esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Machine Learning for Quantum Annealing Accuracy Prediction
## 4 Discussion  
In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance sent to the D-Wave 2000Q annealer. We focus on the MC problem, and train several machine learning models on several thousand randomly generated input problems with the aim to learn features to (a) predict if D-Wave 2000Q will be able to solve an instance of MC to optimality, and (b) predict the size of the clique that the D-Wave 2000Q device will find.  
We show that indeed, for specific problem of predicting the clique size, a relatively small number of features of the input problem, its QUBO formulation and its embedding onto the D-Wave Chimera architecture seems to suffice to predict solubility with high precision. Those features can be summarized in a simple decision tree, which even allows one to manually classify MC instances. A regression analysis demonstrated that the clique size the D-Wave 2000Q will find can, likewise, be predicted with a low root mean square error.  
This article leaves scope for a variety of avenues for future work. For instance, it is unknown how these results generalize to other NP-hard problems, how well prediction will work on future D-Wave machines (or even other quantum annealers), and it remains to be investigated if the presented results can be improved by fitting more sophisticated machine learning models.  
Methodology, A.B. and E.P.; software, A.B. and E.P.; writing--original draft, G.H.; writing--review & editing, H.N.D.; funding acquisition, H.N.D. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the US Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218 CNA000001) and by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project numbers 20190065 DR and 20180267 ER. The work of [PERSON] has been also partially supported by the Grant No. BG05M2 OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program (2014-2020) and co-financed by the European Union through the European Structural and Investment Funds.  
**Data Availability Statement**: Not applicable.  
Figure 5: Regression via gradient boosting. (**Left**): predicted D-Wave clique size vs. true D-Wave clique size. (**Right**): permutation importance ranking. Setting of random annealing time and random UTC prefactor.
# The Architecture of the Access Protocols of the Global Infocommunication Resources +
## 2 Materials and Methods
### Step II. Data Transfer from the Terminal Device to the Base Station
#### 2.2.3 Numerical Experiments
Furthermore, the higher the intensity of input flows of user data blocks, the greater the probability of failure (\(P\)) should be selected for the stable operation of the access radio network. So, for stable operation of the radio network when \(\mu=200\) [1/s] it is advisable to discard at least 10% of traffic, and when \(\mu=225\) [1/s] no less than 20%. Figure 8: The dependence of the average transmission time of user data blocks on the probability of failure in service. Figure 7: The probability that the user blocks being transmitted will be delivered in a timely manner depends on the probability of a denial of service. By varying the value of the access probability at the first service step, taking into account the current intensity of user data blocks appearing for transmission, you can achieve optimal network load and prevent network overload situations (loss of ergodicity). In a traditional decentralized architecture, terminal access equipment and programs [13; 14] are used inefficiently, only during service receipt. In the proposed centralized sharing architecture, it is possible to programmatically manage the use of the AD and the quality of user service. In fact, numerical experiments show that a new mechanism for adaptive network management appears at the last mile.
## 2 Measurement Campaign
### The LORA-11 Passive Radar System  
The LORA-11 passive radar system (see Figure 3) is a software-defined, radio-based (SDR) passive radar designed to operate in UHF and VHF frequency bands, thus exploiting either DAB or DVB-T illuminators of opportunity. It consists of 12 parallel receiving channels, and it can digitize up to 32 MHz signal bandwidth simultaneously. The 12 receiving channels are synchronized with a common local oscillator (LO) disciplined via a GPS. Each receiving channel is digitized with 16 bits analog to digital converters (ADC), which ensure up to 96 dB of dynamic range. Of the 12 receiving channels, 11 are then connected to the LORA-11 antenna, which is a uniform linear array of discone elements arranged horizontally. The LORA-11 antenna enables beamforming, and it serves as a surveillance channel for target detection and localization (in azimuth). The 12 th channel is connected to a dedicated antenna that is positioned in a such a way to allow for the reception of the direct signal as cleanly as possible. The antenna system is calibrated using an external reference signal of known origin. Calibration coefficients can be obtained from direct signals of the surrounding broadcasting transmitters.  
Figure 2: Signal attenuation prediction between transmitter (Tx) and receiver (Rx). The red line represents the hypothetical line of sight between transmitter and receiver corrected for the earth’s curvature.
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 2 Setup Design and Realization
### Setup for TC-008: Broadband Inteference  
The aim of these tests is the assessment of the performance in terms of position accuracy and re-acquisition capability under broadband interference. For the test, simulated signals are used, a static scenario is used as baseline, and then, interference is added using an interference generator in charge of transmitting broadband interference with the following characteristics: a frequency of 1575.42 MHz, an interference bandwidth of 1 MHz, and total RMS power of \(-\)110.5 dBm. The interference generator is switched on/off according to the following schedule: 12:00:00-12:10:00 interference OFF; 12:10:00-12:50:00: interference ON; 12:50:00-13:00:00: interference OFF. In addition, the simulator is set up to disable the generation of GPS/SBAS signals for one minute every 10 min to verify that the receiver can acquire GPS/SBAS signals under interference conditions. The pass-fail criteria include 95% horizontal errors and solution availability--the receiver should not fail to report a position more than 5% of the time.
# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 3 Methodology
### Audio Feature Extraction  
Extracting features from audio files play a crucial role in the classification process. For the proposed research, the goal is to develop a custom set of dominant features that are able to separate classes reliably.  
Feature extraction process from raw audio files include windowing with appropriate frame size, applying short-term Fourier transform, employing appropriate overlap and window function to squeeze as much data as possible, thus reducing the spectral leakage [47]. We have used a sampling frequency of 16 KHz, a frame size of 512 sam  
Figure 4: Spectrogram of scream at different SNR (30 dB, 5 dB, \(-\)10 dB).  
Figure 5: t-SNE plot of custom dataset showing five different clusters referring to five classes.  
ples (32 ms), and a 50% overlap (256 samples or 16 ms) between frames. A total of 16 features were extracted per 32 ms frame duration. The Python library _'Librosa'_[48] was used to extract these features and store it in a comma-separated (csv) file for use in the further classification process. The following section discusses different features used in this research work.  
* Mel-frequency cepstral coefficients (MFCCs): MFCCs have been widely used in previous research on scream detection [49]. These features have received immense popularity for their relevance to human auditory perception. MFCCs are short-term spectral-based features [50]. We find that the first 13 coefficients are the most important ones for scream detection. These coefficients are calculated for each frame.
* Zero crossing rate (ZCR): ZCR represents the number of times in each frame the signal's amplitude passes through a value of zero [51]. Usually, screams tend to have lower ZCR than speech and noise signals due to higher amplitude over zero for a longer duration of time; therefore, we have selected this important feature.
* Spectral centroid (SC): This measures the shape of the spectrum. The higher values in spectral centroid correspond to brighter sound [52]. Usually, screams tend to have high SC due to the presence of high energy over background noise and human speech.
* Root-mean-square energy (RMSE): RMSE is the square root of the mean of total energy calculated from each audio file. This feature is computed using Equation (3) for every 512 samples with an overlap of 256 samples as using Equation (3) where \(x_{i}\) is the amplitude of \(i\)th sample.  
\[\text{RMSE}=\sqrt{\frac{1}{512}\sum_{i=1}^{512}x_{i}^{2}}, \tag{3}\]  
We also looked into two important features of scream, 'pitch' and 'loudness'. However, the library function we used for feature extraction is unable to extract pitch and loudness correctly for low SNR conditions (\(-10\) dB, \(-5\) dB, \(0\) dB). Therefore, we decided not to use these features in our feature set. Table 2 shows the feature description and their number for our proposed model.
## 2 Preliminaries
### Coarse-Grained Reconfigurable Array  
FPGAs have proven their potential for accelerating High-Performance Computing (HPC) applications. Traditionally, such a device primarily contains fine-grained elements, such as look-up tables (LUTs), switch blocks (SBs), and connection blocks (CBs), as the basic programmable logic blocks. However, traditional implementations suffer from high reconfiguration and development costs [20]. To solve the above problems, programmable logic components are defined at a higher level of abstraction. These components are called Processing Elements (PEs) and the group of PEs along with the inter-connection network form an architecture called CGRA [21]. This abstraction facilitates faster reconfiguration/replacement of applications.  
The CGRA introduced in this paper is shown in Figure 2. It consists of reconfigurable PEs which are connected to each other using reconfigurable Virtual Switch Blocks (VSBs). In the CGRA tool flow (right-hand side of Figure 2), the user will determine the CGRA settings that will configure the required configurable components of the CGRA to realize the desired application [22,23]. In CGRA, PE is the main unit that executes the application and can be configured by IP cores, Functional Units (FU) or Arithmetic Units (AU) with different granularities. It occupies most of the entire CGRA area and is the easiest target for HTs. At the same time, its redundancy and reconfigurability characteristics make it difficult to detect HTs. Therefore, it is very necessary to implement a hardware Trojan mitigation mechanism in PEs.
# Banana Peels: A Genuine Waste or a Wonderful Opportunity?
## 4 Potentials of Banana Peels
### Anti-Bacterial Activity  
Because germs are becoming more resistant to the present anti-bacterial treatments, new anti-bacterial agents are now needed. To create novel anti-bacterial agents, researchers are looking at plant materials [95]. The banana peel has anti-microbial effects against both Gram-positive and Gram-negative bacteria such as _Escherichia coli_, _Salmonella enteritidis_ and _Pseudomonas aeruginosa_[91]. Gram-positive species such as _Staphylococcus aureus_, _Bacillus subtilis_ and _Bacillus cereus_ are impacted by banana peels. Peels are also active against _Salmonella typhi_, _Microoccocus luteus_ and _Klebsiella pneumoniae_[90; 96]. _Aggregatibacter actinocytemcomitans_ and _Porphytomonas gingivalis_ are two periodontal infections that the banana peel effectively inhibits. Compared to dried peels, fresh banana peels exhibit the largest zone of inhibition against these microorganisms [97]. Moreover, unripe bananas were observed to have higher anti-bacterial activity due to the content of bioactive compounds such as polyphenols, flavonoids, tannins, alkaloids, terpenoids and glycosides [97]. These anti-bacterial metabolites impact the cell membrane of bacteria, which leads to irregular cell shapes and, ultimately, cell death [98]. A study observed the constricted nature of banana peel tannins, which are effective on bacterial peptidoglycan and proteins, thus causing reduced bacterial activity [99].
## 3 The SensoTube Architecture
### Evaluation and Testing Layer (ETL)  
The behavior of WSAN systems is severely differentiated when they are deployed in the real-world applications environment [21], and practically, this behavior cannot be simulated [135]. Moreover, the detection of possible faults is of crucial importance for the remote system [136]. Traditionally, WSAN designers and developers use various tools for evaluation and diagnostics, referred to as testbeds [29; 137]. Ideally, as reported in [135], an evaluation tool should be scalable, flexible, accurate, repeatable, visible, cross-environment valid, and re-usable. Unfortunately, there are very few testbeds available today [138], and, on the other hand, they appear to be inappropriate for in-situ post-deployment testing [117; 135]. A thorough study of WSAN testbeds is reported in some studies [139; 140]. Through the use of the ETL shields, the SensoTube architecture allows for real-time in-situ monitoring and testing of every single operation of particular circuits and procedures of the WSAN system. In other words, an ETL shield can be considered as the testbed inside the final system. A SensoTube-based system may incorporate more than one ETL shield. An ETL shield is not intrusive on other functional shields and can be easily removed at any time. Among the most interesting testing operations that can be implemented onto an ETL shield are the in-system energy monitoring, the control over the networking protocol execution, the diagnostics of malfunctions in the firmware of MCUs, energy storage monitoring, reliability and lifetime anomalies detection [18; 141] etc. Obviously, the ETL entity can open up new horizons for a WSANs' characterization and modeling based on the systems' behavior, under real-world deployment conditions.
###### Abstract  
The recent rapid development of low Earth orbit (LEO) constellation-based navigation techniques has enhanced the ability of position, navigation, and timing (PNT) services in deep attenuation and interference environments. However, existing navigation modulations face the challenges of high acquisition complexity and do not improve measurement precision at the same signal strength. We propose a pulsed orthogonal time frequency space (Pulse-OTFS) signal, which naturally converts continuous signals into pulses through a special delay-Doppler domain pseudorandom noise (PRN) code sequence arrangement. The performance evaluation indicates that the proposed signal reduces at least 89.4% of the acquisition complexity. The delay measurement accuracy is about 8 dB better than that of the traditional binary phase shift keying (BPSK) signals with the same bandwidth. It also provides superior compatibility and anti-multipath performance. The advantages of fast acquisition and high-precision measurement are verified by processing the real signal in the developed software receiver. As Pulse-OTFS occupies only one time slot of a signal period, it can be easily integrated with OTFS-modulated communication signals and used as a navigation signal from broadband LEO satellites as an effective complement to the global navigation satellite system (GNSS).  
LEO PNT; satellite navigation; OTFS; pulsed signal; performance evaluation +
Footnote †: journal: remote sensing  
Article  
Pulsed Orthogonal Time Frequency Space: A Fast and High-Precision Measurement Signal for Low Earth Orbit Position, Navigation, and Timing. _Remote Sens._**2024**, _16_, _4432_[[https://doi.org/10.3309/s16234432](https://doi.org/10.3309/s16234432)]([https://doi.org/10.3309/s16234432](https://doi.org/10.3309/s16234432))
## 3 Methodology
### DSAs--Historical Shoreline Analysis  
Digital Shoreline Analysis System (DSAS) version 5.1 [31] was used to analyze the historical evolution of the coastline. For its analysis, the study region was divided into three parts, in addition to the cliff area (see Figure 1c). In total, there are 40 transects, 3 of which are in zone A (numbered 1 to 3), 14 are in zone B (numbered 4 to 17), 12 are in zone C (numbered 18 to 29) and 11 are in the zone D or cliff zone (numbered 1d to 11d). The transects are spaced 50 m apart in zones A and D, while in zones B and C, they were spaced 100 m apart. Each transect is a line perpendicular to the coastline, which joins the intersection points of the coastline of each of the available images (dates) with said perpendicular line.  
The first analysis of the historical data was performed considering the complete time series from 1930 to 2022, except for zones A and D. In zone A, the analysis was conducted up to 1989, as this area was removed during the construction of the north breakwater and the channeling of the Segura River. Zone D was analyzed from 1999 to 2022 after human intervention. In addition, to improve the analysis and interpretation of the results and consider the important anthropic actions in zones B and C, an analysis of two-time intervals was also carried out: 1930-1989 (before the channeling of the Segura River) and 1997-2022 (after the construction of the marina and artificial dune).  
The classical linear regression model was selected to estimate the rates of shoreline change. This technique is based on accepted statistical concepts, includes all the data of the series and provides the necessary data for the prediction models used in this research. In detail, the data provided by the DSA software with this calculation method are as follows:  
* Net shoreline movement (NSM): Maximum displacement of each transect between the first and the last available data. Measured in meters.
* Linear regression rate (LRR): A least-squares linear regression is fitted to all points of a transect. The historical rate of change in the shoreline is the slope of the fitted linear regression. Measured in meters per year (m/yr).
* Standard error of the slope (LCI): This estimator is often referred to as the standard deviation and describes the uncertainty (or variability) associated with the calculation of the LRR at a given confidence interval. In this case, it was calculated at 99%, thus ensuring that 99% of the data is within the LRR \(\pm\) LCI. Measured also in meters per year (m/yr).
* R-squared of linear regression (LR2): Also known as the coefficient of determination, it is the percentage of variance in the data that is explained by a regression. Values close to 1 imply that the regression fits the data well, while values close to 0 imply that the regression does not fit as well.
# Mechanical Properties and Flexural Behavior of Sustainable Bamboo Fiber-Reinforced Mortar
## 2 Materials and Methodology
### Mortar Mixtures  
A mortar mixture containing 547 kg/m\({}^{3}\) of Ordinary Portland cement, CEM I, according to Singapore Standard SS EN 197-1 [46] and 656 kg/m\({}^{3}\) of fly ash Class C, according to ASTM C 618 [47], was used as a control mixture. Different contents of bamboo fibers (4, 6, and 8% by volume of the concrete) were added to investigate the resulting mechanical and fracture behavior. Fine sand with a maximum grain size of 2 mm was used as aggregates. The sieve curve of the sand is shown in Figure 2. To adjust the workability of the mixtures, a superplasticizer (ACE 8538, BASF), was added to the mixture. To ensure a constant mix procedure and fiber distribution trail mixes were prepared to evaluate mixing time and the amount of superplasticizer needed to achieve good workability. At first Cement, Fly Ash and aggregates were mixed for 90 s before water containing superplasticizer was added and mixed for 5 min. Finally, the bamboo fibers were gradually added and the constituents were mixed for another 3 min before filled into the molds. To evaluate the workability of the mixtures a flow table test was carried out according to ASTM C1437 [48]. All mixtures showed a spread of more than 255 mm diameter (maximum diameter of the flow table test apparatus according to [49]) and therefore could be filled well in the molds and compacted.The compositions of all mixtures used in this study are provided in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fiber Category** & **300 \(\upmu\)m** & **500 \(\upmu\)m** \\ \hline Mean value of fiber length [mm] & 7.2 & 7.8 \\ Standard deviation [mm] & 2.4 & 2.5 \\ Standard deviation [\%] & 32.7 & 32.3 \\ Mean value of fiber diameter [\(\upmu\)m] & 415.7 & 680.1 \\ Standard deviation [\(\upmu\)m] & 79.4 & 167.7 \\ Standard deviation [\%] & 19.1 & 24.7 \\ Mean value of Aspect ratio [-] & 17.3 & 11.5 \\ Standard deviation [mm] & 6.4 & 6.2 \\ Standard deviation [\%] & 37.10 & 54.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometry of the bamboo fibers.  
Figure 2: Particle size distribution of the fine aggregates used in this study.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Zonal Location Based on Arrival Time  
Using this method requires many single component sensors. These sensors are installed in appropriate locations that can effectively detect acoustic emission/microseismic (AE/MS) activity [67]. Then, based on the observed arrival time, velocity model and sensor coordinates, an arrival time function is established for each sensor. The following is the simplest form of this function:  
\[\sqrt{\left(x_{i}-x\right)^{2}+\left(y_{i}-y\right)^{2}+\left(z_{i}-z\right)^ {2}}=v(t_{i}-t)\]  
The equation represents arrival time functions that assume a constant velocity model. And (\(x\), \(y\), \(z\)) are the source coordinates, (\(x_{i}\), \(y_{i}\), \(z_{i}\)) are the coordinates of the ith transducer, \(t_{i}\) is the arrival time at the ith transducer, \(v\) is the stress wave propagation velocity and \(i\) = 1, 2, \(\ldots\), \(N\), \(\ldots\), \(M\). (\(M\)\(\geq\)\(N\)). d. When M = N, we can determine the unknown precisely; when M > N, the problem becomes overdetermined and a regression method must be used for the optimum solution.