esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Flash Translation Layer (FTL)  
To manage the special hardware nature of flash memory, a flash storage medium typically incorporates a flash translation layer (FTL), which can handle the unique characteristics of NAND flash internally, exposing a block access interface externally (Figure 2). Therefore, block-based file systems (e.g., EXT, FAT, NTFS, etc.) that have been designed for HDDs can be deployed on top of a flash storage medium. The FTL implements four key functions: address translation, garbage collection, wear leveling and bad block management. Address translation manages mappings between the block addresses and the flash memory addresses so that I/Os performed on the block addresses can be translated to I/Os performed on the flash memory. Due to the out-of-place update feature, the data stored in flash blocks may be invalidated over time. Garbage collection is used to periodically reclaim those invalid blocks. Wear leveling ensures that programs/erasures can be distributed evenly across the flash to prolong the service life of flash memory. Unavoidably, \"bad\" blocks will be generated over time, and bad block management is used to handle those bad blocks, preventing them from being used again.  
The FTL stays inside a flash storage device and is isolated from any external entities (e.g., the OS) using the device. Leveraging this hardware-level isolation, our design will integrate the malware detection into the FTL. The benefit is it would not be possible for the malware to subvert our malware detection, even if the malware can compromise the OS.
## 4 Effect of Impulse Duration  
In this study, the IET is employed to identify the sloshing natural frequency of a water-filled tank. One of the key challenges in the numerical analysis of this method is the approximation of the Dirac delta function, which ideally represents an impulse applied over an infinitesimally short duration. However, in numerical simulations, a finite duration must be used to approximate the impulse.  
To evaluate the effect of impulse duration on the accuracy of the results, two cases with different rise times were considered. As shown in Figure 5, the first case involved an impulse with a rise time of 0.05 s, while the second case used a rise time of 0.1 s, which is twice as long as the first. Despite this difference, both rise times are still sufficiently short to effectively simulate an impulse excitation that closely approximates the ideal Dirac delta function, given the practical limitations of numerical methods and computational constraints.  
Figure 6 presents the time-domain responses for both cases, while Figure 7 illustrates the corresponding Fourier Transform results. The frequency spectra indicate that both cases yield nearly identical peak frequencies and amplitudes, suggesting that the variation in rise time has minimal impact on the identified natural frequency.  
Figure 5: Cases study of impulse duration.  
Figure 6: Impulse time series result.
## 2 Generalized Frequency Division Multiplexing (GFDM) Waveform Structure  
The main transceiver block scheme of the communication system using the GFDM waveform is shown in Figure 1.  
Randomly generated serial input binary data on the transmitter side of the GFDM system given in Figure 1 are modulated by the I-Q mapping block. The modulated data is mapped in the GFDM modulator block, each subcarrier is shaped with Raised Cosine (RC) filter as a pulse shaping filter and the subcarrier is modulated with the center frequency of \(e^{-\beta\pi kn/N}\). To fulfill the Nyquist criteria, each symbol is sampled at the level \(N\), which gives a vector of \(MN\) in the sublayer. The pulse shaping filter is selected to have \(M\times N\) periodicity. Different pulse response filters can be used to shape sub-carriers, and this method affects OOBE and Symbol Error Rate (SER) performance. GFDM is based on the modulation of individual blocks, where each block consists of a series of sub-carriers and sub-symbols. The sub-carriers are filtered with RC filter as a pulse shaping filter that is shifted cyclically in the time and frequency region. This procedure allows for split-spectrum and dynamic spectrum allocation by reducing OOBE, without causing serious interference in embedded services or other users. After the data symbols are placed in a \(d(k,m)\) column, all symbols are summed and the GFDM signal is generated. In the communication system, \(d(k,m)\) contains complex data symbols, \(K\) carriers and \(M\) sub-symbols [8]. We obtain the matrix \(d\) concatenating these columns:\[d=\left[\begin{array}{cccc}d(0,0)&\ldots&d(0,M-1)\\ \vdots&\ddots&\vdots\\ d(K-1)&\ldots&d(K-1,M-1)\end{array}\right] \tag{1}\]  
The \(K\times M\) matrix will be treated as an information block, i.e., \(k=0\ldots K-1\) indexes the sub-carriers and \(m=0\ldots M-1\) indexes the elements of a time slot.  
The transmission signal can be expressed as;  
\[x(n)=\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}d(k,m)[gT(n-mN)\text{mod}NM]e^{-j\frac{2\pi }{N}kn},\ 0\leq n\leq NM-1 \tag{2}\]  
Through the frequency selective channel, the obtained GFDM signal is corrupted by AWGN. Assuming \(y(n)\) are the time samples acquired at the receiver, data equalization is done at the receiver where \(\hat{d}(k,m)\) are obtained by inverting the frequency shift, implementing the matched filter \(gR(n)\) and down sampling. Thus, yielding signal at \(n=mN\) is defined as:  
\[\hat{d}(k,m)=\left(y(n)e^{-j\frac{2\pi}{N}kn}\right)\oplus gR(n)\Big{|}_{n=MN} \tag{3}\]  
where \(\oplus\) denotes circular convolution with respect to \(n\)[19]. After correcting the corrupted data, the receiver performs the reverse operation to recover the signal as shown in Figure 1.
## 3 Numerical Calculations and Analysis
### The Influence of Airyprime Beam Parameters
The main peak is located in the second peak on the right side, and the first peak on the right side decays rapidly, while there are multiple oscillatory peaks on the left side of the main peak. The amplitude of the oscillations decreases gradually, forming a long trailing structure. It is worth noting that the intensity of the Airyprime beam gradually becomes more concentrated as the truncation factor increases, which causes the intensity of the main peak to gradually increase and the energy of the side flaps to decrease significantly. Figure 7b shows the effect of the truncation coefficient on the peak intensity of the shedding soliton, which shows a monotonically increasing trend with increases in \(a\). The peak intensity of the shedding soliton is also shown in Figure 7b, which shows the effect of the truncation coefficient \(a\) on the peak intensity of the shedding soliton. Overall, the larger the truncation coefficient \(a\) is, the more concentrated the energy of the Airyprime beam is, and the higher the peak intensity of the shedding soliton is. Figure 6: Evolution plots of Airyprime beams with different truncation coefficients \(a\) when \(P=2.1\), \(w=1\), and \(W_{0}=0.8\): (**a**) \(a=0.2\), (**b**) \(a=0.3\), and (**c**) \(a=0.4\). (**d**,**e**) are for the normalized propagation distance \(Z=30\) and the modulation depths of \(a=0.2\) (green line), \(a=0.3\) (orange line), and \(a=0.4\) (blue line), respectively.
# SMILE: Segmentation-Based Centroid Matching for Image Rectification via Aligning Epipolar Lines
###### Abstract  
Stereo images, which consist of left and right image pairs, are often unaligned when initially captured, as they represent raw data. Stereo images are typically used in scenarios requiring disparity between the left and right views, such as depth estimation. In such cases, image calibration is performed to obtain the necessary parameters, and, based on these parameters, image rectification is applied to align the epipolar lines of the stereo images. This preprocessing step is crucial for effectively utilizing stereo images. The conventional method for performing image calibration usually involves using a reference object, such as a checkerboard, to obtain these parameters. In this paper, we propose a novel approach that does not require any special reference points like a checkerboard. Instead, we employ object detection to segment object pairs and calculate the centroids of the segmented objects. By aligning the y-coordinates of these centroids in the left and right image pairs, we induce the epipolar lines to be parallel, achieving an effect similar to image rectification.  
Keywords:image rectification; epipolar alignment; stereo image; object segmentation; camera calibration
## 2 Related Work
### Flat-Based Protocol  
In a flat-based protocol, each sensor node performs the same role and function and also includes the ability to collaborate with neighboring nodes to collect data. Data transmission to the BS exchanges control information with neighboring nodes to establish a transmission path and transmits the data through the established path. Typical protocols using a flat  
Figure 1: Wireless Sensor Network (WSN) physical structure.  
based protocol include Sensor Protocols for Information via Negotiation (SPIN) [3; 4] and Direct Diffusion [4; 5; 6].  
SPIN adopts the concept of metadata and uses a negotiation technique that ensures that only useful data is transmitted when exchanging data between neighboring sensor nodes. This minimizes implosion and overlapping problems that occur in a flat-based protocol when flooding. SPIN has a drawback in that an individual sensor node needs only information about its immediate neighbor node, and if a very simple protocol or destination exists far away and a sensor node in the middle does not request data transmission, it may fail to transmit data to its destination. Figure 2 shows the process of transmitting to neighboring nodes using the above three types of messages when sensor node A collects new data in SPIN.  
Direct Diffusion is a data-centric routing protocol that propagates requests using interest inquiries from the BS and sends data through reports from sensor nodes with events matching the interest. In this method, the path from the sensor node to the BS is established by applying an enhancement function of the path generated in the opposite direction to the gradient, which is the propagation path of interest. Therefore, if the requested content of the BS is continuous, it can be used properly because data is transmitted to the route set through route reinforcement. However, if the request is a one-time event, it is difficult to properly apply because it requires continuous interest inquiry and route reinforcement. Figure 3 shows the interest propagation process in the BS and the process of reporting an event to the BS from a sensor node that detects an event matching the interest.  
Figure 3: Direct Diffusion data transmission process. (**a**) Interest propagation. (**b**) Gradient establishment. (**c**) Send probe data. (**d**) Reinforce path.  
Figure 2: Sensor Protocols for Information via Negotiation (SPIN) data transmission process. Node A starts by advertising its data to node B (**a**). Node B responds by sending a request to node A (**b**). After receiving the requested data (**c**), node B then sends out advertisements to its neighbors (**d**), who in turn send requests back to B (**e**,**f**).
## 2 Materials and Methods  
Concanavalin A (ConA) and bovine serum albumin (BSA) were purchased from Sigma-Aldrich Co. LLC. Mannotriose-modified BSA (mannotriose-BSA) was purchased from Dextra Laboratories, Ltd. 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC), N-hydroxysuccinimide (NHS), aminoethanol, guanidine hydrochloride (Gdn-HCl), choline chloride (>98.0%), and other chemicals were purchased from Kanto Chemical Co., INC. Tetrabutylammonium bromide (>98.0%), tetrabutylphosphonium bromide (>99.0%) and tributyl-n-octylphosphonium bromide (>98.0%) were purchased from Tokyo Chemical Industry Co, Ltd. Finally, phosphoric acid crystalline (>99.999%) was purchased from Sigma-Aldrich Co. LLC. ILs were synthesized by using an ion exchange resin, as previously described [23]. Synthesized ILs were characterized using electrospray mass spectrometry. Hydrated ILs were prepared by adding a calculated volume of water to thoroughly dried ionic liquids.  
Lyophilized ConA dissolved in 10 mM phosphate buffer (pH 7.4) was separated by column (Sephadex G100), which has dextran chains. Lyophilized ConA was dissolved in buffer (5 mg/mL) and loaded on the column. Buffer was added to the column, then the eluted solution was collected by 1 mL. After collecting 15 mL of eluted solution, additional solution was exchanged from buffer to 0.2 M mannose solution. Inactivated ConA was eluted first, then retained active ConA at the resin surface was eluted after the addition of mannose solution. The concentration of eluted ConA was measured with UV-vis spectroscopy (U2450, Shimazu Corporation). After condensation, active ConA solution (8.3 mg/mL) was observed. Active ConA was prepared in hydrated [ch][dhp] to be mixed with three water molecules per ion pair (1.75 mg/mL). After diluting the solution 100 times with buffer, the mannose binding ability was measured using the biolayer interferometry method.  
Figure 1: Structure of cations and anions for ionic liquids (ILs) used in the present study.  
Purchased lyophilized ConA was dissolved at 5 mg/mL in hydrated ILs and buffer as a reference. Hydrated ILs were prepared in ratios of 3, 7, and 15 water molecules per ion pair. Lyophilized ConA was mixed overnight with prepared hydrated ILs and buffer at room temperature. After dilution with buffer, the carbohydrate-binding ability of ConA was investigated by biolayer interferometry.  
Mannose recognition and binding ability of ConA treated in hydrated ILs were analyzed after 100 times dilution with a buffer solution using biolayer interferometry ([PERSON], Fortebio Molecular Devices Ltd., Fremont, CA, USA). Mannotriose-BSA and BSA were immobilized on an AR2G sensor chip. The binding experiment was performed by first dipping the sensor in a sample, which was diluted 100 times with buffer, for 120 s to measure association. This was followed by dipping the sensor in buffer for 180 s to measure dissociation. The sensorgrams obtained with buffer containing ILs without ConA was used as reference and subtracted from the sample sensorgrams.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using JASP (Version 0.19.2) and Microsoft Excel (Version Professional Plus 2019).  
Nasal airway volume:  
The T1 and T2 nasal airway volumes of patients in the experimental group or control group were compared using a paired-samples \(t\)-test to assess differences, respectively. The nasal airway volume changes in all participants in the experimental group and control group were compared using an independent \(t\)-test to assess differences. \(p<0.05\) was considered significant. The average nasal airway volume changes in the experimental group and control group were compared.  
To test the reliability of this study, inter-reliability research and intra-reliability research were added to this study. For inter-reliability, three investigators conducted the segmentations and measured the nasal airway volumes before and after treatment for two patients' records. The nasal airway volumes measured by these 3 investigators were analyzed by intraclass correlation coefficient (ICC) statistical test. For intra-reliability research, the primary investigator repeated the T1 nasal airway segmentation of one patient's records 3 times (each time at 2-week intervals to minimize the effect of recency bias). The measurements were then analyzed using the Dahlberg error method.  
Intermolar distance:  
The T1 and T2 interpolar distances of patients in the experimental group or control group were compared using a paired-samples \(t\)-test to assess differences, respectively. The interpolar distance changes in all participants in the experimental group and control group were compared using an independent \(t\)-test to assess differences. \(p<0.05\) was considered significant.  
To test the reliability of the distance measurement results, intra-reliability research and inter-reliability research were added. The methodological details were the same as the reliability study of the nasal airway volume.  
Correlation analysis:  
To evaluate the possible relationship between the change in interpolar distance and the change in nasal airway volume, a correlation coefficient statistical analysis was performed for both the experimental and control groups. Within the group, nasal airway volume change values and interpolar distance change values for each patient were used as analysis values.
# Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning
## 3 Results  
### Inversion for Perfect Repeatability  
The ML target-oriented inversion methodology is tested in the synthetic dataset introduced in Section 2.1. Initially, we tested the ML method in noise-free data. The test was performed for the 1000 test samples, which corresponds to 20% of the total of 5000 samples. The obtained values of the SSIM index for the testing inversions are inside the interval \([0.9727;0.9999]\), which is an excellent result for image reproducibility. Moreover, the outcomes of the ML method are illustrated in the first columns of Figure 6 in which we compare true and predicted images for five cases according to SSIM criteria. The illustrated examples are located at key percentiles of the SSIM distribution on the test set: minimum (worst case), the three quartiles, and maximum (best case).  
A more detailed view of the reservoir anomaly is depicted in the last two rows of Figure 6, which shows the central vertical and central horizontal velocity profiles. We notice in these figures a very good agreement between true and predicted velocity profiles for the noiseless case. Indeed, even in the worst scenario, the predicted profiles are very close to the true profiles. As we shall see in Section 3.2, the predicted anomalies are impacted by non-repeatability noise.  
Figure 6: Inversion results of individual samples for the perfect repeatability scenario. The yellow pattern in the panels represent the velocity anomaly, the horizontal and vertical dimensions reproduce the reservoir region indicated in the rectangle of Figure 1. Comparison of true (first row) and inverted (second row) reservoir anomaly for five samples at key points of the SSIM distribution on the test subset: minimum (worst case), 25 th percentile, median, 75 th percentile, and maximum (best case). The last two rows compare, respectively, the central vertical and central horizontal velocity profiles of the velocity anomalies.
## 2 Related Works
### MRC Tasks
#### 2.2.3 Span Extraction  
Unlike the above two types of reading comprehension, this type of MRC has no candidate answer after a given question and needs to extract a piece of continuous text from the document as the answer. Specifically, given the context and the question, the machine needs to find a start position and an end position based on the question, with the text segment between the start position and the end position as the answer. In recent years, span extraction has become the most widely studied reading comprehension task. A typical example of span-extraction MRC is shown in Figure 3.  
Figure 2: Multiple-choice MRC [14].  
In meteorology, precipitation is any product of the condensation of atmospheric water vapor that falls under gravity. The main forms of precipitation include drizzle, rain, sleet, snow, graupel and hail... Precipitation forms as smaller droplets coalesce via collision with other rain drops or ice crystals within a cloud. Short, intense periods of rain in scattered locations are called \"showers\".  
What causes precipitation to fall? gravity  
What is another main form of precipitation besides drizzle, rain, snow, sleet and hail? graupel  
Where do water droplets collide with ice crystals to form precipitation?  
within a cloud
## 5 Methodology
### Software Used  
In terms of versions of software that we used to create this methodology, we used:  
* [noitemsep,topsep=0 pt]
* Ansible 2.17.1;
* Antrea 2.0.0;
* Flannel 0.25.4;
* Cilium 1.15.6;
* Calico 3.20.0;
* Kubernetes 1.30.2;
* iperf 3.16;
* netperf 2.70;
* MySQL 9.0.0;
* Grafana 9.5.20.  
For these evaluations, we made a stack of Ansible playbooks that installed all the necessary components, reviewed all these performance evaluations, and stored their results in a MySQL database. Initially, we worked with a set of CSV files to store and assess results but quickly realized this would become impractical, considering the amount of data we needed to analyze. We are talking about a couple thousand results that must be averaged across multiple runs (we used the arithmetic mean method). Then, data in the MySQL database is used by Grafana to produce real-time performance graphs we can investigate. This also made for a setup that we could use in production environments. However, this research aimed to create an Ansible-based methodology for these performance evaluations that anyone can use. For this reason, we published a set of Ansible playbooks, files, and scripts, implementing our methodology on GitHub [59] so everyone can evaluate and use them. They are accompanied by simple documentation (and heavily documented in code) so anyone can do the necessary configuration and run their evaluations.  
The cleanup process is also included, which tends to be very involved if it needs to be done manually. IP addresses can be changed easily for evaluations in different subnets--the subnets chosen in the paper are used because they were not used in our environment. Some parts of the code need to be changed so that this code can work for every environment. However, the changes are minimal--hostnames, network interfaces, and IP addresses.  
The Ansible part of the methodology presented in this paper starts with variable definitions--evaluated networking devices, MTU values, package sizes, and protocols (TCP and UDP so that we can do both or either one). This will ensure that all the necessary evaluations and variables are available as one file (called vars.yaml, located in each of the test_configuration folders), which can be included in other Ansible playlists, simplifying the process. Also, this file can be used to change the names of network interfaces used, simplifying the process if different interfaces need to be used.
###### Abstract  
The goal of phase retrieval is to recover an unknown signal from the random measurements consisting of the magnitude of its Fourier transform. Due to the loss of the phase information, phase retrieval is considered as an ill-posed problem. Conventional greedy algorithms, e.g., greedy spare phase retrieval (GESPAR), were developed to solve this problem by using prior knowledge of the unknown signal. However, due to the defect of the Gauss-Newton method in the local convergence problem, especially when the residual is large, it is very difficult to use this method in GESPAR to efficiently solve the non-convex optimization problem. In order to improve the performance of the greedy algorithm, we propose an improved phase retrieval algorithm, which is called the greedy autocorrelation retrieval Levenberg-Marquardt (GARLM) algorithm. Specifically, the proposed GARLM algorithm is a local search iterative algorithm to recover the sparse signal from its Fourier transform magnitude. The proposed algorithm is preferred to existing greedy methods of phase retrieval, since at each iteration the problem of minimizing the objective function over a given support is solved by using the improved Levenberg-Marquardt (ILM) method and matrix transform. A local search procedure such as the 2-opt method is then invoked to get the optimal estimation. Simulation results are given to show that the proposed algorithm performs better than the conventional GESPAR algorithm.  
phase retrieval; Levenberg-Marquardt; sparse signal; compressive sensing Article