esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of Free and Bound Phenolic Extracts  
An extraction procedure that allows to obtain the free and bound fractions of phenolics from bread samples was applied. This extraction procedure allows selective quantification of each fraction of phenolics, providing more information regarding the fate of phenolics and antioxidant activity in the final product. Free phenolic extracts of SWAP and freeze-dried fortified breads with SWAP were isolated as follows: SWAP (0.05 g) or bread samples (0.5 g) were mixed with 70% (\(v/v\)) aqueous methanol (20 mL or 5 mL, respectively) and extracted for 2 min by an ultrasonic homogenizer (Sonoplus model HD 4100, Berlin, Germany), consisting of a generator GM 4200, a converter UW 100 and a titanium probe TS 103 with 3 mm diameter and operating at 20 kHz under a working amplitude equal to 50%. The extracts were then centrifuged (Universal 320R, Hettich, Germany) at 4000 rpm (2680\(\times\)\(g\)) for 10 min, and the supernatants were stored at \(-\)20 \({}^{\circ}\)C until further usage. Three replications were conducted for each sample.  
The residual pellet of bread samples, obtained after removing free phenolic compounds, was further processed immediately for quantifying the bound phenolic extracts. Specifically, 20 mL of 4.0 M sodium hydroxide was added to the pellet, and the content was extracted on an ultrasonic bath (frequency 37 kHz, model FB 15051, Thermo Fisher Scientific Inc., Loughborough, UK) for 1.5 h at 40 \({}^{\circ}\)C. Then, the mixture was cooled, acidified to pH 2.0 with 37% hydrochloric acid, and centrifuged at 4000\(\times\)\(g\) for 10 min. The acidic supernatant was transferred into a clean separatory funnel, extracted three times with 30 mL ethyl acetate for each time, then pooled together, and eventually dried under a rotary evaporator until dryness. The extracted residue was re-dissolved in 2 mL of 70% methanol in water (\(v/v\)), and the bound extracts were stored at \(-\)20 \({}^{\circ}\)C until further analysis. Three replications were conducted for each sample.  
Spectroscopic Determination of Total Phenolics, Total Flavonoids, and Antioxidant Activity of Fortified Breads with SWAP
###### Abstract  
Introduction: Cone Beam Computed Tomography (CBCT) scans should be digitally oriented using a reference plane before being evaluated for patient growth and treatment outcome. Current orientation planes are usually dependent on the Porion points. Using more reliable landmarks than the Porion, we introduce the Acta plane and investigate its reliability as an alternative reference plane for cases in which the Porion point is inaccessible. Methods: To test its reliability, a dental student and an orthodnitst located sixteen skeletal and dental landmarks in sixteen CBCT scans before and after reorientation. Also, four measurements were calculated based on these landmarks' coordinates. The reorientation of these CBCT scans with the Acta plane was performed by another dental student. The Intraclass Correlation Coefficient (ICC) was calculated before and after orientation, as well as the intra and inter-examiner. The data were tested for significance through the Shapiro-Wilk test with a threshold set at 0.05. Through the ICC variation of the points' coordinates, this article also evaluated the reliability of this plane with the t-paired and exact-sign tests. Results: The identification of landmarks was more reliable in the reoriented CBCT images than in the original images, with a significant increase of 0.012 in the median of the ICC. Linear and angular measures showed no significant differences between groups. Conclusion: This plane is a reliable alternative for the orientation of CBCT images. The design of the Acta plane might allow the use of a smaller field of view and, consequently, a reduction in the radiation dose.  
Keywords:diagnostic imaging; cone-beam computed tomography; three-dimensional imaging; patient positioning; spatial orientation; observer variation; reproducibility of results +
Footnote †: journal: Medical Imaging
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 7 Conclusions  
In this work, the first hardware implementation and evaluation of an eye-opening PUF is presented, which combines stable responses with rapid readouts and until now has only been validated by Monte Carlo simulation. FPGA-specific routing issues were explained. To compensate for inequalities in crossing paths due to challenge inputs, inspired by [10], the paths were split using SDEs. Further global bias-inducing asymmetries from the source of entropy to the arbiter input were compensated by a newly proposed instance-specific tuning approach. The specific stabilizing feature of the eye-opening architecture, using an adjustable deadzone, was presented, and the bit error rates for different deadzones were investigated, which could reduce the BER \(<10^{-4}\%\) without further error correction. Regarding the amount of oscillations, an upper bound of 1.54 \(\upmu\)s evaluation time per bit was  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **This Work** & [13; 17] & [19] & [10] & [35] & [11] & [42] \\ \hline Architecture & EoA & RO & Loop-PUF & Arbiter & TERO & BR-PUF & DL-PUF \\ bits per instance a  & 63/264 b  & 1 & \(1\times 10^{6}\) b  & 264 b  \\ Throughput [\(\upmu\)s] & **0.85 c**  & 10 & 20,000 & n.a. & 0.62 & \(35-47\) & n.a. \\ HDintra/BER [\%] & \(<\)**10\({}^{-4}\)/2 e**  & 1.7 & \(<\)**10\({}^{-4}\)** & 2 & 2.5 & 2.19 & 0.38 \\ HDinter/Uniqueness \(\pm\)\(\sigma\) [\%] & \(50.0\pm 0.2\) & \(49.2\pm 1.3\) & 95 d  & n.a. & 48.58 & 45 & 49.994 \\ Uniformity \(\pm\)\(\sigma\) [\%] & \(50.2\pm 4\) & n.a. & n.a. & \(\approx\)50 f  & n.a. & n.a. & 50.016 \\ resources in slice per bit & \(\frac{65}{63}\approx 1.03\) & 2 & n.a. & n.a. & 4 & n.a. & n.a. \\ intrinsic autodetection & **yes** & no & no & no & no & no \\ of unstable bits & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Comparison with other FPGA-PUF implementations.  
found. Additionally, a new concept of auto error detection was introduced, which is based on the amount of oscillations to exceed the predefined deadzone. The dependence of the error rate on varying environmental temperatures was discussed, as well as compensation techniques, using the intrinsic features of the EoA-PUF. Overall, the EoA-PUF outperforms other state-of-the-art implementations as it combines fast and stable readouts, achieving a mean readout time of 0.85 \(\upmu\)s and a BER \(\leq 10^{-4}\%\).  
Conceptualization, H.M., B.D. and J.S.; methodology, H.M. and M.O.; software, H.M. and J.S.; validation, H.M. and J.S.; formal analysis, H.M.; investigation, H.M.; resources, H.M.; data curation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, H.M., B.D., J.B. and M.O.; visualization, H.M.; supervision, J.B. and M.O.; project administration, M.O.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the German Federal Ministry of Education and Research project VE-VIDES with grant number 16 ME0253.  
Dataset available on request from the authors.
# Pore Size Distribution in Granular Material Microstructure
## 2 Synthetic Specim Generation  
The macro behavior of granular materials is highly dependent on particle arrangement, local and overall porosity [34; 35; 36]. While extensive research has shown that granular materials can be reproduced numerically [19; 37; 38; 29], careful attention should be paid to the packing generation method to achieve reasonable equivalence between physical and simulated materials. Accordingly, a thorough discussion of the methods employed in the present study follows. In general, the structure is represented as random packings of spherical particles, produced via dynamic or geometrical methods. In dynamic packing simulations, particles are subject to [PERSON]'s second law of motion and contacts are treated as a linear-spring-dashpot system, after the method developed by [PERSON] [39], known as the Discrete Element Method (DEM). While DEM can produce any desired randomly packed structure in a predefined density range, particles are allowed to overlap, which requires a high contact stiffness to be assigned in order to avoid numerical artifacts. Since the time step is inversely proportional to contact stiffness, the number of iterations necessary to reach convergence becomes substantially greater. Given DEM's inextricable computational expense [40], such added cost can render simulations intractable. The computational advantage of the presented approach has been discussed in a related study [16]. Similar benefits have been demonstrated by others [41; 42].  
This study focuses on the characterization of geomaterials, which are generally well-sorted and uniform in size. Therefore, for the comparative study of the pore size distribution presented herein, it was deemed suitable to analyze four mono-sized sphere packings, each pertaining to a different class following [PERSON]'s classification [43] shown in Table 1. In this paper, the terms density and solid ratio are used interchangeably.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Description** & **Packing Density** \\ \hline Very loose random packing & Spheres slowly settled & 0.56 \\ Loose random packing & Dropped into bed or packed by hand & 0.59 to 0.60 \\ Poured random packing & Spheres poured into bed & 0.609 to 0.625 \\ Dense random packing & The bed vibrated & 0.625 to 0.641 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density states of packing models, adapted from [PERSON] [43].
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
## 3 Mobile Node
### Architecture and Implementation
#### 3.2.1 Local Mode  
In this mode, the Mobile node is seen by the network as a single, multi-sensor node. To do so, an enhanced frame is constructed containing the information from all the sensors present in the Transmitter nodes within the Mobile node.  
Once data from all the sensors have been received, the Receiver node builds an enhanced frame with data from all of them, includes location data and updates the internal database. Synchronization is then performed with the external database (via 3G or Wi-Fi, if available), where the information appears as data from a single node. A simplified protocol message flow diagram for this working mode is shown in Figure 5.  
Figure 6 shows an example of part of an enhanced frame built integrating information from different Transmitter nodes. In this case, a Gas node including several gas sensors is installed. Location data are included by the Receiver node in an additional sensor field.  
The local mode limits the communications, but only in the 3G/Wi-Fi segment. Thus, the number of data available at the external database is reduced: since a frame is updated in the database only when all Transmitter nodes have sent their data, the dynamics of the data acquisition is as slow as the slowest sensor present. Transmitter nodes keep their own pace sending their data to the Receiver node, but only the most recent data from every sensor are used to construct the enhanced frame. Depending of the types of nodes, real-time presentation of the data might be difficult to attain.  
Figure 4: ASCII frame structure employed in the mobile node.
## 5 Simulation Results and Analysis
### Comparison with Existing State-of-the-Art Techniques  
A comparison of the PRA of the proposed AMR approach with that of existing state-of-art techniques is quantitatively evaluated in Table 6. As seen from Table 6, for [60], the recognition accuracy for BPSK and QPSK was very low at an SNR of 5 dB, while in comparison, our proposed AMR approach achieved 100% at an SNR of 0 dB. Similarly, for [64], the Gabor filter network was used to classify QAM, 16-QAM, and 64-QAM at an SNR of 5 dB with a PRA of 72.35%, 71.94%, and 69.96%. For the same set of modulation schemes, SNR, and number of samples, our proposed AMR approach gave almost 100% accuracy. In our previous work [54], although we achieved higher accuracy for QAM, 16-QAM, and 64-QAM at an SNR of 5 dB, almost 100% accuracy was achieved for all of the considered modulation schemes at an SNR of 0 dB. For the AWGN comparison, as shown in Table 6, the proposed AMR outperformed the others for the all of the modulation formats, even at lower SNRs and numbers of samples.  
The PRA of the proposed AMR approach was compared with that of [54] in the presence of a Rayleigh fading channel, as shown in Table 7. The remarkable improvement in the performance of the proposed AMR approach can be seen in Table 7. The PRA was compared for 512 and 1024 samples at SNRs of 0 and 5 dB. In [54], the authors only used the cumulants as a feature set and the KNN as a classifier, but in the proposed AMR approach, the super cumulant features were optimized by using the GA, which showed supremacy in terms of PRA.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Modulation Schemes** & **[PERSON] et al.** & **[PERSON] et al.** [36] & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **Proposed** & **Classifier** \\ \hline
**No. of Samples** & **–** & **–** & **–** & **–** & **2048 Samples** & **1024 Samples** & **1024 Samples** \\ \hline
**SNR** & **0 dB** & **5 dB** & **0 dB** & **5 dB** & **0 dB** & **8 dB** & **0 dB** & **5 dB** & **0 dB** & **5 dB** \\ \hline BPSK & 50 & 65 & - & 98 & - & 99 & - & - & 98 & 99.9 & 100 & 100 \\ QPSK & 73 & 86 & - & 98 & - & 98 & - & - & 99.9 & 100 & 99.9 & 100 \\ QAM & - & - & 96 & - & - & - & 72 & 98 & 91 & 99 & 94 & 99.9 \\
16-QAM & - & - & 97 & - & - & 97 & 72 & 97 & 99.8 & 99.9 & 99.9 & 100 \\
64-QAM & - & - & 98 & - & - & 97 & 70 & 98 & 99 & 99.9 & 99.9 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: PRA comparison on the AWGN channel model.  
Figure 7: PRA for 64-QAM signals.
_Algorithms_**2012**, \(5\), 330-363; doi:10.3390/a5030330  
_algorithms_  
ISSN **1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**Use of Logistic Regression for Forecasting Short-Term Volcanic Activity**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Central Florida Remote Sensing Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Box-162450, Orlando, FL 32816-2450, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Graduate Faculty Scholar, School of Electrical Engineering and Computer Science, University of Central Florida, Box-162450, Orlando, FL 32816-2450, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-407-823-6603; Fax: +1-407-868-1035.  
_Received: 15 May 2012; in revised form: 28 July 2012 / Accepted: 7 August 2012 / Published: 22 August 2012_  
**Abstract:** An algorithm that forecasts volcanic activity using an event tree decision making framework and logistic regression has been developed, characterized, and validated. The suite of empirical models that drive the system were derived from a sparse and geographically diverse dataset comprised of source modeling results, volcano monitoring data, and historic information from analog volcanoes. Bootstrapping techniques were applied to the training dataset to allow for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and rising eruption frequency. Cross validation yielded a series of receiver operating characteristic curves with areas ranging between 0.78 and 0.81, indicating that the algorithm has good forecasting capabilities. Our results suggest that the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information.  
**Keywords:** logistic regression; eruption forecasting; event tree
## 4 Results and Discussion
### Broadening Coefficient, Ar  
The same method was used to conduct the measurements of NO-Ar broadening. The spectrum treatment was the same as for measurements of CO\({}_{2}\) and N\({}_{2}\) broadening. Figure 4a shows the measured NO absorption spectrum in Ar (938.3 mbar and 143.4 mbar, 294 K), the fitting residuals were around (1\(\sigma\)) 3-4 \(\times\) 10\({}^{-3}\) (optical density). Figure 4 displays the measured Lorentzian widths at various pressures (b). For Peaks 1 and 2, respectively, the measured NO-Ar broadening coefficients from the linear fit slopes are \(\gamma_{\rm Ar}\) = 42.64(22) \(\times\) 10\({}^{-3}\) and 37.67(19) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm. Additionally, the NO concentration measurement in the gas-phase oxidation of ammonia under 90% argon dilution determined in the temperature range of 1581-2720 K near 3 bar is being used and well validated with the evaluated Ar broadening coefficient data. More details on the measurement are available in the reference [27].
# Research on Multi-Robot Formation Control Based on MATD3 Algorithm
## 5 Simulation Experiments  
### Experimental Environment Setup  
The experimental hardware environment of this paper was an Intel Core i7-10700K CPU and NVIDIA GeForce RTX 3090 GPU, and a multi-robot formation simulation training environment was built based on the OpenAI platform. The simulation environment was a square two-dimensional plane with a side length of 8 m. Five robots existed in the environment, and one of them was the formation leader; the radius of each robot was set to 0.1 m, and the maximum speed limit was 0.85 m/s. Four obstacles and one target point existed in the environment; the radius of the obstacles was randomly distributed between (0.4 m, 0.6 m), and the radius of the target point was 0.05 m. At the beginning of the training, the obstacles and target point were randomly generated within a fixed range.
# Data Acquisition System Based on the Bluesky Suite in the Shanghai Synchrotron Radiation Facility
## 5 Conclusions  
A unified and extensible data acquisition system based on Bluesky suite has been developed. The data acquisition system can be invoked and executed through input instructions or through GUI. SSRF users prefer and are accustomed to using the GUI interface, so all user-defined parameters, initialization, composition, execution procedures, etc., are implemented through the GUI interface. In SSRF, massive data storage and full life-cycle management, real-time data analysis, data mining and artificial intelligence and automation are possible, and those functions are supported by the Big Data Science Center system [35; 36].  
The experiment result at BL07U shows that with up to five channel signals, the system is capable to collect signals of experimental sample, standard sample and incident photon flux simultaneously. For users, when conducting experiments at the BL07U, TEY and TFY data acquisition can be implemented at the same time thanks to the hardware layout of the beamline. In addition, the SR578 in the hardware layer filters out the medium and high frequency noise and suppresses the DC background current, giving the system the characteristics of low noise and high signal-to-noise ratio. User experimental efficiency and quality are highly improved.  
In addition, for the Bluesky suite, all devices are abstracted into only two categories: motors and detectors. Whether they are motors or detectors, they can be represented  
Figure 8: The incident photon flux detected by a clean gold mesh.  
by a Python object, in that they can be read or set. The interface is general enough to address every kind of hardware SSRF has encountered. Therefore, with the architecture, modules and interface developed for BL07U, the system can be extended to more beamlines efficiently and easily.  
Conceptualization, Z.Z., J.C., and Y.Z.; software, Y.Z. and Z.Z.; formal analysis, Y.Z., C.W., and J.C.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., C.W., J.C., and C.H.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research and the article processing charge were funded by National Natural Science Foundation of China (NO.12105351) and the Youth Innovation Promotion Association, CAS (Grant no. 2022290).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The research team would like to thank all participants for participating in the study during the pandemic.  
The authors declare no conflict of interest.
# Temporal Encoding to Reject Background Signals
## 2 Results
### Signal-to-Noise Ratio
Here, \(P_{n/s}(k)\)  
Figure 3: Measured correlation histograms for: (**a**) 5 pulse repetitions; (**b**) 100 pulse repetitions; and (**c**) 100 pulse repetitions with background noise. Poissonian distributions for the signal and noise correlation counts for: (**d**) 5 pulse repetitions; (**e**) 100 pulse repetitions; and (**f**) 100 pulse repetitions with background noise. (**g-i**) Poissonian overlap according to Equation (3) as a function of: (**g**) number of pulse repetitions; (**h**) average number of photons detected per pulse; and (**i**) average number of background noise correlations. are the the probabilities of \(k\) correlation counts occurring due to noise or signal with mean \(\lambda\), given by Equation (5). \[Overlap=\sum_{k=0}^{\infty}P_{T}(k)P_{n}(k), \tag{3}\] \[P_{T}(k)=\sum_{m=0}^{k}P_{n}(m)P_{s}(k-m).\] (4) \[P_{n/s}(k)=\frac{\lambda^{k}e^{-\lambda}}{k!} \tag{5}\]  
Figure 3g-i shows the calculated overlap for changing pulse repetitions, photon detection rate and noise correlations, respectively. The overlap reduces exponentially with pulse repetitions, faster than exponential with photon detection rates, and increases sub-exponentially with background correlations. This is understood by noting that \(\lambda\) in Equation (5) follows \(\lambda=p_{ph}^{2}N_{rep}\), where \(p_{ph}\) is the probability of detecting a photon from a single pulse, and \(N_{rep}\) is the number of pulse repetitions. Therefore, the distinguishability of binary 0 and 1 is governed by the Poissonian overlap, Equation (3), and depends on the number of sampled pulse repetitions, the received signal power, and the background intensity, with the first two parameters dominating.
# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 3 UAV View Object-Detection Method Based on YOLOv7-Tiny
### The Improved YOLOv7-tiny Algorithm  
The improved YOLOv7-tiny algorithm introduces a series of enhancements to its core components: backbone, neck, and head. In the backbone, the AK-ELAN module replaces the original ELAN module, which integrates the AKConv layer to better combine and enhance information from different network layers. Compared to the traditional ELAN module, AK-ELAN incorporates Adaptive Kernel Convolution (AKConv) and the Bi-directional Feature Pyramid Network (BiFPN) as the feature fusion network, significantly improving the ability to process and utilize features.  
In the neck network, the BiFPN structure is employed to optimize the flow of information between different feature layers, ensuring that the algorithm can adapt to and effectively handle targets of various sizes. Additionally, the head improvements include replacing the standard convolution in the traditional CBL module with Partial Convolution (PConv) and integrating the Dilated Residual (DWR) attention module, resulting in a new object-detection head called Partial_C_Detect. These technical innovations enhance the model's detection capabilities and improve its adaptability and robustness in complex environments. The detailed structure of the improved YOLOv7-tiny algorithm is shown in Figure 2.  
Figure 2: The red dotted line marks the structure of the improved YOLOv7-tiny algorithm, which differs from the original algorithm structure.