esa-sceva/satcom-chunk-collection huggingface dataset:
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 3 Eliminating Deterministic FPGA Routing Delays to Avoid EoA-PUF Bias
### Arbiter Routing Bias in FPGA-Based EoA-PUF
#### 3.2.2 PDL Tuning Approach  
In [10], the authors found a fixed tuning set for all instances by evaluating the frequency of 1-responses, averaged over several PUF instances. However, due to the nature of PUFs, all routes are affected by individual delay variations that cannot be compensated by the same value, which would cause large bias in some PUFs. In [10] (Figure 10), this can be assumed by the large distribution of the quartiles in the plot, indicating that some instances have an occurrence of 1-responses of just 5%. This problem was not further discussed in [10] and motivates an individual tuning per instance, which we present in the following.  
In order to be able to tune the arbiter routing bias without affecting the source of entropy (i.e., the ROs), the FPGA design was extended: a symmetric calibration impulse can be triggered at the outputs of the ROs \(out_{a}\) and \(out_{b}\) in Figure 1c, which then propagates to the arbiter and allows to excite only the PDLs and arbiter. By observing the arbiter (D-FF) outputs upon this pulse, one can determine if one arbiter D-FF is triggered faster than the other, inducing a bias in the final response due to asymmetric propagation delays. This allows to adjust the PDLs accordingly. For this, an experiment was conducted on the used XILINX ZYNQ-7010.  
In the following, the PDLs are named such that their first index refers to the D-FF referred to in Figure 3 (a or b), while the second index refers to the connected D-FF-port (c(lock) or d(ata)); i.e., \(\mathrm{PDL_{ad}}\) is connected to the upper D-FF\({}_{a}\)'s data port. The EoA-PUF arbiter output is written as \(\mathrm{r}=\langle\mathrm{D-FF_{b}}\rangle\langle\mathrm{D-FF_{a}}\rangle\). Thereby, \(\mathrm{r}=\langle 01\rangle\) indicates that \(\mathrm{D-FF_{b}}\) was triggered first and \(\mathrm{r}=\langle 10\rangle\) that it was triggered last. They indicate a deterministic bias in the arbiter if the symmetric calibration pulse is triggered. \(\mathrm{r}=\langle 11\rangle\) and \(\mathrm{r}=\langle 00\rangle\) appear if both D-FFs in the arbiter receive their D- and C-inputs simultaneously but either D before C or C before D, respectively. The goal of the PDL tuning is then to avoid a deterministic bias of the arbiter regarding either decision. Thus, the ideal tuning achieves a \(\langle 11\rangle\) or \(\langle 00\rangle\) decision upon the symmetric calibration pulse.  
Figure 6: Internal implementation of an n-bit PDL, consisting of n-concatenated H-SDEs.  
To speed up the tuning in the experiment, the PDL input vector was changed to unary-coded order; for all input bits, the same \(\delta\tau_{det}=7\) ps can be expected.
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 2 Analysis of Key Technologies for the Transition from Open-Pit to Underground Mining
### Selection of Mining Methods and Technical Adaptability Analysis
#### 2.2.1. Stope Mining Method  
The stope mining method relies on the self-supporting ability of the surrounding rock for excavation, eliminating the need for artificial support structures [23]. It is primarily suitable for mining areas with regular orebody shapes, strong roof stability, and simple geological conditions. This method features a straightforward process, low initial investment, and strong economic adaptability. For instance, during the transformation of the Shiren Gou Iron Mine in Hebei Iron and Steel Group, a segmented stope method was adopted based on the orebody geometry and roof structure conditions. This approach significantly enhanced unit capacity and reduced operational disturbances [24]. However, the method is highly dependent on roof stability and presents considerable safety risks in deep high-stress zones, weak surrounding rock formations, or tectonically fractured zones. Moreover, prolonged exposure of the stope may lead to void collapse, extension of surrounding rock fissures, and far-field disturbance effects, which can negatively impact the continuity and safety of subsequent operations [25]. As a result, this method is typically applied to shallow ore bodies or utilized as a transitional technology for pre-mining trials prior to backfilling.
## 2 Methodology
### Initialisation  
Initially, the excitation sequence is created where the inspector inputs the centre frequency and the number of cycles. Then, the \"_windowSize_\" is calculated based on the number of samples required for the excitation sequence. This sequence is already normalised by its maximum value. The normalisation bounds the signal amplitudes between \(1\) and \(-1\) by dividing each sample by the maximum amplitude within the signal. Since 10 cycles are typically used for inspection, the duration of the excitation sequence is small; therefore, this signal is zero padded by a factor of 4 to achieve better resolution in the spectral density. Zero padding is the operation of adding extra zeroes to the end of the sequence in order to increase the resolution of the power spectrum [24]. The power spectrum is then evaluated by applying the fast Fourier transform [24], where the magnitudes of the spectrum are saved in \"_signalRef_\" and the corresponding frequency for each bin is saved in \"_frqList_\".  
The first extracted feature from the spectrum is the maximum magnitude and its corresponding bin number which are stored in \"_maxRef_\" and \"_idFC_\", respectively. This should be the bin representing the centre frequency of excitation as set before by the inspector. Afterwards, the lowest and highest frequencies of the 10 dB BW of this spectrum are calculated. The bin numbers of the lowest and highest frequencies within this BW are saved in \"_teFLID_\" and \"_teFHID_\". If the previously calculated \"_idFC_\" is not in the centre of this spectrum, these numbers are expanded away from the centre frequency so both the lower half and the upper half of the spectrum have the same amount of bin numbers. Then, the greater magnitude achieved from these (border) bins is stored in \"_teMax_\". The flowchart of this function is shown in Figure 2 and a summary of the extracted variables are shown in Table 1. All values except \"_windowSize_\" are needed for the conditions function.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Variable Name** & **Description** & **Conditions** \\ \hline windowSize & Length of the moving window & - \\ signalRef & The power spectrum of the normalised excitation sequence & C2, C3, C4 \\ frqList & List of the corresponding frequency for each bin number & C0 \\ maxRef & Maximum magnitude achieved from signalRef & C4 \\ idFC & Bin number of maxRef & C0 \\ teFLID & Bin number of the lowest frequency within 10 dB bandwidth & C0, C1, C3 \\ teFHID & Bin number of the highest frequency within 10 dB bandwidth & C0, C1, C3 \\ teMax & The greater magnitude between teFLID and teFHD from signalRef & C3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of variables extracted from the Initialisation.  
Figure 2: Flowchart of the initialisation.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF
### Derivation of the MCCKF
From which it can be derived that  
\[K_{k}^{t-1}=P_{(k|k)}^{t-1}\Big{(}H_{k}^{t-1}\Big{)}^{T}\Big{(}H_{k}^{t-1}P_{ (k|k)}^{t-1}\Big{(}H_{k}^{t-1}\Big{)}^{T}+R_{k}^{t-1}\Big{)}^{-1} \tag{46}\]  
\[P_{(k|k)}^{t-1}=V_{(p,k|k-1)}\Big{(}W_{(x,k)}^{t-1}\Big{)}^{-1}V_{(p,k|k-1)}^{T} \tag{47}\]  
\[R_{k}^{t-1}=V_{(k,k)}\Big{(}W_{(z,k)}^{t-1}\Big{)}^{-1}V_{(k,k)}^{T} \tag{48}\]  
\[W_{(x,k)}^{t-1}=\text{diag}\Big{(}G_{\tau}\Big{(}f_{(1,k)}^{t-1}\Big{)},\cdots,G_{\tau}\Big{(}\hat{e}_{(n,k)}^{t-1}\Big{)}\Big{)} \tag{49}\]  
\[W_{(z,k)}^{t-1}=\text{diag}\Big{(}G_{\tau}\Big{(}f_{(n+1,k)}^{t-1}\Big{)}, \cdots,G_{\tau}\Big{(}f_{(n+p,k)}^{t-1}\Big{)}\Big{)} \tag{50}\]  
\[f_{(i,k)}^{t-1}=y_{(i,k)}-\text{d}_{i}\Big{(}\hat{x}_{(k|k)}^{t-1}\Big{)} \tag{51}\]  
The covariance is represented by Equation (52). \[P_{(k|k)}^{t}=\Big{(}I_{n}-K_{k}^{t-1}H_{k}^{t-1}\Big{)}P_{(k|k-1)}\Big{(}I_{n }-K_{k}^{t-1}H_{k}^{t-1}\Big{)}^{T}+K_{k}^{t-1}R_{k}\Big{(}K_{k}^{t-1}\Big{)}^{T} \tag{52}\]
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 3 Data Collection and Model Equation Development
### Data Collection  
The development of the speed limit estimation model involves data collection and analysis stages, as illustrated in Figure 10. Of the 215 sections surveyed, 36 \"safe road sections\" with low accident rates and standard deviations were selected.
## 4 Conclusions  
This study proposes synthesizing recycled Kevlar(r) fibers, LPET fibers, and PET fibers to successfully create recycled Kevlar(r)/PET/LPET nonwoven geotextiles. First, the influences of different contents of Kevlar(r) fibers on the tensile strength of nonwoven geotextiles are examined. In light of experimental results and statistically analyses, the incorporation of Kevlar(r) fibers can improve the tensile strength of the nonwoven geotextiles. In particular, 15 wt. % and 20 wt. % of Kevlar(r) fibers strengthen the tensile strength by 20% to 90%, and reaches the statistical significance of \(p\) < 0.05. Second, the influences of immersion periods on the tensile strength of the nonwoven geotextiles are examined. The correlations between a 4-month immersion in different environment conditions and the differences in the tensile strength of the nonwoven geotextiles are 0% in a neutral environment, while there is a 30%-50% decrease for the control group and a 17%-20% decrease for the experimental group in an acid environment, and a 100% decrease for the control group and a 95%-97% decrease for the experimental group in an acid environment. Statistical analyses also confirm that the experimental group has a higher tensile strength than the control group. Third, the influences of thermal treatment on the tensile strength of the nonwoven geotextiles are examined. The thermal treatment is proved to provide a 15%-45% greater tensile strength to the nonwoven geotextiles and yields a statistical significance at \(p\) < 0.05. Finally, demonstrated by the multiple regression analyses, the factors that influence the tensile strength of the nonwoven geotextiles with their corresponding environments is contents of Kevlar(r) fibers (neutral environment), immersion periods, contents of Kevlar(r) fibers, and immersion temperatures (acid environment), and immersion periods (alkaline environment). The combination of recycled Kevlar(r) fibers and LPET fibers have been proven to significantly improve the tensile strength of nonwoven geotextiles in terms of a neutral, acid, and alkali treatment.  
**Acknowledgments:** The authors would especially like to thank Ministry of Science and Technology of Taiwan, for financially supporting this research under Contract MOST 103-2622-E-035-025-CC2.  
**Author Contributions:** In this study, the concepts and designs for the experiment, all required materials, as well as processing and assessment instrument are provided by [PERSON] and [PERSON]. Data is analyzed, and experimental results are examined by [PERSON], [PERSON], [PERSON], [PERSON]. The experiment is conducted and the text is composed by [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 4 Experimental Setup, Procedure, Results, and Analysis
### Experimental Results

Table 1: Measured values of three sizes of mini-cracks based on laser excited thermography. Figure 11: Test results of a mini-crack detection. (**a**) The original image is taken with the conveyor belt at rest; (**b**) mini-crack feature image; (**c**) gray mini-crack feature image; (**d**) image enhancement; (**e**) threshold segmentation; (**f**) mini-crack size. limited by the camera frequency. In fact, the displacement of the conveyor belt from frame to frame is 0.83 mm. Accordingly, when the width of the mini-crack is less than 2 mm, it is very close to the error value so that the measurement accuracy decreases. In addition, an infrared camera of faster frame rate will help improve measurement accuracy. Figure 12 shows the precision comparison of the measurement results of 100 groups of mini-cracks. Figure 12a shows the variation trend of measurement accuracy with constant length but gradually increasing width. With the increase of the mini-crack width, the measurement accuracy of the mini-crack width 2\(\sim\)4 mm is between 90-95%, and the detection accuracy for the mini-crack with width larger than 4 mm is greater than 95%. Figure 12b shows the variation trend of measurement accuracy with constant width but gradually increasing length. It shows that the length measurement accuracy of all the measurement groups is stable at more than 96%. Therefore, this method has a greater detection accuracy for mini-cracks with widths larger than 1 mm.
## 3 Results
### Univariate Analysis
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**HREVs**} & \multicolumn{2}{c}{**Without HREVs**} \\
**Parameter** & \multicolumn{2}{c}{**(_n = 48_)**} & \multicolumn{2}{c}{**(_n = 18_)**} & \multicolumn{2}{c}{**(_n = 30_)**} & \(p\) \\  & **M \(\pm\) SD/n(\%)** & **M \(\pm\) SD/n(\%)** & **M \(\pm\) SD/n(\%)** & **M \(\pm\) SD/n(\%)** & \\ \hline Gender & & & & \\ M & 35 (72.9\%) & 17 (94.4\%) & 18 (60.0\%) & 0.017 \\ F & 13 (27.1\%) & 1 (5.6\%) & 12 (40.0\%) & \\ Age (y.o.) & 55.77 \(\pm\) 13.09 & 51.50 \(\pm\) 13.50 & 58.33 \(\pm\) 12.36 & 0.08 \\ Portal vein caliber (mm) & 13.37 \(\pm\) 1.51 & 14.11 \(\pm\) 1.63 & 12.92 \(\pm\) 1.27 & 0.007 \\ SBD (mm) & 134.25 \(\pm\) 16.26 & 142.56 \(\pm\) 13.92 & 129.27 \(\pm\) 15.702 & 0.005 \\ Albumin (g/L) & 3.53 \(\pm\) 0.59 & 3.13 \(\pm\) 0.54 & 3.77 \(\pm\) 0.50 & \textless{}0.001 \\ Platelets (\(n\)/cmm) & 129.02 \(\pm\) 45.74 & 94.72 \(\pm\) 23.71 & 149.60 \(\pm\) 43.51 & \textless{}0.001 \\ Total bilirubin (mg/dL) & 1.69 \(\pm\) 0.84 & 2.1072 \(\pm\) 0.80 & 1.44 \(\pm\) 0.77 & 0.007 \\ INR & 1.32 \(\pm\) 0.26 & 1.44 \(\pm\) 0.34 & 1.24 \(\pm\) 0.15 & 0.022 \\ TGO & 69.33 \(\pm\) 54.81 & 91.94 \(\pm\) 63.76 & 55.77 \(\pm\) 44.47 & 0.067 \\ TGP & 49.17 \(\pm\) 39.02 & 57.67 \(\pm\) 43.60 & 44.07 \(\pm\) 35.80 & 0.160 \\ LS (kPa) & 14.18 \(\pm\) 3.04 & 16.05 \(\pm\) 3.76 & 13.06 \(\pm\) 1.81 & 0.004 \\ SS (kPa) & 17.67 \(\pm\) 4.48 & 22.20 \(\pm\) 3.98 & 14.94 \(\pm\) 1.67 & \textless{}0.001 \\ GOV & & & & \\  & Present & 5 (10.4\%) & 5 (27.8\%) & - & 0.005 \\  & Absent & 43 (89.6\%) & 13 (72.2\%) & 30 (100.0\%) & \\ GPH & & 7 (14.6\%) & 7 (38.9\%) & - & \textless{}0.001 \\  & Absent & 41 (85.4\%) & 11 (61.1\%) & 30 (100.0\%) & \\ CHILD & & & & \\  & A & 26 (54.2\%) & 3 (16.7\%) & 23 (76.7\%) & \\  & B & 22 (45.8\%) & 15 (83.3\%) & 7 (23.3\%) & \textless{}0.001 \\ FIB-4 & & 1.02 \(\pm\) 0.51 & 1.03 \(\pm\) 0.50 & 1.37 \(\pm\) 0.82 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline characteristics of the studied sample and the univariate analysis of parameters related to the presence of HREVs. Figure 2: ROC curve analyses for the investigated parameters. The ROC curve for SS values is presented in Figure 3. SS seems to be a significant predictor for the presence of HRVs (SS AUROC 0.987; \(p<0.001\)), with an appropriate cutoff value of 17.95 kPa given the ratio between a sensitivity of 94.4% and specificity of 96.7%, calculated according to [PERSON]'s coefficient.
## 2 Results and Discussion
### Effect of Microwave Pretreatment on Volatile Compounds of Oil  
Flavor is the most important sensory quality of oil. Because of the strong nutty and roasty flavor, aromatic roasted oil is more popular with consumers than cold-pressed peanut oil. Pyrazines accounting for 50% relative percentage area were the highest contributors to the volatile profile of aromatic roasted peanut oil [35]. As can be seen from Table 3, most of the pyrazines showed a nutty and roasty flavor. Their contribution to the whole flavor of oil will be based on their odor threshold values. 2,5-dimethyl-pyrazine is highly correlated to a roasted peanut flavor and aroma [36]. A total of 101 volatile compounds in pretreated and untreated samples were identified by HS-SPME/GC-MS. The key pyrazine compounds in extracted oil from MW treated and untreated peanut by cold pressing re shown in Table 3. There were no pyrazines detected in the extracted oil from untreated and 1 min MW-treated peanut. With 2 min MW treatment on peanuts, 0.47 \(\pm\) 0.02% trimethyl-pyrazine was detected in the volatile profile of the extracted oil. The relative content and peak area of pyrazines in extracted oil significantly (\(p\) < 0.05) increased in the period of 3-5 min of MW treatment. This observation was consistent with the previous research results, which found pyrazines in extracted oil significantly increased after 6 min of MW treatment on rapeseed [21]. With 4, 5 min MW treatment on peanuts, the relative content and peak area of pyrazines in extracted oil reached the maximum (33.37 \(\pm\) 0.24% and 31.56 \(\pm\) 0.31 \(\times\) 10\({}^{7}\), respectively). The relative content and peak area of 2,5-dimethyl-pyrazine in extracted oil reached the maximum (5.56 \(\pm\) 0.12%, 4.34 \(\pm\) 0.06 \(\times\) 10\({}^{7}\), respectively) with 4, 5 min MW treatment on peanuts, respectively. Pyrazines are heterocyclic nitrogen-containing compounds derived from nonenzymatic protein-sugar interactions [37]. The formation of pyrazines in peanuts required at least 30 min roasting, and could reach a temperature of about 180 \({}^{\circ}\)C. Forty to 50 min roasting of peanuts leads to the formation of large amounts of pyrazine compounds [34]. With 5 min of MW treatment, the peanut temperature could reach 125-130 \({}^{\circ}\)C [38]. Compared with roasting, MW could promote the formation of pyrazines by a shorter period of thermal treatment under a relatively low temperature, which could be considered the most suitable way to improve the flavor of cold-pressed peanut oil. The MW pretreatment solved the bottleneck of cold-pressed peanut oil in industrial application and promotion. As a high-quality vegetable oil, flavored cold-pressed peanut oil provided a new choice for consumers.  
Figure 2: Effect of microwave pretreatment on the oxidative stability of oil. Different characters (a–e) on top of the line indicate significant (\(p\) < 0.05) differences among samples with different treatment times.
# A New Coding Paradigm for the Primitive Relay Channel +
###### Abstract  
We consider the primitive relay channel, where the source sends a message to the relay and to the destination, and the relay helps the communication by transmitting an additional message to the destination via a separate channel. Two well-known coding techniques have been introduced for this setting: decode-and-forward and compress-and-forward. In decode-and-forward, the relay completely decodes the message and sends some information to the destination; in compress-and-forward, the relay does not decode, and it sends a compressed version of the received signal to the destination using Wyner-Ziv coding. In this paper, we present a novel coding paradigm that provides an improved achievable rate for the primitive relay channel. The idea is to combine compress-and-forward and decode-and-forward via a chaining construction. We transmit over pairs of blocks: in the first block, we use compress-and-forward; and, in the second block, we use decode-and-forward. More specifically, in the first block, the relay does not decode, it compresses the received signal via Wyner-Ziv, and it sends only part of the compression to the destination. In the second block, the relay completely decodes the message, it sends some information to the destination, and it also sends the remaining part of the compression coming from the first block. By doing so, we are able to strictly outperform both compress-and-forward and decode-and-forward. Note that the proposed coding scheme can be implemented with polar codes. As such, it has the typical attractive properties of polar coding schemes, namely, quasi-linear encoding and decoding complexity, and error probability that decays at super-polynomial speed. As a running example, we take into account the special case of the erasure relay channel, and we provide a comparison between the rates achievable by our proposed scheme and the existing upper and lower bounds.  
primitive relay channel; compress-and-forward; decode-and-forward; chaining construction Article
# Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes
## 1 Introduction  
An earthquake is defined as a \"natural disaster\" in accordance with the Framework Act on Disaster and Safety Management [1], and it can negatively impact structures, communication, and traffic. The frequency of earthquakes in Korea has increased, and since 1999, an average of 69.9 occurrences of earthquakes have occurred per year [2]. Furthermore, the frequency of occurrences and the magnitude of earthquakes have increased significantly since 2015. Therefore, more attention needs to be directed to mitigate various impact from earthquakes.  
Recent earthquakes in Korea have been concentrated near Pohang City in Gyeongsangbuk-do. The following figure shows where earthquakes of magnitude 3.0 or higher occurred from 1 January 1990, to 30 March 2020 [2]. As shown in the figure, 15 earthquakes have occurred with greater number of earthquakes on land than at sea. Table 1 shows details of the earthquakes depicted in Figure 1.  
A bridge's structural failure can disrupt traffic flow and degrade network performance [1]. One of the biggest impacts during earthquakes is disconnection of bridge transport networks, which could hamper post-earthquake emergency responses, such as the movement of emergency vehicles. Hence, analysis of traffic network is needed to better understand the overall impact caused by structural damages to bridges from earthquakes.
## 3 SOA-Enabled HONoC Architecture  
Our proposed architecture, as shown in Figure 2, is a mesh-based HONoC with integrated SOAs and consists of two layers. The electrical layer controls the optical layer while transmitting control packets and small-size data through packet switching. Before the optical data transaction between the cores starts, the routing path should be setup in advance through the electrical layer. During the path setup process, the ON/OFF state of SOAs and MRs located in the path are determined and the SOA gain is controlled by adjusting the bias current applied to the SOA.  
Figure 2: Semiconductor optical amplifier (SOA)-enabled HONoC architecture.  
In the optical layer, data transaction is performed using circuit switching due to the absence of optical storage. The SOAs can amplify the optical signal power attenuated by photonic devices in the routing path. The SOAs outside of the routing path are in the OFF state and absorb the optical signal power. In this study, the expected effects of integrating the SOA with the mesh-based HONoC are as follows:  
* The SOA compensates for the insertion loss by amplifying the optical signal and reduces the burden of the laser source to reduce the total power consumption of the HONoC.
* An appropriate SOA gain can prevent the SNR degradation problem of the longest path due to the network size growth of the mesh-based HONoC.