esa-sceva/satcom-chunk-collection huggingface dataset:
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Framework**} & **Security** & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{3}{c}{**Efficiency**} \\ \cline{3-8}  & & **Circuit** & **Comparable** & **Accuracy** & **Non-Linear** & **Unbounded** & **Non-Interactive** \({}^{b}\) & **SIMD** & **Small** \\  & & **Privacy** & **Accuracy** & \({}^{a}\) & & & & & **HE Params**\({}^{c}\) \\ \hline \multirow{11}{*}{LHE} & CryptoNets & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & CryptoDL & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & BNormCrypt & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & Faster-CryptoNets & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & HCNN & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & E2 DM & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & LoLa & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & CHET & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & SEALion & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\  & nGraph-HE & ✓ & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ \\ \hline \multirow{11}{*}{FHE} & FHE-DNN & ✓ & ✗ & ✓ & ✓ & ✓ & ✗ & ✓ \\  & TAPAS & ✓ & ✗ & ✓ & ✓ & ✓ & ✗ & ✓ \\  & SHE & ✓ & ✗ & ✓ & ✓ & ✓ & ✗ & ✓ \\ \hline \multirow{11}{*}{MPC} & DeepSecure & ✗ & ✗ & ✓ & ✓ & ✗ & - & - \\  & XONN & ✗ & ✓ & ✓ & ✗ & - & - \\  & GarbledNN & ✗ & ✓ & ✓ & ✗ & - & - \\  & QUOTIENT & ✗ & ✓ & ✓ & ✓ & ✗ & - \\  & Channelon & ✗ & ✓ & ✓ & ✓ & ✗ & - \\  & ABY3 & ✗ & ✓ & ✓ & ✓ & ✗ & - \\  & SecureNN & ✗ & ✓ & ✓ & ✓ & ✗ & - \\  & FalconN & ✗ & ✓ & ✓ & ✓ & ✗ & - \\  & CrypTFlow & ✗ & ✓ & ✓ & ✓ & ✗ & - \\  & Crypten & ✗ & ✓ & ✓ & ✓ & ✗ & - \\ \hline HE-MPC & Gazelle & ✓ & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ \\ hybrid & MP2 ML & ✓ & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ \\ \hline iLHE & nGraph-HE2 & ✗ & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ \\  & **HeFUN (this work)** & ✓ & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ \\ \hline \hline \end{tabular} \({}^{a}\) Among frameworks considered having comparable accuracy with plain model, LHE-based frameworks are less accurate than others because they approximate non-linear function by polynomial, while MPC-based, HE-MPC hybrid-based, and iLHE-based approaches achieve the same accuracy as the plain model (details in Section 5.3). \({}^{b}\) HeFUN requires less communication rounds than HE-MPC hybrid-based approach (details in Section 5.1). \({}^{c}\) The size of HE parameters directly impacts the inference time of SNNI frameworks. HeFUN uses smaller HE parameters than current LHE-based frameworks, hence significantly accelerating inference time (details in Section 5.3).  
\end{table} 
Table 1: Comparison of SNNI frameworks.
## 4 Animal Tracking and Safety
### Animal Location Tracking
To support the animal tracking with real-time monitoring capability, architecture is illustrated in the Figure 5. As discussed above, regarding the limitations of previous system, here a system is demonstrated for the real-time monitoring of cattle animals with the assistance of drones. In general, humans find it difficult to enter restricted areas, thus drones are capable of entering restricted areas via the air and perform necessary inspection. Figure 5: LoRa based Architecture of animal location tracking and identification. A LoRaWAN GPS tracker [71] interfaced to the cow neck, is capable of providing the GPS location and motion of a cow. This tracker comprises of a low power GPS module and 9-axis accelerometer, and these two components are interfaced to the STM32L072 MCU along with LoRa module and 1000 ma Li-on battery. The ID provided in the GPS tracker assists the drone to categorize the identification of particular dairy cattle in the grazing field. The drone is equipped with a LoRa module that allows it to communicate with cattle for knowing the location and tracking during emergency state. In addition to this, the LoRaWAN GPS tracker communicates the location and motion information of dairy cattle to the farmer/user upon request from the cloud server through the gateway. The gateway is linked to LoRa and Wi-Fi in order to transmit and receive sensor data via LoRa and the internet. The cloud server provides feasibility to the farmer/user to visualize position and motion of each dairy cattle and it enables to realize the digitalization of the animal tracking. Moreover, the geo-fencing can be added to this system, as it also triggers the events if any dairy cattle move away from the fencing of the premises. Geo-fencing relies on GPS, Wi-Fi, RFID, and cellular data, and it is achievable with the existing system because GPS is available in the cow neck.
## 3 Results
### Discrimination of Soybean Varieties  
In the first step, seven (4.1%) varieties were discriminated according to the results when using Sat_076, which had the highest allele and PIC values, and were not discriminated from 165 varieties. In step 2, 73 (42.4%) varieties were discriminated among the 165 varieties by including Sat_417. In step 3, 69 (40.1%) varieties were discriminated among 92 varieties by including Sat_043. In step 4, 13 (7.6%) of 23 varieties were discriminated when including Satt197. In step 5, 8 (4.6%) of 10 varieties were discriminated when including Satt434. In the final step, two varieties (1.2%), Mansu and Nampung, were discriminated by including Sat_179, allowing for discrimination of all 172 varieties. Of the 20 markers, Mansu and Nampung could only be discriminated when using Sat_179 (Table 8, Figure S2).
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 2 Interference Management
### Future Perspectives
#### 2.3.2 Separation of Mixed Signal with Progressive Cancellation
With the photonic circuit pre-process the mixed signals, the carrier frequencies of the SOI can be estimated and a bandpass filter 100 MHz bandwidth is applied to the mixed signals before ADC, 10 bit ADC at 100 MHz bandwidth can be achieved for another 30 dB digital cancellation. The hybrid system achieves a total of 60 dB cancellation. This applies to the receivers at the 4G cellular station, which receives a stronger interference than the mobile device. When both the interference and the SOI have large bandwidths (3 GHz), two stages of analog separation are applied with three photonic circuits. The few SOI samples that are not in the center are fake points and depend on the distribution of the Gaussian noise. As for application, the system with two stages of photonic separation applies to the receivers at 5G stations, which requires both large bandwidth and a high cancellation ratio. When the cancellation ratio goes further to 85 dB, three stages of separation are deployed. The two analog stages provide 60 dB separation, and the third digital stage provides another 25 dB separation with an 8 bit ADC at 3 GHz. This applies to the passive receivers of radio telescopes. The SOI of a radio telescope coverage a large bandwidth and is orders of magnitude (>60 dB) weaker than the interference from the cellular network [66, 67, 68, 69, 70]. Compared with commercial products, such as mobile devices and cellular stations, the design of the radio telescope station is less limited by the space, power, and cost, and therefore, more stages can be applied to achieve a higher cancellation ratio. In summary, the photonic circuit is scalable to multiple stages with a cancellation ratio multipliable across stages. Wideband and narrowband signals and can be adjusted to other bandwidths with a corresponding cancellation ratio based on the experimental results in Table 1.
# Improving Performance of Key-Value Stores for High-Performance Storage Devices
###### Abstract  
Key-value stores (KV stores) are becoming popular in both academia and industry due to their high performance and simplicity in data management. Unlike traditional database systems such as relational databases, KV stores manage data in key-value pairs and do not support relationships between the data. This simplicity enables KV stores to offer higher performance. To further improve the performance of KV stores, high-performance storage devices such as solid-state drives (SSDs) and non-volatile memory express (NVMe) SSDs have been widely adopted. These devices are intended to expedite data processing and storage. However, our studies indicate that, due to a lack of multi-thread-oriented programming, the performance of KV stores is far below the raw performance of high-performance storage devices. In this paper, we analyze the performance of existing KV stores utilizing high-performance storage devices. Our analysis reveals that the actual performance of KV stores is below the potential performance that these storage devices could offer. According to the profiling results, we argue that this performance gap is due to the coarse-grained locking mechanisms of existing KV stores. To alleviate this issue, we propose a multi-threaded compaction operation that leverages idle threads to participate in I/O operations. Our experimental results demonstrate that our scheme can improve the performance of KV stores by up to 16% by increasing the number of threads involved in I/O operations.  
key-value stores; high-performance storage devices; solid-state drives +
Footnote †: journal: Applied Sciences
# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 2 Materials and Methods
### The Used Machine Learning Methods
#### 2.2.5 The J48 Algorithm  
The J48 algorithm [97] is one of the most used supervised machine learning algorithms, used to construct decision trees for classification data. This method was tested on a series of classification problems, such as the prediction of diabetes [98], network intrusion detection [99], classification of criminal data [100], fingerprint gender classification [101], fake news classification [102], etc. Also, the J48 algorithm was used to predict forest fires using data from Slovenia in a recent work [103]. A similar study was performed in Algeria using the J48 algorithm among other machine learning models [103].
## 1 Introduction
[16] proposed a fault diagnosis method based on Bi LSTM (binary long short term memory) network in order to solve problems such as large amounts of data and difficulty in extracting features in gear fault diagnosis. The results showed that the Bi LSTM network method for gear fault diagnosis was better than the CNN and LSTM network. From what has been discussed above, it can be seen that the method of deep learning has been widely used in the field of fault diagnosis of mechanical equipment due to its strong feature extraction and other advantages such as its high efficiency, portability, and so on [17]. However, at present, all kinds of intelligent fault diagnosis algorithms mainly focus on the diagnosis of a single fault component, and few intelligent diagnosis models can simultaneously carry out comprehensive fault diagnosis for a rotating system composed of a shaft, bearing, gear, and so on. In order to solve this problem, a novel stacked auto encoders sparse filter rotating component comprehensive diagnosis model (SAFC) was proposed to extract domain invariant features of various health conditions at different speeds and classify them. The proposed model can be used to extract domain invariant features of various health conditions at different speeds. Using the spectrum of fault signal as input, the deep neural network (DNN) was established through SAE, and the back propagation (BP) algorithm was used to fine-adjust the network. Meanwhile, the batch standardization (BN) algorithm was introduced into DNN to realize the rapid training of intelligent fault diagnosis model of rotating parts. After the clustering and dimension reduction of the data, the data was input into the sparse filtering model, and the unsupervised feature extraction of the input signal was carried out by using the sparse filtering. Finally, softmax was used as the classifier to realize fault diagnosis. The SAFC model was validated by the vibration data collected, and the results show that the proposed model has high diagnostic performance. The main innovations are as follows:  
1. A novel SAFC model that can extract and classify fault signal domain invariant features is proposed. 2. The proposed method is able to process multiple rotating parts at the same time and has the ability to process large amounts of data. The domain invariant features of the same fault at different speeds can be extracted and clustered. The organizational structure of the rest of this paper is as follows. Section 2 briefly introduces the SAE and SF theories, respectively; Section 3 introduces the architecture and training methods of SAFC model; Section 4 verifies the superiority of the SAFC model through two groups of experiments; Section 5 is the conclusion.
## 4 Evaluation
### Load Test  
To test the feasibility of the sensor as a force sensor, the developed sensor was attached to a test piece with a V-notch in the middle, as shown in Figure 5. Note that the test piece was fabricated considering applications in surgery (e.g., putting a force sensor on conventional surgical forceps). The load was applied by a motorized precision stage (OSMS26, Sigmakoki, Japan) with a force gauge (ZTA-50N, IMADA, Japan). In the experiment, \(\pm\)25 N tension and compression were applied using the experimental setup shown in Figure 5.  
The measurement was conducted three times, and each result showed the same tendency. A representative experimental result is shown in Figure 6. A linear output characteristic of the proposed sensor is suggested by the result with a correlation coefficient of 0.998. The resolution of the sensor in  
Figure 4: Positioning stage with an integrated temperature-controlled heater for sensor assembly.  
Figure 5: Load test setup. The developed sensor was attached over the V-notch of a test piece. Force was applied at the end of test piece while the other end was fixed by a view.  
this experimental setup was computed to be 119 mN with a range of \(\pm\)25 N, while the sensitivity was found to be 2.52 nm/N.
# A General Euler Angle Error Model of Strapdown Inertial Navigation Systems
## 2 The Proposed General Euler Angle Error Model  
The general Euler angle error model contains the relative Euler angle error model and the convected Euler angle error model. The relative Euler angle error model indicates the relationship between Euler angle errors and angular velocity errors of the body frame with respect to the navigation frame denoted in the body frame. The convected Euler angle error model indicates the relationship between Euler angle errors and Euler angles.
## 2 Materials and Methods
### Sources and Processing of Data
#### 2.2.2 Meteorological Data  
The meteorological data used in this study included monthly average temperatures and monthly precipitation from 2001 to 2021, sourced from the CRU TS monthly dataset (Climate Research Unit) produced by the National Centre for Atmospheric Sciences (NCAS) in the UK, [[https://crudata.uea.ac.uk/cru/data/hrg/](https://crudata.uea.ac.uk/cru/data/hrg/)]([https://crudata.uea.ac.uk/cru/data/hrg/](https://crudata.uea.ac.uk/cru/data/hrg/)) (accessed on 19 November 2022). This dataset is currently one of the most widely used climate datasets, and it includes temperature and precipitation on land worldwide from 1901 to the present, with the exception of Antarctica. Compared to other meteorological data, it has significant advantages, such as wide coverage and long observation time. Therefore, it can not only reveal long-term climate trends but also provide a scientific basis for decision-making in areas such as regional ecology, agriculture, and water resource management. The spatial resolution of the data are 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), and the format is nc data. We obtained the annual average temperature and precipitation in the study area after performing pixel statistics and inverse distance weighting interpolation.
## 4 Materials and Methods
### The Electric Field from the Dipole Antenna
Importantly, the \(E\)-maxima observed near the antenna have no effect on the field distribution at a large distance from the antenna where the field is quasi-homogeneous and the radiation pattern is isotropic. In the far-field (known as the antenna radiation zone), the electric field from a single dipole antenna is a plane wave. The modeled shortened 1-GHz half-wave dipole antenna is an the omnidirectional antenna, i.e., the waves emitted by the antenna propagate with the same intensity in each direction, the radiation pattern is torus-shaped, and the \(E\)-field disappears along the \(x\)-axis of the dipole antenna. Two of the most important characteristics of the used applicator are shown in Figure 14, i.e., the reflection coefficient and the input impedance of the dipole antenna. The definitions of these quantities are detailed described in [7]. Moreover, the characteristic extremes are visible for the resonant frequency of about 1.3 GHz (\(S_{11}\) = \(-\)16.11 dB and \(Z_{\text{in}}\) = 63.8 \(\Omega\)), as seen in the curves in Figure 14. It is worth emphasizing that a resonant frequency of the single dipole antenna differs from the 1-GHz antenna operating frequency, which is an inevitable consequence of shortening the dipole antenna size. Figure 13: The electric field derived from the 1-GHz half-wave dipole antenna with a shortening ratio set at \(k_{\text{s}}\) = 0.67: (**a**) near field distribution; (**b**) far field radiation. From the viewpoint of the discussed case of hyperthermia for female breast tissues affected by a tumor and surrounded by a matrix of 8 dipole antennas, we were particularly interested in the near field to determine the specific absorption rate (SAR) and, in effect, the breast tumor temperature.
## 2 Materials and Methods
### Test Areas and Datasets
The input data for the statistical analysis are derived from a digital elevation model (DEM) with grid cell sizes equal to 20 \(\times\) 20 m, and from categorical vector maps, all of them available in a regional GIS repository at [[https://rsdi.regione.basilicata.it/](https://rsdi.regione.basilicata.it/)]([https://rsdi.regione.basilicata.it/](https://rsdi.regione.basilicata.it/)) (accessed on 1 February 2023). Ground-displacement measurements were obtained by processing synthetic aperture radar data (SAR) via a differential interferometric (DInSAR) technique of very-high-resolution X-band Cosmo-SkyMed images. These radar images were processed by the e-GEOS company (partner of the MITIGO project financially supporting this research; see Funding section) using their commercial processing chain that implements the base concepts of the persistent scatterer interferometry (PSI) [43,44] algorithm. This required a strict phase calibration of the measured data aimed at compensating phase errors, mainly associated with propagation delays in the atmosphere of the electromagnetic radiation, which cause phase mismatches with the expected linear models associated with height and deformation contributions. For the purpose of the present study, the processed datasets consist of 156 images acquired on descending orbit (from May 2012 to August 2021) in Vaglio Basilicata and 124 images acquired on descending orbit (from May 2012 to August 2021) in Trivigno. These remote sensing data provide high coverage of the observed scene with coherent reflector benchmarks also along linear elements (i.e., infrastructure networks) [45]. Figure 2c,d show the distribution of the DInSAR benchmarks and the measured average velocity values along the sensor target line of sight (_LOS_) for the visible area in Vaglio Basilicata and Trivigno, respectively. The identification and assessment of the road damage severity level was carried out using photographic images from Google Street View (Figure 2c,d).
_Appl. Sci._**2015**, \(5\), 415-426; doi:10.3390/app5030415  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Expeditious Synthesis of Noble Metal Nanoparticles Using Vitamin B\({}_{12}\) under Microwave Irradiation  
**[PERSON] 1,2, [PERSON] 3, [PERSON] 4, [PERSON] 4,* and [PERSON] 1,* 1**  
Water Quality Management Branch, Nation Risk Management Research Laboratory,  
The Office of Research and Development, U.S. Environmental Protection Agency,  
26 W. Martin Luther King Jr. Drive, Cincinnati, OH 45268, USA;  
E-Mail: [EMAIL_ADDRESS]  
Pegasus Technical Services Inc., 46 E Hollister St, Cincinnati, OH 45219, USA  
Sycamore High School, 7400 Cornell Road, Montgomery, OH 45242, USA;  
E-Mail: [EMAIL_ADDRESS]  
Sustainable Technology Division, National Risk Management Research Laboratory,  
U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, MS 443,  
Cincinnati, OH 45268, USA; E-Mail: [EMAIL_ADDRESS]  
Authors to whom correspondence should be addressed;  
E-Mails: [EMAIL_ADDRESS] (R.S.V.); [EMAIL_ADDRESS] (M.N.N.);  
Tel.: +1-513-487-2701 (R.S.V.); +1-513-569-7232 (M.N.N.);  
Fax: +1-513-569-7677 (R.S.V.); +1-513-569-7172 (M.N.N.).  
Academic Editor: [PERSON]  
_Received: 1 July 2015 / Accepted: 10 August 2015 / Published: 19 August 2015_  
**Abstract:** A greener synthesis protocol for noble nanometals is developed using vitamin B\({}_{12}\) as a reducing and capping agent in conjunction with the use of microwaves. Successful assembly of nanoparticles or microparticles with varied shapes and sizes have been demonstrated. The synthesized Ag, Au, and Pd samples were thoroughly characterized with X-ray diffraction, scanning electron microscopy, transmission electron microscopy, high resolution transmission microscopy, and UV-visible spectrophotometry, confirming that metallic Ag, Au, and Pd were synthesized by the green chemistry method.  
**Keywords:** green synthesis; microwave irradiation; noble metal nanoparticles; vitamin B\({}_{12}\)
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Driving Behavior Characteristics Analysis Results
#### 3.1.1 Speed Characteristics  
Speed is an important indicator to characterize driving behavior. We examined speed choice patterns at sharp turns based on data from 30 drivers who took part in our driving simulation exercises and cumulatively made 60 drives (each driver undertook two trials) on the virtual stretch of road X412. This yielded 18,540 speed measurements at various points along the simulated route. Statistical indicators of speed in different sections are shown in Table 3. The results show that the speed of the driver in the curve section is significantly lower than that in the adjacent straight section, but still more than 30% of the drivers have different degrees of speeding behavior. By analyzing the correlation between the driver's speed selection and the curve geometry index, it can be seen that when the curve radius is less than 60 m, the driver's 85% fractional speed decreases significantly with the decrease in the curve radius, and the decline rate is higher than the road's design speed, indicating that it is difficult for the driver to control the speed reasonably according to the linear conditions. Based on the speed-curve radius data, a prediction model of 85% percentile speed was constructed:  
\[V_{85}=61.72+0.41R-17.35/R \tag{3}\]  
where \(V_{85}\) represents the 85 th percentile speed (km/h) and \(R\) stands for the radius of the curve (m). Experimental verification confirmed that this model can be used for predicting speeds and evaluating safety on sharp turns.