esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Analysis
### Search Failure Probability
#### 4.2.3 \(d\)-Left Hash Table  
The \(d\)-left hash table is assumed to have only one entry per bucket in this work. If collisions occur in all \(d\) buckets, an element cannot be stored. When searching an element that could not be inserted, search failure occurs due to false negative. Let \(P_{d}\) represent the probability that an element cannot be stored in a \(d\)-left hash table because of collisions in all \(d\) buckets. Then, \(P_{d}\) is formulated as follows.  
\[P_{d} = \frac{1}{n}\Big{(}0^{d}+(\frac{1}{B_{d}})^{d}+(\frac{2}{B_{d}})^{ d}+\cdots+(\frac{n-1}{B_{d}})^{d}\Big{)} \tag{24}\] \[= \frac{1}{nB_{d}{}^{d}}\sum_{j=1}^{n-1}j^{d}\]  
Let \(P(F_{d})\) represent the _false negative_ probability of a \(d\)-left hash table. For \(3n\) inputs, since \(P(F_{d}|S^{c})=0\), \(P(F_{d})\) is as follows.  
\[P(F_{d}) = P(S)P(F_{d}|S)+P(S^{c})P(F_{d}|S^{c}) \tag{25}\] \[= P(S)P(F_{d}|S)=\frac{1}{3}P_{d}=\frac{1}{3 nB_{d}{}^{d}}\sum_{j=1 }^{n-1}j^{d}\]  
Labels (24) and (25) hold true when \(\alpha\leq 1\). In case of \(\alpha>1\), the number of elements is more than the number of entries in a hash table (i.e., \(n>B_{d}\)). Assuming that no empty entry exists in a hash table when the number of programmed elements is \(B_{d}\), the \(i^{th}\) element cannot be inserted in the table for \(B_{d}<i\leq n\). In other words, in calculating \(P_{d}\) for \(\alpha>1\), the probability that the \(i^{th}\) element cannot be inserted is 1, which means collision unavoidably occurs. Thus, for \(\alpha>1\), \(P_{d}\) is formulated as follows.  
\[P_{d} = \frac{1}{n}\Big{(}0^{d}+(\frac{1}{B_{d}})^{d}+(\frac{2}{B_{d}})^{ d}+\cdots+(\frac{B_{d}-1}{B_{d}})^{d}+(n-B_{d})\Big{)} \tag{26}\] \[= \frac{1}{n}\Big{(}\frac{1}{B_{d}}\sum\limits_{j=1}^{B_{d}-1}j^{d }+(n-B_{d})\Big{)}\]  
Therefore, \(P(F_{d})\) for \(\alpha>1\) is as follows.  
\[P(F_{d}) = P(S)P(F_{d}|S)=\frac{1}{3}P_{d} \tag{27}\] \[= \frac{1}{3n}\Big{(}\frac{1}{B_{d}}\sum\limits_{j=1}^{B_{d}-1}j^{ d}+(n-B_{d})\Big{)}\]
## 3 Methodology of TSDCA-BA Model
### Perceptual Metric-Based Model Evaluation and Selection-PESQ  
In the process of selecting an appropriate loss function, it was found that although using logarithmic mean squared error (log-MSE) as the training objective yielded continuously reduced loss values, this metric does not fully correspond to the subjective perception of speech quality by human listeners. Notably, log-MSE struggles to capture improvements in speech enhancement that are perceptible at the auditory level. Although the PESQ metric is widely recognized as an objective measure of speech quality, its non-differentiable characteristic and high computational cost render it unsuitable for direct use as a training loss. To overcome this limitation, following approaches similar to those treating PESQ as a performance evaluation tool [30], we integrated the PESQ calculation into a callback function. This function evaluates the model on the validation set at the end of each epoch, computing the average PESQ score as an important indicator of the model's perceptual quality. Model checkpointing was then guided by selecting weights that correspond to the peak PESQ score observed on the validation set. Comparative experiments indicate that this PESQ-based model selection strategy yields better speech enhancement results compared to the conventional early stopping approach.
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
## 4 Measuring the LT Precession via Pulsar Timing
### Lense-Thirring Precession in \(\dot{\omega}\)
#### 4.2.3 PSR J1946\(+\)2052, the Most Compact Double-Neutron Star System  
A DNS system similar to the Double Pulsar is PSR J1946\(+\)2052, which was discovered in the PALFA survey with Arecibo [80]. PSR J1946\(+\)2052 is a 17 ms pulsar in a slightly eccentric (\(e=0.06\)) 1.88 h orbit, the shortest orbital period among all known DNS systems. These features suggest that PSR J1946\(+\)2052 underwent an evolutionary process similar to that of the Double Pulsar, and it resembles the Double-Pulsar system after about 40 Myr of further evolution due to GW damping, i.e., this system will merge in approximately 46 Myr. The faster spin period and smaller orbital period amount to a larger LT contribution \(\dot{\omega}^{\rm LT}\), making this system a promising candidate for measuring the MoI. The recent profile analysis based on FAST data revealed a very small misalignment between the spin vector of this pulsar and the orbital angular momentum vector (\(\delta^{\rm SO}\sim 0.2^{\circ}\)) [35]. Therefore, the LT contribution \(\dot{\omega}^{\rm LT}\) can be calculated the same way as for the Double Pulsar (see Section 4.2.2) and is shown in Table 1. The more compact orbit also means a more significant decay of the orbital period due to the emission of GWs, as well as smaller kinematic effects (which scale with \(P_{\rm b}\)), allowing for increased measurement capability. These all suggest that PSR J1946\(+\)2052 is a very promising system for measuring the MoI, especially with the high-precision data from FAST.  
However, the most challenging part for measuring the MoI of PSR J1946\(+\)2052 is that this system is very far away, making it difficult to correct for the kinematic effects in \(\dot{P}_{\rm b}\). Given that the dispersion measure (DM) of PSR J1946\(+\)2052 is \(93.965(3)\) pc cm\({}^{-3}\), Ref. [80] predicted that the distance of the pulsar to be \(d=4.2\) kpc and \(3.5\) kpc based on the electron-density models NE2001 [102] and YMW16 [103], respectively. A precise distance measurement using VLBI is unlikely for a faint pulsar at such a distance. Nonetheless, by assuming a proper motion, one could estimate the kinematic contributions over a range of distances, as shown in Figure 9. The green curves indicate the sum of the kinematic effects \(\dot{P}_{\rm b}^{\rm D}\), which depend significantly on the proper motion of the pulsar (e.g., with 3 mas yr\({}^{-1}\) and 6 mas yr\({}^{-1}\)), but the difference of \(\dot{P}_{\rm b}^{\rm D}\) at \(d=3.5\) and 4.2 kpc changes by only \(-0.39\) and \(0.23\) fs s\({}^{-1}\), respectively. With an uncertainty at this level (i.e., only from the uncertainty of the distance), the GR's prediction for orbital period decay can still be tested with a better precision than the Hulse-Taylor pulsar [80]. Therefore, the knowledge of proper motion is essential for the \(\dot{P}_{\rm b}\) test and MoI measurement, which is expected to come out from future timing.
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 5 Optical Methods
### Other Camera-Based Automotive Monitoring
#### 5.4.2 Automotive Monitoring Using Near Infrared Light (NIR)  
Near infrared camera investigations aiming at driver drowsiness detection during darkness date back at least 20 years. As early as 1994, [PERSON] and colleagues from the Nissan Research Center reported on their experiences in face analysis and eye tracking using a CCD camera and NIR illumination [10].  
For truck driving, [PERSON] et al. [134] reported on using the well-known PERCLOS (percentage eye closure) index, a measure of drowsiness associated with slow eye closure, in 1998. Covering the state-of-the-art a decade ago, a survey on drowsiness detection and driver state monitoring using NIR cameras was published by [PERSON] et al. in 2006 [135]. Interestingly, these authors state that the best techniques for fatigue measurement are _\"based on physiological phenomena like brain waves, heart rate, pulse rate and respiration. These techniques are intrusive, since they need to attach some electrodes on the drivers, causing annoyance to them\"_[135]. Hence, at the time, NIR-based computer vision was proposed as an (inferior?) alternative instead. That way, using a CCD camera with NIR illumination as a physiological sensor fuses the advantages of both approach.  
For many years, the group of [PERSON] at EPFL in Lausanne, Switzerland has worked on signal processing on facial images in the car. A special focus has been the quantification of emotion, see, e.g., [[https://transport.epfl.ch/en/research-overview/vehicles-infrastructures/intelligent-vehicles/face_analysis/emotion-detector/](https://transport.epfl.ch/en/research-overview/vehicles-infrastructures/intelligent-vehicles/face_analysis/emotion-detector/)]([https://transport.epfl.ch/en/research-overview/vehicles-infrastructures/intelligent-vehicles/face_analysis/emotion-detector/](https://transport.epfl.ch/en/research-overview/vehicles-infrastructures/intelligent-vehicles/face_analysis/emotion-detector/)). Note that, in 2014, [PERSON] et al. published on using near-infrared (NIR) cameras on the dashboard of a car for that purpose [136]. Based on the tracking of 49 face landmarks, the authors report a correct classification rate of up to 90% in their experimental setting.  
In 2016, [PERSON] and coworkers [115] presented their results on the fatigue analysis of eyes, mouth, and head-pose using a NIR camera. For face analysis, the Viola-Jones algorithm [132] was employed. In addition, heart rate and heart rate variability were extracted using no-contact PPG imaging. Hence, their proposed system features two major contributions, namely _\"(1) integrating various facial feature methods using an NIR camera, and (2) measuring the driver's physiological state with a non-contactable method\"_[115].
## 4 Numerical Simulation
### Numerical Simulation Process and Working Conditions  
To simulate the site's seepage conditions accurately, the model's initial ground stress under self-weight is first calculated. Stability is assessed by the maximum unbalance force to determine if the iterative calculation has reached equilibrium. An isotropic seepage model is used, setting varying water surface heights for tunnel excavation and support. The numerical simulation is then verified against on-site monitored water pressure distribution around the tunnel. Subsequently, the force and deformation behavior of the tunnel lining during operation are analyzed.  
The engineering geological investigation report for the Gudou Mountain Tunnel indicates that the average groundwater level in the section from K51 + 590 to K51 + 640 is 126 m. Numerical simulations were set up with water surface heights at 126 m, 127 m, 128 m, and 129 m, respectively. Using this calculation scheme, fluid-solid coupling simulations were conducted for the tunnel at various groundwater levels.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 4 Discussion  
### Algorithm Performance for Valence and Arousal  
Classifier screening shows that classifiers using wavelet-extracted features outperform the ones employing features extracted with traditional time and frequency metrics, both for valence and arousal classification. The increased performance can be explained by the wavelet scattering method's ability to decompose and extract features from different scales of the signal, providing time and frequency resolution, and allowing a classifier to capture the difference between classes [52]. Regarding one-dimension classification, we found slightly better performance for arousal than for valence when considering the same type of features and classifier, similar to other works [6; 53]. For both arousal and valence classification, ensemble and 10-KNN classifiers presented better accuracy than the other classifiers. The good performance of the ensemble classifier might be due to its characteristics, since the selected ensemble bagged tree classifier is a boosted aggregation algorithm that creates a collection of decision trees, which are combined in order to reduce the variance of classification result. On the other side, KNN makes clusters of data from the feature vector, which may be close enough for similar classes. Ensemble classifier has previously been reported to have high accuracy for ECG arrhythmia classification [54].
## 3 The Proposed Algorithm
### Hybrid Spatial Filter  
In our algorithm, we designed a hybrid spatial filter to improve the accuracy and robustness of image segmentation. The proposed hybrid spatial filter integrates four filters, including Gaussian filter, median filter, mean filter, and mean-median filter. These four filters have been proved by time and experiments, can remove the noise in the image very well, and retain the edge information in the image. It also can make full use of the spatial information of the original image while eliminating noise [30,41]. The common Gaussian filtering, median filter and mean filter are not repeated here. The mean-median filter is defined as follows:  
\[\textit{mean-median}(x,y)=\frac{\sum\limits_{i=1}^{a*b}\textit{average}_{(u,v )\in Sxy}\{g_{i}(u,v),\ \textit{median}\ \}}{a*b} \tag{7}\]  
where \(Sxy\) represents the set of pixels in the rectangular sliding window with size a*b, \"median\" represents the median gray value of pixels in the window centered on the point \((x,y)\), and \(g_{i}(u,v)\) represents the gray value of the \(i\)-th pixel \((u,v)\) in the window.  
We sum up and find the average for the above four filters to form our proposed hybrid spatial filter. Hybrid spatial filter is defined as follows:  
\[\mathrm{H}(x,y)= \varepsilon G(x,y)+\phi\textit{median}(x,y)+\gamma\textit{mean}( x,y)+\gamma\textit{mean-median}(x,y) \tag{8}\]  
where \(\mathrm{H}(x,\,y)\) represents the hybrid spatial filter; \(G(x,\,y)\) denotes the Gaussian filter; \(\textit{median}(x,y)\) represents the median filter; \(\textit{mean}(x,y)\) represents the mean filter; _mean-median_(\(x,\,y\)) represents the mean-median filter; \(\varepsilon\), \(\phi\), \(\gamma\) and \(\eta\) are four weights to balance \(G(x,\,y)\), \(\textit{median}(x,\,y)\), \(\textit{mean}(x,\,y)\), \(\textit{mean-median}(x,y)\); and \(\varepsilon+\phi+\gamma+\eta=1\). Here, we set \(\varepsilon=\phi=\gamma=\eta=1/4\) empirically.  
Figure 6 shows the results of two test images processed by the proposed hybrid spatial filter. It can be seen that the images after hybrid spatial filtering are smoother. The noise has been removed, and the details and structural information in original images are well preserved.  
Figure 5: Framework of the proposed algorithm.
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 5 DT Solutions and Future Directions
### Cost-Effective and Reliability  
The implementation of EV networks is one of the most relevant challenges due to the shortage of infrastructure and safety measurements. Providing infrastructure services to implement EV networks is very cost-effective. The maintenance of EV services is also cost-effective. The DT model of the EV can be evaluated before deployment, thus lowering maintenance costs and making DT a cost-effective option. The present state of the EV system does not have reliability in data transmission. Reliability is the main challenge in EV transportation systems to operate EVs safely under various conditions. In the future, EV transportation systems should maintain data reliability and scalability.
# A Survey of Low-Rank Updates of Preconditioners for Sequences of Symmetric Linear Systems
## 7 Numerical Results
### Fe Discretization of a Parabolic Equation
In Figure 4, left, we plot the number of iterations needed by the \"no update\" and tuned preconditioners for every system in the sequence compared also with the scaled \(\sqrt{\Delta t_{k}}\approx\sigma_{k}^{-1/2}\) values (blue lines) as indicators of the increasing condition number of the coefficient matrices. On the right plot the behavior of the tuned vs spectral preconditioners is displayed, showing the substantial equivalence between the two approaches. Note finally that the PCG solver with the tuned preconditioner is almost insensitive to the increasing condition number of the matrices, displaying a number of iterations roughly constant throughout the timesteps. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**IC (\(A_{1},1e-5\))**} & \multicolumn{3}{c}{**IC (\(A_{2},1e-4\))**} \\ \hline update & \# its & CPU & CPU per it. & \# its & CPU & CPU per it. \\ no update & 8231 & 805.8 & 0.098 & 16582 & 1177.2 & 0.071 \\ spectral & 5213 & 557.3 & 0.107 & 10225 & 817.5 & 0.080 \\ SR1 tuned & 5161 & 551.7 & 0.107 & 10152 & 811.4 & 0.080 \\ BFGS tuned & 5178 & 612.3 & 0.118 & 10198 & 924.6 & 0.091 \\ deflated & \(\dagger\) & \(\dagger\) & \(\dagger\) & \(\dagger\) & \(\dagger\) & \(\dagger\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of iterations and total CPU time for solving the sequence (12) of shifted linear system. \(\dagger=\) for some of the system in the sequence convergence not achieved.
# Cover Time in Edge-Uniform Stochastically-Evolving Graphs +
## Preliminaries  
Let us hereby define a few standard notions related to a simple random walk performed by a single agent on a simple connected graph \(G=(V,E)\). By \(d(v)\), we denote the degree, i.e., the number of neighbours, of a node \(v\in V\). A simple random walk is a Markov chain where, for \(v,u\in V\), we set \(p_{vu}=1/d(v)\), if \((v,u)\in E\), and \(p_{vu}=0\), otherwise. That is, an agent performing the walk chooses the next node to visit uniformly at random amongst the set of neighbours of its current node. Given two nodes \(v\), \(u\), the expected time for a random walk starting from \(v\) to arrive at \(u\) is called the hitting time from \(v\) to \(u\) and is denoted by \(H_{vu}\). The cover time of a random walk is the expected time until the agent has visited each node of the graph at least once. Let \(P\) stand for the stochastic matrix describing the transition probabilities for a random walk (or, in general, a discrete-time Markov chain), where \(p_{ij}\) denotes the probability of transition from node \(i\) to node \(j\), \(p_{ij}\geq 0\) for all \(i,j\) and \(\sum_{j}p_{ij}=1\) for all \(i\). Then, the matrix \(P^{t}\) consists of the transition probabilities to move from one node to another after \(t\) time steps, and we denote the corresponding entries as \(p_{ij}^{(t)}\). Asymptotically, \(\lim_{t\to\infty}P^{t}\) is referred to as the limiting distribution of \(P\). A stationary distribution for \(P\) is a row vector \(\pi\) such that \(\pi P=\pi\) and \(\sum_{i}\pi_{i}=1\). That is, \(\pi\) is not altered after an application of \(P\). If every state can be reached from another in a finite number of steps, i.e., \(P\) is irreducible and the transition probabilities do not exhibit periodic behaviour with respect to time, i.e., \(gcd\{t:p_{ij}^{(t)}>0\}=1\), then the stationary distribution is unique, and it matches the limiting distribution (fundamental theorem of Markov chains). The mixing time is the expected number of time steps until a Markov chain approaches its stationary distribution.  
In order to derive lower bounds for RWA, we use the following graph family, commonly known as lollipop graphs, capturing the maximum cover time for a simple random walk, e.g., see [21; 22].  
Definition 1: A lollipop graph \(L_{n}^{k}\) consists of a clique on \(k\) nodes and a path on \(n-k\) nodes connected with a cut-edge, i.e., an edge whose deletion makes the graph disconnected.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 3 Preliminaries and Background
### Bloom Filters  
Bloom filters are efficient probabilistic data structures used to represent sets and support membership queries. In privacy-preserving record linkage, they are commonly employed to encode quasi-identifiers (QIDs; such as names or addresses). The specific approach involves decomposing a QID value into q-grams, mapping them to a bit array of length \(m\) using \(k\) hash functions, and setting the corresponding \(k\) bits to 1. This encoding supports approximate matching while providing a degree of privacy protection through the one-way nature of hashing and collision properties.
## 2 Materials and Test Methods
### Specim-Preparation Methods  
The specimens were prepared using the Vertical Vibration Compaction Method (VVCM), employing Vertical Vibration Test Equipment (VVTE) equipped with two symmetrical eccentric blocks to replicate the action of a typical field vibratory roller, as illustrated in Figure 1a. This setup ensures the stability of the VVTE by counterbalancing the horizontal components of centrifugal forces generated by the eccentric blocks, while allowing the vertical components to add up. Consequently, the VVTE experiences only a single vertical force. The operational parameters of the VVTE can be found in Table 4.  
The test specimens are prepared by drying the required aggregates at (105 \(\pm\) 5) \({}^{\circ}\)C to a constant weight. Coarse aggregates above 19 mm are mixed with asphalt initially, followed by the addition of the remaining aggregates and thorough mixing. Mineral powder is then added to ensure complete mixing within a total duration of 180 s. The mixture is loaded into the mold upon specimen formation, and the VVTE vibration is applied for 90 s.  
The LSAM-50 asphalt mixture has a nominal maximum aggregate size of 53 mm, and it appears that the use of large Marshall specimens (\(\Phi\)152.4 mm \(\times\) h93.5 mm) may no longer be suitable. Based on experimental findings [27], utilizing larger specimens (\(\Phi\)200 mm \(\times\) h160 mm) effectively eliminates the size effect. Therefore, the LSAM-50 asphalt mixture specimens are now standardized to the size of 200 mm \(\times\) 160 mm.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{8}{c}{**Aggregate Size (mm)**} \\ \cline{2-9}  & **37.5–53** & **19–37.5** & **9.5–19** & **4.75–9.5** & **2.36–4.75** & **0–2.36** & **Mineral** \\ \hline Apparent relative & & & & & & & & \\ density(g/cm\({}^{3}\)) & 2.819 & 2.779 & 2.755 & 2.721 & 2.728 & 2.728 & 2.706 \\ Flaky particle content (\%) & 2.2 & 7.9 & 7.3 & 13.1 & / & / & / \\ Water absorption rate (\%) & 0.37 & 0.62 & 0.98 & 1.73 & 0.75 & / & / \\ Crushed value (\%) & & & 18.5 & & & / & / \\ Sturdeness (\%) & & & 3.7 & & & 7.3 & / \\ Methylene blue value (g/kg) & / & / & / & / & / & 3.4 & / \\ Plasticity index (\%) & / & / & / & / & / & / & 3.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical indicators of aggregate.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Gradation**} & \multicolumn{8}{c}{**Percentage by Mass Passing through the Sieve Sizes (mm)**} \\ \cline{2-10}  & **53** & **37.5** & **19** & **9.5** & **4.75** & **2.36** & **1.18** & **0.6** & **0.3** & **0.15** & **0.075** \\ \hline A & 100.0 & 58.0 & 46.0 & 40.0 & 35.0 & 29.2 & 21.7 & 16.3 & 12.0 & 8.2 & 3.8 \\ B & 100.0 & 70.0 & 60.0 & 42.0 & 34.0 & 26.0 & 18.0 & 14.0 & 10.0 & 7.5 & 4.5 \\ C & 100.0