analysis  
Whole genome resequencing was utilized to identify genomic variants associated with FeNP and control populations. DNA was extracted from each replicate population at 25 days of selection in magnetite NP using EZNA Bacterial DNA kit (OMEGA) following the manufacturer's instructions. DNA concentrations were determined fluorometrically using the QuantiFluor(r) ONE dsDNA System (Promega Corporation, Madison, WI, USA) on a Quantus(r) fluorometer (Promega Corporation). Measurements were performed according to the manufacturer's recommendations. Genomic libraries were prepared using the standard protocol as described in the manufacturer's instructions (Illumina, Nextera DNA Flex Library Prep Reference Guide). All the reagents used are included in the Nextera DNA Flex kit (Illumina, cat. Nos. 20,018,704, 20,018,705).  
The quality of the final library was verified using D1000 Screen Tape (Agilent Technologies, Santa Clara, CA, USA) following the manufacturer's instructions. Library concentration was measured using the fluorometric quantification using the dsDNA binding dye as previously described and diluted 12 pM with Resuspension Buffer. The pooled libraries were then run on an Illumina MiSeq (Illumina, San Diego, CA, USA) using the MiSeq v3 reagent kit. The depth of coverage of the sequencing runs ranged from \(\sim\)20X to \(\sim\)80X, with most exceeding 40X coverage. The SRA accession number for sequencing data is PRJNA694183 (FeNP-resistant, FeNP\({}_{1-5}\)D28 and controls, C\({}_{1-5}\)D28).  
Sequence alignment and variant calling from the samples were achieved by use of the breseq 0.30.0 pipeline set to polymorphism mode (-p) and default parameters [39]. The pipeline makes use of three types of evidence to predict mutations, read alignments (RA), missing coverage (MC), and new junctions (JC) [40]. Reads that show a distinction between the sample and the reference genome that cannot be resolved to describe precise genetic changes are considered \"unassigned\" and would not be described nor interpreted. Finally, we report here only genomic variants that were not present in the ancestral population. Variants in our ancestral founding populations that differ from the _E. coli_ K-12 MG1655 reference genome are reported in our previous studies [41; 42].
# ICARUS--Very Low Power Satellite-Based IoT
## 5 ISS Antenna Concept and Link Budget  
The ICARUS packet reception performance strongly depends on the UL and DL link budget. The UL link budget is especially crucial for gaining the tag position data. The following Table 5 hence summarizes the major parts of the ICARUS UL link budget:  
The ICARUS UL antenna consists of three independent antenna elements which are slightly steered in the backward direction (relative to ISS orbit path). Two of the antenna elements are steered off-track. Each of the antenna elements feeds a separate digital receiver branch which increases demodulation capacity. The following Figure 18 shows the UL satellite antenna gain contours as a projection on ground. The color code indicates the probability of successful UL packet reception depending on random channel fading and the given link budget.  
Figure 17: ICARUS tag overview.
## 3 Results and Discussions
### Effect of Process Parameters on the Characteristics of Microcapsules
#### 3.1.2 Effect of Initiator Dosage on Shell Structure  
During the polymerization reaction, the amount of initiator APS determines the polymerization rate of the microcapsule shell, which in turn affects the shell structure of the microcapsules. An appropriate amount of initiator can allow the polymerization reaction to proceed steadily, resulting in higher sphericity microcapsules. When the amount of initiator is too high, the excessively fast polymerization rate may lead to lower sphericity of the microcapsules after the reaction. Conversely, when the initiator concentration is too low, it can result in a polymerization reaction that is too slow or incomplete shell material polymerization, significantly reducing the yield of microcapsules.  
When the amount of initiator is 2 g, as shown in Figure 4a, the number of microcapsules in the emulsion is small, the sphericity is average, and the molding rate is low, indicating that the polymerization reaction rate is too slow and the polymerization reaction is incomplete; when the amount of initiator increases to 2.5 g, as shown in Figure 4b, the number of microcapsules in the emulsion significantly increases, and the sphericity is good, indicating that the reaction rate is relatively stable at this time. When the initiator is further increased to 3 g, as shown in Figure 4c, the sphericity of the microcapsules decreases, and the surface is rough, indicating that the amount of initiator is too much, leading to a reaction rate that is too fast. Therefore, the best polymerization reaction effect occurs when the amount of initiator is 2.5 g.
# Modes Control of Lamb Wave in Plates Using Meander-Line Electromagnetic Acoustic Transducers  
[PERSON]  
1 State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (B.W.)1 State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS] (Y.Z.); wangbin1982@nuaa.  
EMATs. For example, the genetic algorithm is used to optimize the diameter and the lift-off of magnet to obtain the preferred A0 mode [11], the wavelength filtering function of the double spiral coils are used to obtain the preferred S0 mode [12], and the dual magnets are used to provide the horizontal magnetic field to obtain the preferred A0 mode [13; 14]. In general, according to the direction of the bias magnetic field, EMATs can be grouped into two types: Normal biased EMATs (NB-EMATs) and parallel biased EMATs (PB-EMATs). An NB-EMAT refers to the EMAT whose bias magnetic field is normal to the specimen surface [11; 12], while a PB-EMAT refers to the EMAT whose bias magnetic field is parallel to the specimen surface [13; 14]. Although the Lamb wave mode control methods proposed in [11; 12; 13; 14] can generate a single omnidirectional Lamb wave mode, these methods are difficult to apply to the meander-line coil EMAT. Compared with the omnidirectional EMATs, the meander-line coil EMATs have higher directivity and lower amplitude attenuation, so they are more widely used in practical applications of large-area scan detection. Consequently, in this paper, we focus on the design of EMATs with meander-line coil for Lamb wave mode excitation.  
At first, from the frequency equations of Lamb waves, we deduce the force conditions for the excitation of single-mode Lamb waves. After that, based on the exciting force conditions of symmetric and antisymmetric Lamb wave modes, two novel paired EMAT configurations were proposed to control the Lamb wave modes. The paired NB-EMAT configuration is used to generate symmetric modes and the paired PB-EMAT configuration is used to generate antisymmetric modes, respectively. Then we present the simulation and experimental verification; the simulations and experiments are carried out on the aluminum plate, in which the NB-EMAT works at frequency 328 kHz, and the PB-EMAT works at 366.7 kHz. Finally, we summarize the performance of the proposed paired EMAT configuration for controlling the excitation of Lamb wave modes.
## 6 Continuous Transmission of Packet Traffic
### Use of TOA
#### Multi-Cluster  
It is advised that networks featuring autonomous underwater vehicles use a multi-cluster protocol. The plan is to separate the network into several clusters, each consisting of several nearby vehicles. TDMA is paired with extended band protectors inside each cluster to lessen the effects of the underwater propagation delay. TDMA is relatively efficient in this scenario because of the proximity of the vehicles in the same cluster. Therefore, the propagation delay impact is negligible. Separate clusters are given various spreading codes to avoid interfering with one another. Additional tools for cluster reorganization following node mobility are included in the suggested procedure.
## 4 DT Use Cases and Application in Healthcare
### Diagnosis and Therapy  
Researchers are leveraging digital twins (DTs) to deepen our understanding of human biology, creating DTs at the levels of single cells, genomes, or entire organs. This ground-breaking approach enables the monitoring of patient health, the diagnosis of diseases, and the planning of preventive care strategies. By testing various treatment methods on a patient's DT, healthcare providers can determine the most effective approach, reducing both time and the costs associated with unsuccessful treatments [68]. For instance, surgeons can use a patient's virtual replica to plan procedures, assess potential risks, or even consider alternatives to surgery [53; 67]. A practical application of this technology is seen in the National Institutes of Health (NIH) DT models, which predict concussion-related trauma in athletes. This data-driven solution not only enhances surgical planning but also accelerates recovery [53].  
Physiotherapists can employ DT technology to track patients' muscle growth and monitor bone movement during physical therapy for conditions like scoliosis. Furthermore, real-time data transfer allows for studying exercise and muscle activity changes and the efficacy of various strains or exercises. Patient response to treatment and desired outcomes can be assessed by tracking their condition [57].  
The innovative health digital twin concept offers a powerful tool to fight cardiovascular disease, a major global contributor to mortality [60]. Utilizing Extended Reality (XR) and digital twins (DT), medical professionals can develop customized virtual representations of patients' cardiovascular systems, allowing for more precise and efficient analysis and optimization of treatments. This virtual platform also promotes a more profound comprehension of various treatment options' efficacy and potential side effects, leading to well-informed decisions and improved patient outcomes. In addition, by employing advanced analytics, trends can be recognized, early warning indicators detected, and the probability of future cardiovascular events predicted. This proactive methodology empowers healthcare providers to act early, halting disease progression.  
Finally, the digital health twin can be distributed to experts worldwide, facilitating remote discussions and cooperation among healthcare practitioners. This can lead to enhanced access to specialized treatment and a more holistic strategy for managing cardiovascular diseases.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 4 Interference Alignment in Cognitive Radio: Second Paradigm
### Water-Filling Techniques For IA in CR Networks  
One of the attractive features of MIMO systems is spatial multiplexing gain and consequently a higher capacity performance over single-input single-output (SISO) system, achieved by the classical water-filling (WF) algorithms [111]. WF algorithms are known to provide capacity-achieving scenarios arising from MIMO systems taking advantage of the DoF offered by antennas to increase spectral efficiency as well as maximizing the mutual information between the input and the output of a channel composed of several sub-channels with the availability of global CSI at the Tx's [83].  
Figure 4 describes how the classical WF works, where units of water per sub-carrier are filled into the vessel and \(\mu\) is the height of the water surface. For some sub-carriers, the bottom of the vessel is above the water and no power is allocated to them, making them the unused spatial directions that could be utilized by the SU transmission. In these sub-carriers, the channel is too poor for it to be worthwhile to transmit on, thus allocates more power to the stronger sub-carriers.  
Figure 3: Multiuser CR network model consisting of one PU link and multiple Sus—Paradigm 2.
# Evaluating Classical Airplane Boarding Methods for Passenger Health during Normal Times
## 2 Airplane Boarding Methods  
The airplane boarding methods considered are some of the most common boarding methods used world-wide by the airlines before the pandemic, either on all the flights or only on selected flights, as listed in Table 1.  
Most of the boarding methods are based on seat assignment where prior to boarding, the passengers know their allocated seat, usually being stamped on the boarding ticket. An exception is the open seating method where the passengers choose their seats after entering the airplane based on their preference [38; 40].  
\begin{table}
\begin{tabular}{c c} \hline
**Boarding Method** & **Airlines** \\ \hline Open seating method & Southwest \\ \hline Random with assigned seats & Air France; Easylet; KLM; Hainan Airlines; Ryanair; \\  & Tarm; WizzAir \\ \hline WilMA & Lufthansa; United Airlines; Swiss \\ \hline Back-to-front-by-group & Air Canada; Air China; Alaska; American Airlines; British \\  & Airways; Cathay Pacific; Eva Air; Frontier; Japan Airlines; \\  & Korean Air; Spirit; Virgin Atlantic \\ \hline Back-to-front-by-row & Delta \\ \hline Reverse pyramid & JetBlue \\ \hline \end{tabular}
\end{table}
Table 1: Boarding methods used by various airlines [6; 9; 23; 27; 38; 39].  
Figure 1: Airplane configuration.  
When seats are assigned, in the scientific literature, three types of airplane boarding methods emerge based on the passengers' entrance into the aircraft, namely _random_, _by group_, and _by seat_. In the case of _random_ boarding with assigned seats, the passengers know the position of their seats in the aircraft and are called for boarding in a random manner, without considering their seat [41; 42]. The _by group_ boarding methods divide the passengers prior to boarding into several groups depending on their seat positions in the aircraft and are called in each group for boarding according to some given rules. Some of the most-known by group methods are: back-to-front, WiMA, reverse pyramid, modified optimal method, rotating zone, and non-traditional method [29; 41; 43; 44]. Last, the _by seat_ boarding methods call the passengers one-by-one to board into the airplane based on their seat position, usually starting from the back of the airplane and by following some given rules. The methods included in this category are: Steffen, Variation in Steffen method, and By seat descending order [13; 43].  
As it can be observed from Table 1, in practice, only _random_ (random with assigned seats) and _by group_ (WiMA, back-to-front-by-group, back-to-front-by-row, reverse pyramid) methods are used by airlines. The _by seat_ methods are hard to implement in practice due to the additional support staff needed for airlines to invite the passengers one-by-one to board.  
The rules accompanying the methods in Table 1 are presented in the following for each airplane boarding method.
# Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset
## 3 Results
### Performance Validation  
We use the pre-trained model SSD MobileNet V2 FPNLite and compared the performance regarding the Average Precision and detection score. Table 16 shows the performance results of our selected EfficientDet-Lite2 model and SSD MobileNet V2 FPNLite, using the same training data for each of them. We compare the performance of the SSD MobileNet to that of our EfficientDet-Lite2 model with the Alpha1 dataset and found that our results were significantly better. Compared to SSD MobileNet V2 FPNLite, our model shows a significant improvement of accuracy for the yellow cube at 40.4% and red cube at 40.1%.  
Table 17 shows the comparison of detection scores for the custom dataset running on our EfficientDet-Lite2 model and SSD MobileNet V2 FPNLite. The detection scores are taken with the project setup of the 135-degree lamp and level 0 illumination. When compared to SSD MobileNet V2, our model significantly improves the overall detection scores by 5.19%. Comparing EfficientDet-Lite2 to SSD MobileNet V2, significant improvements are observed for the red cube (+19.33%) and red cylinder (+20.66%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **EfficientDet-Lite2 with Alpha1 Dataset (\%)** & **SSD MobileNet V2 FPNLite (\%)** & **Improvement of Accuracy (\%)** \\ \hline Yellow cube & 78.1 & 37.7 & 40.4 \\ Yellow cylinder & 72.2 & 35.8 & 36.4 \\ Red cube & 85.1 & 45.0 & 40.1 \\ Red cylinder & 65.9 & 43.4 & 22.5 \\ Blue cube & 66.1 & 48.8 & 17.3 \\ Blue cylinder & 77.3 & 37.7 & 39.6 \\ Overall mean & 74.1 & 41.4 & 32.7 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison of APs for custom object detection model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **135-Degree Lamp with Level** & **SSD MobileNet V2** & **Comparison of** \\
**0 Illumination (\%)** & **FPNLite (\%)** & **Detection Scores (\%)** \\ \hline Yellow cube & 80.00 & 91.50 & \(-11.5\) \\ Yellow cylinder & 80.33 & 94.67 & \(-14.34\) \\ Red cube & 82.00 & 62.67 & \(+19.33\) \\ Red cylinder & 82.33 & 61.67 & \(+20.66\) \\ Blue cube & 79.67 & 83.00 & \(-3.33\) \\ Blue cylinder & 78.67 & 58.33 & \(+20.34\) \\ Overall mean & 80.50 & 75.31 & \(+5.19\) \\ \hline \hline \end{tabular}
\end{table}
Table 17: Comparison of detection scores with SSD MobileNet V2.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 4 Experiment
### Ablation Study
#### 4.5.3 Necessity of Low-Light Image Guidance  
The significance of employing a low-light image as guidance in the filtering process, particularly in the absence of clear images, is assessed. Experimental results indicate that applying the low-light image as guidance during filtering yields more satisfactory outcomes compared to using the enhanced image itself. As depicted in Figure 7, when the low-light image is employed as guidance during filtering, the results are notably improved compared to utilizing the enhanced image itself. In low-light images, the night sky often appears as a smooth region but may exhibit noticeable noise after enhancement. When the enhanced image itself serves as the guide, some of the noise is erroneously treated as texture information and retained. Conversely, this issue can be effectively avoided by deploying the low-light image as the guide.
# GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization
## 2 Related Work  
### Dataset Condensation  
Dataset distillation and condensation are techniques aimed at reducing the size of large datasets while maintaining their core information and utility for training machine learning models. [PERSON] et al. [18] distilled knowledge from massive image datasets into a few synthetic images, while still keeping the comparable performances of the models trained on them. Nonetheless, this method involves a nested loop optimization and is computationally expensive. [PERSON] et al. [14] proposed to align the gradients of the model parameters to correspond with real and synthetic training data. GCond [11] extends this idea to graph data, whereby learning synthetic graphs with only hundreds of nodes have training GNNs applied to them. DosCond [10] further accelerates the condensation process by performing one-step updates for nested optimizations, i.e., it only updates the synthetic graph without training the upstream GNNs that are used for condensation. Although these methods can preserve the core utilities of the original graph, we observed degradation in the subgroup fairness for GNNs that were trained on synthetic graphs. In this work, our primary focus is on how to effectively apply condensed graph representations that treat node subgroups fairly in order to prevent possible harmful applications.
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 6 Quality
They comprehensively evaluated the sonicated product using sensory panelists, an electronic nose, an electronic tongue, and gas chromatography-mass spectrometry (GC-MS). The untreated soybean milk exhibited some off flavors described as grease oxidative, metallic, and grassy. However, ultrasound reduced them during the first 7 min of treatment. After this threshold value, the off flavors were increased. The electronic nose and tongue could distinguish between sonicated and unsonicated milk products. The GC-MS analysis showed a decrease in aldehydes, furans, ketones, and alcohols in the sonicated samples. The threshold value between off flavors can be explained due to the generation of free radicals and oxidative processes, enzyme inactivation, and changes in the protein conformation. In recent studies, the sensory evaluation of hog plum juice showed positive results. Several ultrasound treatments were conducted at 40 kHz, 400 W, with temperatures from 40 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, and the processing time was from 5 to 30 min. The sensory evaluation of the juice included taste, color, flavor, mouthfeel, raw juice's overall acceptability, thermal pasteurization (90 \({}^{\circ}\)C, 60 s), and the different sonication treatments. Untrained panelists (n = 50) used a 9-point Hedonic scale, showing the preference for those juices treated with ultrasound in all the sensory characteristics evaluated, followed by the raw juice, and leaving the thermal pasteurized juice in the last place [46]. Apple juice was also assessed in sensory properties by a trained panel (n = 20) using the same 9-point Hedonic scale. In this case, the evaluation showed that appearance, odor, cloudiness, and the general acceptability of sonicate juice were preferred over thermal pasteurized juice (85 \({}^{\circ}\)C, 10 min). The only trait that was not showing positive feedback was the taste of the sonicated apple juice [42]. Amora juice was also evaluated in sensory attributes and compared to raw and thermal pasteurized juice. Twenty-five untrained panelists assessed with a 9-point Hedonic scale the taste, odor, and overall acceptability. Results showed excellent results for all the juices (>8.5) and no difference in taste or smell after sonication and up to 30 days of refrigerated storage between thermal or sonicated products [21]. These results represent a milestone in ultrasound research. Now, the study of ultrasound is more comprehensive and is not only limited to microbial inactivation. Besides, scientists are playing with the processing conditions to find the best combination between ultrasound and other parameters, such as temperature, that ensures microbial inactivation and delivers a high-quality product.