esa-sceva/satcom-chunk-collection huggingface dataset:
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
###### Abstract  
In the context of robotics and game AI, grid-based Distance Maps are often used to fulfill collision checks by providing each traversable cell maximal clearance to its closest obstacle. A key challenge for DMs' application is how to improve the efficiency of updating the distance values when cell states are changed (i.e., changes caused by newly inserted or removed obstacles). To this end, this paper presents a novel algorithm to speed up the construction of DMs on planar, eight-connected grids. The novelty of our algorithm, Canonical Ordering Dynamic Brushfire (CODB), lies in two aspects: firstly, it only updates those cells which are affected by the changes; secondly, it employs the strategy of Canonical Ordering from the fast path planning community to guide the direction of the update; therefore, the construction requires much fewer cell visits and less computation costs compared to previous algorithms. Furthermore, we propose algorithms to compute DM-based subgoal graphs. Such a spatial representation can be used to provide high-level, collision-free roadmaps for agents with certain safety radius to engage fast and rational path planning tasks. We present our algorithm both intuitively and through pseudocode, compare it to competing algorithms in simulated scenarios, and demonstrate its usefulness for real-time path planning tasks.  
distance map; incremental algorithms; canonical ordering; path planning; subgoal graph +
Footnote †: journal: Applied Sciences
## 3 Results
### In Silico Analysis
#### 3.3.3 Overall 3D Model of \(D\). _salina_ LCYB  
Because no structure has been reported in the LCYB family, the crystal structure of geranylgeranyl reductase from a _Sulfolobus acidocaldarius_ strain, which displays a 27% sequence identity with \(D\). _salina_ LCYB, was used as a template, as described previously by [16].  
The overall structure of the enzyme could be divided into two distinct domains: a catalytic domain involving a FAD binding site, as well as a substrate-binding site and a transmembrane domain (Figure 5). The catalytic domain belongs to the Rossmann-fold family structure, consisting of a core height-stranded \(\beta\)-sheet flanked by four \(\alpha\)-helices. In our case, the catalytic domain also comprised of residues Gly128-Ser129-Gly130-Pro131-Ser132-Gly133, known as a G-x-G-x-x-G motif, for nucleotide-binding [54].  
Figure 4: Multiple sequence alignment of LCYB from \(D\). _salina_ in comparison to other LCYB. The secondary structure elements of the generated 3D model indicate DasilLBC: _Dunaliella salina_ LCYB (accession number: ACA34344.1), AAO64977.1: accession number of LCYB from _Haematococcus lactisris_, AAA81880.1: accession number of LCYB from _Anhidopsis thaliana_, WP_012166204.1: accession number of LCYB from _Acaryochloris marina_. Internal insertion corresponding to \(\alpha\)6 helix is shown in a green frame, and the conserved sequences, PTFLYAM and FLEETSL, are highlighted in a red frame. Strict \(\beta\)-turns are presented as TT letters, and strict \(\alpha\)-turns are shown as TTTI.  
The second domain consisted of only three large helices covering the total length of the enzyme, which are suspected to constituting the transmembrane part of the enzyme, as predicted by the TMHMM server.  
Calculations of the size and shape of binding sites performed using the CAST program (Figure 6) demonstrated the presence of a small cavity for FAD-binding (radius probe was set to 2), with a total volume of 139.5 A\({}^{2}\), and a larger cavity for the substrate binding (radius probe was set to 1.4), with a total volume of 480.14 A\({}^{2}\) and an interior length of about 35 A, able to accommodate a large and hydrophobic substrate like lycopene.  
Figure 5: Ribbon representation of the LCYB 3D model, showing the catalytic domain and the transmembrane domain.  
Figure 6: The volume (red) of the probable LCYB Flavin Adenine Dinucleotide (FAD) binding site (**a**) and substrate binding site (**b**), calculated using the CAST program.
## 4 Results
### Feature Extraction Results
Table 4 details the execution time information for the three features compared, in terms of extracting the relevant features and translating them into a 227 \(\times\) 227 image. Recording the execution time was achieved through a machine with Intel Core i7-9850H CPU @ 2.60 GHz processor, operated in single core. The reported execution times are in seconds and are an average of 100 different readings. As observed, scalograms also returned the shortest overall time duration across all three features compared. The numerous processes involved with the MFCC and Log-mel features justify the longer extraction time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Scalograms** & **MFCC** & **Log-Mel** \\ \hline Feature Extraction Execution Time & 0.1981 & 1.0076 & 1.0640 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average execution time for inference (in s). Figure 5: Confusion matrices for the top performing algorithm—CWTFT scalograms for: (a) Imbalanced dataset using the full synthetic database; (b) balanced dataset with 1565 files for training, and 260 files for testing. CWTFT coefficients are derived through taking the product between the DFT of the signal and the analyzing function through FFT, and inverting this in order to extract the wavelet coefficients. On the other hand, both MFCC and Log-Mel are based on the Mel-scale filter bank. This is based on the short-term analysis, from where vectors are computed per frame. Further, windowing is performed to remove discontinuities, prior to utilizing the DFT to generate the Mel filter bank. Further processes, such as the use of triangular filters and warping, are also necessary prior to the application of the IDFT and transformation. It is important to note that in terms of memory usage, there are negligible differences between the three features compared. This is because the features are being resized and translated into a 227 \(\times\) 227 image through bi-cubic interpolation, in order to fit the classifier. Nonetheless, each image translation occupies between 4-12 KB of memory, depending on the sound class.
# Earthquake Damage Index and Fragility Analysis of Steel Damper for Seismic Isolation Bridge
## 3 Steel Damper Damage Indexes
### Combined Assessment of Cumulative Inelastic Strain and Maximum Deformation  
This paper establishes the comprehensive damage index \(D\) of the steel damper, considering the damage assessment criteria of the cumulative inelastic strain effect and the maximum deformation effect simultaneously.  
\[D=\left(\frac{cis}{CIS_{u}}\right)^{\beta}+\left(\frac{\varepsilon}{\varepsilon_{u }}\right)^{\beta} \tag{7}\]  
where _cis_ represents the cumulative inelastic strain of the damper during the earthquake, _CIS\({}_{u}\)_ is the ultimate cumulative inelastic strain that the damper can withstand, \(\varepsilon\) represents the maximum strain of the damper during the earthquake, \(\varepsilon_{u}\) is the ultimate strain that the damper can withstand, and \(\beta\) is a coefficient, which is taken as 2 in this case [9]. Refer to the definition of steel damper's damage threshold values under the combined action of cumulative inelastic strain effect and maximum deformation effect in Tables 3 and 4. The comprehensive damage index's threshold values are calculated from Equation (7), as shown in Table 5.
## 5 Empirical Case Study: Courier Company in Bangkok
### Experimental Results and Analysis
#### 5.3.1 Vehicle Time Improvement  
We provide a comparison of vehicle time when routes were built using different time instances. Figures 5 and 6 give results of vehicle time of initial assignment according to the vehicle-zone map (Phase I: pre-adjustment), and the post-adjustment after the Phase II was applied on problems in Scenarios A and B, respectively. Note that values from different time instances of same vehicle are connected by the lines just for the sake of visualization. Problems with different time instance, however, are unrelated, and solved independently.  
Figure 4: Mobility between 9 a.m. 7 p.m. (data collected hourly).  
Figure 3: Distribution of pairwise distance of all customer locations. Left: Scenario A; Right: Scenario B.  
For Scenario A, regardless of the time instance used, we can see that initially three vehicles exceeded 480-min working time limit. After applying the route adjustment algorithm, all vehicles are within in the limit.  
As for Scenario B, we can see from Figure 6 that initially about half the number of vehicles exceeded 480 min of work time. After applying the route adjustment algorithm, no vehicle time violation presented except for 11 a.m. and 12 p.m. time instances where 1 and 3 vehicles violated the time limit, respectively. Let us remind that relatively slow average speeds were recorded at these two time instances (See Figure 4).
# CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis
## 2 Materials and Procedures
### Materials
#### 2.1.1 Mammography Datasets  
Various datasets are publicly accessible. They differ in terms of size, image format, image type, and resolution, etc., such as DDSM and DDSM's Curated Breast Imaging Subset (CBIS-DDSM) as show (Table 2).  
One of the most significant characteristics of a mammogram is the utilization of low-energy X-rays, to screen and diagnose the human breast. Two master views are introduced for acquiring the X-ray images: CC and MLO (Figure 2). Mammography mainly aims to detect BC early [35; 36], ordinarily by detecting abnormal regions or masses in the images of the X-ray. These masses are often highlighted by a physician or an expert radiologist.  
The paper applied mammographic images from databases. As a dataset, DDSM was first assembled and became available online in 2007 by South Florida University. It contains 2620 scanned film mammographic images of normal, benign, and malignant cases, all stored in Lossless Joint Photographic Experts Group format (IJPEG) with altered sizes and resolutions [37].  
DDSM is employed to conduct research in the systems of detecting and classifying BC. It shows real breast data with a resolution of 42 microns, 16 bits, and an average size of 3000 \(\times\) 4800 pixels. It [15; 36; 39] holds 2620 scanned film mammography studies distributed in 43 volumes. DDSM database holds 695 normal cases and 1925 abnormal cases (914 malignant/cancer cases and 870 benign cases, as well as 141 benign without  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Type** & **\# of Images** & **View** & **Format** & **Classes** & **Resolution** (Bit/Pixel)** & **Availability** \\ \hline \multirow{2}{*}{**DDSM**[37] & \begin{tabular}{c} Digital \\ Mammogram (DM) \\ \end{tabular} } & \multirow{2}{*}{10,480} & \multirow{2}{*}{MLO/CC} & \multirow{2}{*}{LJPEG} & \multirow{2}{*}{\
\begin{tabular}{c} Normal, benign \\ and malignant \\ \end{tabular} } & \multirow{2}{*}{8–16} & \multirow{2}{*}{
\begin{tabular}{c} Publicly \\ available \\ \end{tabular} } \\ \cline{1-1} \cline{6-6}  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mammography datasets for breast cancer [1; 37; 38].  
Figure 2: Mammography in CC and MLO views, respectively.  
callback), specifying the boundaries and locations of the abnormal cases. For every case, four images can be found to represent the left and right breasts in the MLO and CC views (Figure 2) [34]. An experienced radiologist can recognize malignant and benign masses in all mammograms. CBIS-DDSM Dataset: CBIS-DDSM is a developed and united edition of DDSM. Table 3 displays the distribution of data.
###### Abstract  
A Timepix3 detector with a 300 \(\upmu\)m silicon sensor has been studied as a novel radiation monitor for the mixed radiation field at the Large Hadron Collider at CERN. This work describes a test campaign carried out at Centro Nacional de Aceradores with quasi-mono energetic protons (alphas) from 0.6 (1) to 5 (5.6) MeV, where orthogonal irradiations are used to obtain an energy calibration, and a low-energy angular scan to estimate the front dead layer thickness of the sensor. The detector is operated in hole collection mode and at a partial bias of 250 \(\upmu\)m at 50 V, which increases the charge sharing among pixels to mitigate the signal saturation at high energy depositions. The data, supported by FLUKA Monte Carlo simulations of energy losses in the sensor, show that the Timepix3 monitor operates in a linear regime up to energy depositions of around 600 keV per pixel and 2 MeV per cluster. As a result, the detector has been found to be suitable for measuring charged particle fluxes in the LHC mixed radiation field within the linear calibration regime, with the partial exception of inelastic nuclear reaction hits (mostly from neutrons).  
Timepix3; radiation monitor; Radiation to Electronics (R2E); FLUKA; silicon pixel detector; saturation effect; proton and alpha calibration +
Footnote †: journal: Physics Letters B
# An Efficient Algorithm to Determine Probabilistic Bisimulation
## 4 A Partition-Refinement Algorithm for Probabilistic Bisimulation (Detailed)
### Data Structures
#### 4.2.1 Global  
In the detailed algorithm, there are arrays containing transitions, actions, blocks and constellations. There is a stack of non-trivial constellations to identify in constant time which constellation must be investigated in the main loop. Furthermore, there is an array containing the variables _state_to_constellation_cnt_, which are explained below.  
For all action transitions \(s\overset{a}{\rightarrow}u\), it is maintained how many action transitions there are labelled with the same action \(a\), and that go from \(s\) to the constellation \(C\) containing \(u\). This value is called _state_to_constellation_cnt_ for this transition. The value is required to efficiently split probabilistic blocks (the idea of using such variables stems from [5]). For each state \(s\), constellation \(C\), and action \(a\) there is one instance of _state_to_constellation_cnt_ stored in a global array. Each transition \(s\overset{a}{\rightarrow}u\) contains a reference called _state_to_constellation_cnt_ptr_ to the appropriate value in this array. See Figure 3 for a graphical illustration with a constellation \(C\) of probabilistic states and blocks \(B_{1}\) and \(B_{2}\) of action states. The purpose of this construction is that _state_to_constellation_cnt_ can be changed by one operation for all transitions from the same state with the same action to the same constellation, simultaneously.
## 3 Background and Theory
### GSVM Classifier (SVM with RBF Kernel)  
The initial supervised machine learning classifier used in this study is the Gaussian radial basis function (RBF) support vector machine (GSVM) [31], a variant of the traditional SVM. This approach incorporates the radial basis function, also known as the Gaussian kernel, within the support vector machine framework. It is especially well suited for handling non-linearly separable data, a common scenario in EEG signal processing. The Gaussian RBF kernel is defined in Equation (21):  
\[K(\mathbf{x},\mathbf{x}^{\prime})=\exp\left(-\gamma\big{|}\mathbf{x}-\mathbf{ x}^{\prime}|^{2}\right) \tag{21}\]  
where the following apply:  
* \(K\) is the RBF kernel function;
* \(\mathbf{x}\) are feature vectors in the input space;
* \(\mathbf{x}^{\prime}\) are other feature vectors in the input space;
* \(\gamma\) is a parameter that defines how much influence a single training example has.  
The decision function of SVM in this kernel-induced feature space is expressed as follows in (22):  
\[f(x)=\sum_{i=1}^{N}\alpha_{i}y_{i}K(x,x_{i})+b \tag{22}\]  
where the following apply:\(N\) is the number of support vectors;
* \(\alpha_{i}\) are Lagrange multipliers obtained by solving the SVM optimization problem;
* \(y_{i}\) are the class labels of the training examples;
* \(\hat{b}\) is the bias term.
## 1 Introduction
### Voice Familiarity: Improved Outcomes and Reduced Listening Effort  
As we explore the potential for new hearing aid features, there are several steps and questions that need to be considered. For example, (1) conceptualization of potential features, (2) testing behavioral/cognitive outcomes associated with and without the feature, (3) proof of concept of the feature in technological space, (4) development of hardware and software to support the feature, and (5) validation and verification in a real-world setting. While this is not an exhaustive list of the necessary steps, and by no means happens in a purely linear fashion, it does provide a general guideline from which we can test claims about potentially useful hearing aid advancements. With respect to voice familiarity as a hearing aid feature, steps 1 and 2 have been well established [25; 27; 28]. For example, the age-old story of an individual with hearing loss who claims they have no trouble hearing their spouse across the table in a restaurant, but they do have trouble understanding the friendly patron at the table next to them, is one genesis of the notion that voice familiarity is an important feature to decreased listening effort. In recent work, this claim has been tested in individuals with and without hearing loss. Indeed, increased voice familiarity is associated with improved performance on complex dual-tasks, speech-in-noise tasks [25], and auditory recall [26; 27]. For example, [PERSON] et al. [25] brought married couples into the lab to make recordings of signals to be used in the experiment with their partner. The researchers then played either their partner's voice as the target or another person's voice as the target in differing signal-to-noise ratios (SNRs). Predictably, performance improved as the SNR from the study increased, but, more importantly, performance when listening to their familiar partner was on average about 7 dB better than listening to an unfamiliar voice. Indeed, the cognitive and behavioral outcomes of a \"familiar voice\" compared to an \"unfamiliar voice\" are relatively stable in that there is enhanced performance [5; 12; 25; 26; 27; 28].
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 2 Lip-Reading Model Architecture  
Lip reading is one of the most challenging problems in artificial intelligence, which recognizes the speech content based on the motion characteristics of the speaker's lips. With the development of artificial intelligence [10; 11; 12], traditional lip-reading methods are being gradually substituted by deep-learning methods. Depending on the functional characteristics, the lip-reading method mainly consists of a frontend network and a backend network. The frontend networks include Xception [13], MobileNet [14; 15], ShuffleNet [16; 17], VGGNet, GoogLeNet, ResNet, and DenseNet [18], while the backend networks include Temporal Convolutional Network [19; 20], the Long Short-Term Memory (LSTM) [21], and the Gate Control Unit (GRU) [22]. The attention mechanism, aimed at better allocation of resources, processes more important information in a feature map. Our network is composed of ShuffleNet, CBAM and TCN, with the structure shown in Figure 1.  
The model consists of five parts:  
Input: using the Dlib library for detecting 68 landmarks of the face, we can crop the lip area and extract 29 consecutive frames from the video sequence. The frames go through a simple C3D network for generic feature extraction.  
CNN: ShuffleNet performs spatial downsampling of a single image.  
Figure 1: The architecture of the lip-reading model. Given a video sequence, we extract 29 consecutive frames that have a single channel indicating a gray level. The frames firstly go through a 3D convolution with kernel 5 \(\times\) 7 \(\times\) 7 called C3D. A ShuffleNet network inserted with the attention module called CBAM is used for spatial downsampling. Finally, the sequence of feature vectors is fed into the Temporal Convolutional for temporal downsampling followed by a SoftMax layer.  
CBAM: Convolutional Block Attention Mechanism includes two independent modules that focus on important information of the channel dimension and the spatial dimension.  
TCN: models the output of the frontend network and learns the long-term dependencies from several consequent images.  
Output: lastly, we pass the result of the backend to SoftMax for classifying the final word.
# Visible Light Communication: An Investigation of LED Non-Linearity Effects on VLC Utilising C-OFDM
## 3 Simulation Results and Discussion
### Power Penalties  
Analysing the optical power penalty for the signals under test, as depicted in Figure 13, provided additional insight for the C-OFDM and F-OFDM using 2-PAM for the 10\({}^{-3}\) BER target. As a result, the power penalty (in dB) increased exponentially with the increase in the DC bias points compared to the theoretical BER curve. Additionally, there was an \(\sim\)2.5 dB difference between the two modulation techniques when the LED's nonlinear effect was considered.  
Finally, Figure 14 illustrates the PAPR measured for the C-OFDM, F-OFDM and conventional OFDM. The PAPR was given as follows:  
\[PAPR=10\log 10\left[\frac{max|x_{m}|^{2}}{E\left[|x_{m}|^{2}\right]}\right] \tag{5}\]  
where \(E[.]\) denotes the average power of the data and \(x_{m}\) is the OFDM signal.  
Simulation results showed that the C-OFDM, FOFDM and OFDM systems indicated that C-OFDM achieved the lowest PAPR for values of N of 16 and 64. Reductions of \(\cong\)2 dB and 5 dB PAPR, respectively, were performed relative to F-OFDM and OFDM. That was because DBS reduced the likelihood of the constructive superposition of the input samples.  
Moreover, it was apparent that the PAPR of C-OFDM was the lowest compared to the F-OFDM and OFDM. The reason why the conventional OFDM had a higher PAPR for any value of \(N\) was due to the cosine and sine components. However, in C-OFDM and F-OFDM, only the cosine component contributed to the power signal. Moreover, concerning the C-transformer, more than 65% of its elements were zero, which reduced the probability of obtaining a high PAPR compared to the F-OFDM. Additionally, the simulation results above presented two significant improvements concerning C-OFDM over F-OFDM and the conventional OFDM. Firstly, C-OFDM demonstrated an enhanced performance under the same signal conditions. Secondly, it obtained a lower power penalty and PAPR. Due to  
Figure 13: The net power penalty gains for C-OFDM over F-OFDM at 200 mA.  
the limitations imposed by non-linearity transmission devices, a lower power penalty and PAPR were advantageous for communication systems that were achieved with C-OFDM.
## 3 Results and Discussion
### PCA Analysis of Volatile Components  
PCA is a method for data dimensionality reduction, which recombines the original variables into several new unrelated variables to extract and reflect critical information about the actual variables [38]. In this study, PCA was performed on the GC\(-\)MS results of _A. tsaoko_ and adulterant plant leaves and fruits in order to differentiate the differences between samples from different species. PC1 and PC2 accounted for 25% and 16.7% of the total variance, respectively (Figure 4A). In PC1, _A. tsaoko_ and adulterant plant samples showed clear differentiation along the PC1 axis. The samples from different species could be effectively distinguished, which was consistent with the results of the clustering analysis and enabled precise classification based on species. Since PCA is an unsupervised model validation method, it cannot eliminate intra-group noise. Therefore, PLS-DA was conducted to address this issue.