esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The model described by Equation (2) assumes that a small crack may be difficult to measure, and as the crack becomes larger, the measurement of crack length becomes more accurate [17]. In order to capture any changes in the error distribution, it was assumed that the error follows a normal probability density function with zero mean and a standard deviation, \(s\) (Equation (3)). Based on a single experiment, [PERSON] [17] concluded that \(s\) was independent of crack growth rate. To capture the dependence of the crack length on \(s\), [PERSON] [17] assumed that \(s\) follows a two-parameter exponential distribution that changes as a function of AE count rates. However, these assumptions  
Figure 1: Characteristics of AE Signal. were not supported by the results of this research. The experimental results of this study suggested that \(s\) follows a normal distribution and is not a function of AE count rates; \(s\approx N(\mu_{s},\sigma_{s})\). \[\varepsilon\approx N(0,s),\;s\approx N(\mu_{s},\sigma_{s}) \tag{3}\]  
The significance of the proposed model represented by Equations (2) and (3) is that once the model parameters are estimated using experimental data, this equation can be used to estimate crack growth rates of structures by monitoring AE signals and extracting the AE count rate from the observed signals. [PERSON]'s model [17] was derived based on the results from one experiment with sinusoidal loading conditions of a maximum load of \(P_{max}\) = 300 lbf (1334 kN), loading ratio of \(R\) = 0.1, and loading frequency of \(f\) = 10 Hz. The results have not been validated with respect to different loading conditions and were limited to one experiment on Al7075-T6. Therefore, the first step in this research was to study the consistency and variability of the model by performing several standard fatigue experiments using compact tension test specimens of the same aluminum alloy (Al7075-T6) subjected to cyclic loading with varied loading ratios and frequencies. This part of the research addressed model development and validation, with respect to changes in loading frequency and loading ratio, through statistical analysis of the crack growth data. In the second step, the material effect was studied based on three identical crack growth tests with Ti-6 Al-4V titanium alloy (known as Ti-6-4). Experimental procedures and methods of analyzing the data were similar to those used for the Al7075-T6 samples. This paper first discusses the details of the performed fatigue experiments, followed by the description of the data analysis methods, and finally presents results including model development and validation. In the final section, conclusions are summarized and suggestions are made for future work.
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 4 Experiments
### OTFS Configuration  
The parameters of the used OTFS modulation are shown in Table 1. The parameters have been tailored to the envisaged application in human monitoring and activity detection in an indoor environment. The used setting gives us a potential for approximately 60 cm resolution in position and around 1 km/h resolution in speed. For other types of environments and potential objects to detect, the parameters would need to be changed accordingly. To limit the spectrum of the transmitted signal, one hundred zero subcarriers are inserted in the frequency domain prior to the Heisenberg transform (Equation (2)).
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece
### Proposed Non-Device-Centric Authentication Scheme
#### 3.3.2 Verification Phase  
1. The user identifier \(ID^{U}\) and the biometric samples \(S^{UV}\) are acquired by the client device \(U\).
2. Binary biometric features \(b^{UV}\in\mathbb{F}_{2}^{l}\), which preserve the distances, are extracted from \(S^{UV}\).
3. The substrings \(eb_{i}^{UV}\) with Hamming weight greater than \(t\) are obtained by applying the same transformation as in the enrollment phase.
4. The \(ENCODE\) algorithm is employed to encode the substrings \(eb_{i}^{UV}\) by using the authentication server public key \(T^{A}\), resulting the encoded substrings \(cb_{i}^{UV}\in\mathbb{F}_{2}^{n-k}\).
5. The client device \(U\) generates the nonce \(mU_{i}\in\mathbb{F}_{2}^{n-k}\).
6. The authentication server \(A\) generates the nonce \(mA_{i}\in\mathbb{F}_{2}^{n-k}\).
7. The encoded substrings are XORed with the nonces generated, obtaining \(cbn_{i}^{UV}\)\(\in\mathbb{F}_{2}^{n-k}\) as \(cb_{i}^{UV}\oplus mU_{i}\oplus mA_{i}\).
8. The client device \(U\) maps the \(ID^{U}\) to the index \(x^{U}\).
9. The index \(x^{U}\) and \(cbn_{i}^{UV}\) are sent to the computation server \(C\).
10. The computation server \(C\) retrieves the protected template \(cb_{i}^{UR}\) from the database server by using a PIR protocol with \(x^{U}\) as input, that is, \(PIR\left(x^{U}\right)\).
11. The computation server \(C\) applies a XOR operation to obtain \(cb_{i}^{IR}\oplus cbn_{i}^{UV}\).
12. This result is sent to the authentication server \(A\).
13. The authentication server performs the XOR operation to obtain \(cb_{i}^{UR}\oplus cbn_{i}^{UV}\oplus mU_{i}\oplus mA_{i}\) to obtain \(cb_{i}^{UR}\oplus cb_{i}^{UV}\).
14. \(eb_{i}^{UR}\oplus eb_{i}^{UV}\) is decoded by using the private key \(\Gamma^{A}\).
15. The final distance result is computed as in Equation (1).
16. The result is compared with the threshold value \(th\) to generate the recognition decision. Figure 5 illustrates the verification phase.  
Figure 4: Setup and enrollment phases of the distributed authentication protocol based on the Classic McEliece protection proposed.
## Nomenclature  
\begin{tabular}{l l} Symbol & Description \\ \(V_{cell}\) & Cell voltage \\ \(E_{memst}\) & Ideal electromotive force \\ \(V_{act}\) & Activation overpotential \\ \(V_{olm}\) & Ohmic overpotential \\ \(V_{con}\) & Concentration overpotential \\ \(\Delta G\) & Gibbs free energy change value \\ \(F\) & Faraday constant \\ \(\Delta S\) & Entropy change \\ \(T\) & Temperature of fuel cell \\ \(T_{ref}\) & Reference temperature \\ \(R\) & Gas constant \\ \(P_{H_{2}}\) & Hydrogen partial pressure \\ \(P_{O_{2}}\) & Oxygen partial pressure \\ \(\alpha\) & Exchange current constant \\ \(i_{stack}\) & Stack current density \\ \(i_{n}\) & Internal current density \\ \(i_{0}\) & Exchange current density \\ \(R_{memb}\) & Membrane resistance \\ \(B\) & Concentration voltage drop coefficient \\ \(i_{1}\) & Limited current density \\ \(V_{a}\) & Actual working voltage \\ \(V_{fc,int}\) & The initial voltage of the fuel cell \\ \(\delta\) & Degradation rate \\ \(\delta_{lp}\) & Degradation rate in low power \\ \(\delta_{np}\) & Degradation rate in normal power \\ \(\delta_{lc}\) & Degradation rate in load change \\ \(\delta_{ss}\) & Degradation rate in start/stop \\ \(n_{ss}\) & Count of start/stop \\ \(k_{lp}\) & Coefficient of low power \\ \(k_{np}\) & Coefficient of normal power \\ \(k_{lc}\) & Coefficient of load change \\ \(ECSA\) & Electrochemical surface area \\ \(i_{0}^{p_{t}}\) & The exchange current density per Pt surface \\ \(s_{0}\) & The ratio of ECSA to single-piece area \\ \(\tau_{lp}\) & Polarization voltage coefficient \\ \(R_{ohm}\) & Ohmic resistance \\ \(R_{ele}\) & Electronic impedance \\ \(R_{lon}\) & Ion impedance \\ \(R_{contact}\) & Contact impedance \\ \(\tau_{mp}\) & Ohmic voltage coefficient \\ \(\tau_{tp}\) & Ohmic and Concentration polarization coefficient \\ \(\tau_{lc}\) & Electrochemical coefficient of load transformation \\ \(P_{fc}\) & Fuel cell power \\ \(P_{fc,ref}\) & Reference fuel cell power \\ \(m_{beh}\) & Equivalent hydrogen consumption \\ \(m_{fc}\) & Fuel cell hydrogen consumption \\ \(\Delta\)SOC & SOC variation \\ \(Q_{bat}\) & Battery capacity \\ \(\eta\) & Battery charge efficiency \\ \(H_{L}\) & Low heating value of hydrogen gas \\ \end{tabular}
# Probabilistic Ship Detection and Classification Using Deep Learning
## 5 Experimental Results
### Performance
#### 5.2.2 Results of Detection Based on Video
Unlike the previous intervals, several misdetections and tens of false classifications occurred together in Interval 3. The proposed method worked well even for this challenging situation for the first 30 frames but the frequency of the misdetection and false classification exceeded a certain threshold. Moreover, our algorithm failed to classify effectively and returned the wrong result. Figure 8: The change in the confidences in the image sequence with environmental factors: (**a**) Faster R-CNN, (**b**) Faster R-CNN and IoU tracking, and (**c**) Faster R-CNN, IoU tracking and Bayesian fusion. Three competing methods are compared on a per-frame basis in Table 4. As stated, the ground truth was a destroyer. In Intervals 1 and 4, only Faster R-CNN returned a false classification for the aircraft carrier in several frames such as #19, #20 and #21 and its confidence remained around 0.5. When Faster R-CNN was combined with IoU tracking and Bayesian fusion, however, the confidence steadily increased and approached one. In Interval 2, only Faster R-CNN often missed the destroyer but when it was combined with IoU tracking and Bayesian fusion, Faster R-CNN overcame the misdetections and the confidence also approached one. Here, when the target was not detected or falsely classified for several consecutive frames, for example, during frames #124 to #127 in Interval 2 frame or frames #235 to #240 in Interval 4, the confidence for the destroyer dropped to around 0.9. However, the confidence quickly recovered from the loss when Faster R-CNN returned the correct result. Figures 9-11 show captured images from the frames #19 to  
Figure 10: Captured images from Interval 2. Figure 9: Captured images from Interval 1. The performance of ship detection with the proposed method on test videos was compared with using only Faster R-CNN for ship detection. The ship detection results are summarized in Figure 6:0 Overall, our proposed method outperformed the previous Faster R-CNN detector.
# A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter
## 2 UWB Positioning Method Based on Unscented Kalman Filter
### UWB Location Algorithm Based on the UKF
Step 3: Calculate the covariance matrix and the predicted state vector:  
\[ \begin{cases}\chi_{k|k-1}=f(\chi_{k-1})\\ \hat{X}_{k|k-1}=\sum_{i=0}^{2n}w_{i}^{\intercal}\chi_{k|k-1}^{i}\\ P_{k|k-1}=\sum_{i=0}^{2n}w_{i}^{\varepsilon}\Big{[}\chi_{k|k-1}^{i}-\hat{X}_{k |k-1}\Big{]}\end{cases}  \tag{16}\]  
Among these variables, \(w_{i}^{m}\) is the weight of the mean value and \(w_{i}^{\varepsilon}\) is the weight of the covariance. Step 4: With \(\hat{X}_{k|k-1}\) and \(P_{k-1}\) as the output values, calculate the sigma sample points according to Step 2 (\(\zeta_{k-1}\)), then calculate the measurement information predicted at time \(k\):  
\[\zeta_{k-1}=\left[\hat{X}_{k|k-1}\hat{X}_{k|k-1}+\gamma\sqrt{P_{k|k-1}}\hat{X}_{ k|k-1}-\gamma\sqrt{P_{k|k-1}}\right]^{T} \tag{17}\]  
Step 5: Measure and update to obtain the covariance matrix and the predicted state vector:  
\[\left\{ \begin{aligned} \zeta_{k|k-1}=h(\zeta_{k-1})\\ \hat{Z}_{k|k-1}=\sum_{i=0}^{2n}w_{i}^{m}\zeta_{k|k-1}^{i}\end{aligned} \right. \tag{18}\]  
\[P_{xz}=\sum_{i=0}^{2n}w_{i}^{c}\Big{[}\chi_{k|k-1}^{i}-\hat{X}_{k|k-1}\Big{]} \Big{[}\chi_{k|k-1}^{i}-\hat{X}_{k|k-1}\Big{]}^{T} \tag{19}\]  
\[P_{zz}=\sum_{i=0}^{2n}w_{i}^{c}\Big{[}\zeta_{k|k-1}^{i}-\hat{Z}_{k|k-1}\Big{]} \Big{[}\zeta_{k|k-1}^{i}-\hat{Z}_{k|k-1}\Big{]}^{T}+R_{k} \tag{20}\]  
\[\left\{ \begin{aligned} & K_{k}=P_{xz}P_{z}^{-1}\\ \hat{X}_{k}=\hat{X}_{k-1}+K_{k}\Big{[}\hat{Z}_{k}-\hat{Z}_{k|k-1} \Big{]}\\ & P_{k}=P_{k|k-1}-K_{k}P_{zz}K_{k}^{T}\end{aligned} \right. \tag{21}\]  
where \(K_{k}\) is the gain matrix, \(\hat{X}_{k}\) is the state vector at time \(k\), and \(P_{k}\) is the covariance matrix at time \(k\).
# Synchronization of Acoustic Signals for Steganographic Transmission
## 5 Results
### Hidden Transmission Effectiveness Assessment  
The aim of the study was to check whether the application of the developed mechanisms of synchronization of acoustic signals will allow for the implementation of steganographic transmission in a telecommunications channel in which there are signal degrading factors. The research was carried out using two different methods of steganographic signal transmission between the sender and the recipient. A teletransmission system based on radio waves and VoIP Internet telephony were used.
## 2 Materials and Methods
### Optimization of HS-SPME Parameters  
The optimization procedure involved the selection of those experimental parameters that were important for the SPME extraction efficiency, and the peak areas obtained via GC\(\times\)GC-TOFMS were used to evaluate the extraction efficiency [21]. To obtain the HS-SPME procedure with a maximum response area of the detected peak for extraction of compounds from Baijiu samples, the influence of sample dilution (0.5, 1, 2, 5, 10, and 15% vol), sample volume (1, 2, 4, 5, 6, and 8 mL), extraction time (15, 30, 45, 60, and 75 min), and extraction temperature (35, 40, 45, 50, 55, and 60 \({}^{\circ}\)C) were studied considering the high level of alcohol in the samples (Figure 1). Ultrapure water was used to dilute the Baijiu sample to make a solution with the desired ethanol concentration. The solution was saturated with NaCl, and different volumes of the diluted Baijiu sample were added to a 20 mL headspace glass vial. To create calibration curves and quantitation of volatile compounds in the Baijiu sample, 20 uL of the ISs mixture (final concentration: 2,2-dimethylpropanoic acid, 1197.55 ug/L; L-menthol, 700.19 ug/L; 2-octanol, 69.84 ug/L; \(\beta\)-phenethyl acetate-d\({}_{3}\), 20.12 ug/L; \(n\)-hexyl-d\({}_{13}\)-alcohol, 200.05 ug/L; and 2-methoxy-d\({}_{3}\)-phenol, 80.14 ug/L) was added during sample preparation. After that, the vial was sealed with a PTFE/silicone septum and a screw top.  
Figure 1: Schematic diagram of the determination of volatile compounds via HS-SPME-GC\(\times\)GC-TOFMS.  
All runs were carried out with a 2 cm divinylbenzene/carbon wide range/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 \(\upmu\)m fiber obtained from Supelco (Bellefonte, PA, USA). DVB/CAR/PDMS SPME fibers were previously demonstrated to be suitable for analysis of trace volatile and semi-volatile compounds in Baijiu and were consequently used during this study [22]. The HS-SPME procedure was performed using a MPS autosampler (Gerstel Inc., Mulheim, Ruhr, Germany) and ChromaTOF software (LECO Corp., version 4.61.1). Samples were incubated for 5 min at the extraction temperature under continuous agitation (400 rpm) for equilibration, and then the fiber was exposed to the headspace. The desorption in the GC\(\times\)GC injector was performed for 5 min at a temperature of 250 \({}^{\circ}\)C in the splitless mode. Each sample was analyzed in triplicate.
## 4 Discussion
### Water Tongue and Air Entrainment behind Aerators  
As shown in Figure 10, the contour of air volume fraction of the water tongue behind the aerator no. 1 was compared to the model test result ([PERSON] et al., 2017). It can be seen that the surface aeration region, forced aeration region, and the jet core with pure water were simulated successfully. The air entrainment and shape of water tongue were similar to that from the model test. In general, the Euler-Euler model based on the hybrid drag model was in accordance with the physical reality for the air-water two-phase flow in the spillway tunnel.  
As shown in Table 3, the air entrainment quantities of the forced aerators were calculated for comparison with the prototype data. Restricted to prototype data, only the average aeration concentration of the cross-section behind the aerators no. 2 and no. 4 were listed. The average aeration concentration of the cross-section behind the aerators is defined as follows:  
\[C_{average}=Q_{air}/(Q_{water}+Q_{air}), \tag{10}\]  
Figure 10: Water tongue and air entrainment behind aerator no. 1.  
Figure 9: Air velocity distribution in the cross-section of air ducts no. 1 to no. 3 under 100% gate opening ratio.  
where \(Q_{air}\) is the air flux in the aeration well, and \(Q_{water}\) is the water flow rate under the corresponding gate opening ratio. As can been seen, the maximum relative error was less than 30%, which means the aeration magnitude from numerical simulation was close to the prototype data. In addition, it is obvious that the quantities of air entrainment will not increase in an equal proportion with the increase of water flow rate.
## 2 Quantum Dots in Sensing Applications
### Carbon Quantum Dots  
Carbon quantum dots are generally carbon-based nanoparticles that exhibit optoelectronic properties due to the quantum confinement effect [34]. Carbon quantum dots (CQDs) have attracted much attention in many applications because of their advantages, such as water solubility, fluorescence, low toxicity, cheap scale-up production, abundance and ease of modification [35]. CQDs can be prepared from different carbon sources, such as food waste [36], waste biomass sources [37], corncobs [38] and weak organic acids [39]. Carbon dots are typically prepared via chemical methods and physical methods. Chemical methods include electrochemical synthesis, combustion/thermal/hydrothermal/acidic oxidation, supported synthesis, microwave/ultrasonic and solution chemistry methods. Physical methods include arc discharge, laser ablation/passivation and plasma treatment [40; 41]. These materials have been investigated for the fluorescence sensing of aromatic compounds due to their fluorescence properties. However, they have not been investigated for PAHs such as phenanthrene, naphthalene, pyrene, anthracene, etc. Recently, [PERSON] et al. synthesized CQDs for the fluorescence sensing of para-nitrophenol. The sensor and the phenol interacted through the IFE mechanism. This mechanism was proven via spectral overlap [42]. In 2015, [PERSON] et al. also prepared CQDs for the fluorescence sensing of 2,4,6-trinitrophenol. The quenching of the CQDs by the phenol was suspected to be due to the IFE [43]. [PERSON] et al. reported the fluorescence detection of 2,4,6-trinitrophenol (TNP) using manganese (Mn)-doped CDs. The probe showed fluorescence quenching with an increasing concentration of TNP and high selectivity in the midst of other nitroaromatic compounds [41].
## 1 Introduction
For similar reasons, a considerable number of researchers are tackling the challenge of estimating building heating and cooling needs [3, 5, 15, 16, 17, 18, 19]. A number of studies can be found in the literature that address the forecast of energy demand of non-domestic buildings (both thermal and electrical) using machine learning strategies. In [20], artificial neural networks (ANN) are compared to a physical principle-based model (_EnergyPlus_) when forecasting the amount of energy consumed by an administrative building in Sao Paulo, Brazil. In [16], a load forecasting method using wavelet transform, SVM and partial least squares regression is applied to an office building in Tianjin, China. In [21], a linear regression model on air temperature data is compared to decision trees, SVM and ANN as they attempt to forecast the total heating energy demand of a set of 52 residential buildings. In [22], the effect of considering a single artificial intelligence based method or combining multiple single artificial intelligence based prediction  
Figure 1: Comparing paradigms related to building energy performance simulation tools and machine learning adoption. models (ensemble approach) is also analyzed, trying to capture the benefits and avoid drawbacks associated with different strategies. The article is organized as follows: Section 2 describes the dataset used for this study and presents some background related to the used methods and the strategies adopted when applying them in this study. Additionally, the adopted error metrics and the list of features considered to predict heating and cooling energy demands are also introduced. In Section 3 the main results are presented, with a feature selection stage followed by a detailed comparison among the proposed methods, for both seasons. Section 4 is dedicated to the discussion, revealing the potential amongst the forecasting models tested and highlighting a comparative analysis of these selected models. Finally, in Section 5, the main conclusions are drawn and some topics for further research are proposed.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 2 State of the Art Research  
This section highlights the recent work undertaken in areas of pilot-based channel estimation and equalization in the domain of UWA.
## 2 Materials and Methods  
### Study Area  
The Yellow River basin (YRB) (32\({}^{\circ}\)-42\({}^{\circ}\)N and 96\({}^{\circ}\)-119\({}^{\circ}\)E, Figure 1), which is one of the most important watersheds across north China, was selected as the study area for its outstanding vegetation cover change [31; 32]. Its total watershed size is about 79,500 km\({}^{2}\), with a quite developed river system. The YRB encompasses a complex terrain that spans a large elevation range, e.g., the Qinghai-Tibet Plateau, the Loess Plateau, and the North China Plain. It contains eight sub-watersheds, i.e., the area above Longyang Gorge (ALG), Longyang Gorge to Lanzhou (LGL), Lanzhou to Hekou Town (LHT), Hekou Town to Longmen (HTL), Longmen to Sannen Gorge (LSG), Sannen Gorge to Huayuankou (SGH), the area below Huayuankou (BH), and an inflow zone (IZ). The YRB is a typical transitional zone between a continental climate in the western part and a monsoon climate in the eastern part. It is covered by numerous kinds of vegetation with the diverse climate, dominated by grass followed by crops and mixed forests.  
The YRB has experienced dramatic changes in the landscape because of intensive climate change and increasing human activities during the past decades [33]. Several con  
Figure 1: Spatial pattern of MCD12C1 land cover over the Yellow River basin (YRB) in 2001. Only natural plants (the colored areas) were included in this study. The sub-basins of the YRB include the area above Longyang Gorge (ALG), Longyang Gorge to Lanzhou (LGL), Lanzhou to Hekou Town (LHT), Hekou Town to Longmen (HTL), Longmen to Sannen Gorge (LSG), Sannen Gorge to Huayuankou (SGH), the area below Huayuankou (BH), and an inflow zone (IZ). Frequency histogram displaying the areal proportions (%) of corresponding land cover types is inset.  
servation programs, the Grain for Green (GFG) project and the Natural Forest Conservation (NFC) program, have been carried out to mitigate serious environmental and ecological issues [34; 35; 36; 37]. Land surface greening has been widely observed across the YRB since 2000 [38; 39]. It is reported that the vegetation coverage on the Loess Plateau, especially, has doubled from 31.6% in 1999 to 59.6% in 2013 [40]. Hence, the YRB is regarded as a natural laboratory for studying land cover change and its corresponding effects [31; 32].