esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 3 Methodology
### Regime Identification Based on Probabilistic Modeling  
Traffic behaviors under various traffic conditions can be treated as a stochastic process [34]. In view of this, probabilistic modeling is conducted in this study to properly identify the associated regimes of traffic flow. The hidden Markov model (HHM) is one of the most powerful algorithms in probabilistic modeling [35; 36], and is thus used here. An HMM describes two stochastic processes based on the concepts of the Markov Process and Markov Chain. The first stochastic process is hidden, while the second is observable. The hidden stochastic process is established to infer the observations of the observable stochastic process. In the regime identification task, the regimes are described as the hidden states in HMM, and the hidden process depicts how the regimes are transformed into each other. Meanwhile, the values of the traffic flow measures are described as the observations in HMM, and the observable process describes how the collected observations of the measures evolve over time.
## 2 Research Framework
### PLM Functional Model  
Relying on established conceptualizations of PLM and the current status of its technological and conceptual evolution, a PLM theoretical framework (Figure 2) is defined that provides a comprehensive overview of PLM functional domains, which are in the context of this research regarded as partial aspects of holistic PLM or as specific areas for developing PLM competencies.  
Figure 2: PLM functional model.  
The model emerged as a result of the systematization of business domains that need to be integrated and effectively coordinated through PLM to ensure a cohesive and comprehensive approach to managing the product lifecycle, including the following: (1) product information management, (2) collaborative product engineering, (3) process planning, (4) customer needs management, (5) supply chain processes support, including support at the managerial level.  
The model structures the total functional scope of PLM into four main areas.  
* Product information management: access and use product definition information and maintain its integrity throughout the product lifecycle;
* Functionally focused capabilities: designed to address the specific needs of product information management at different stages of the product lifecycle;
* Cross-cutting elements that permeate various product lifecycle processes;
* Functional domain of collaboration in the interorganizational supply chain.  
It should be noted that PLM processes are not subject to any particular standard; therefore, PLM configuration must be adapted to the company's specific needs and business circumstances, taking into account various situational variables. In that context, the model intends to point to the comprehensive functional domain of PLM, thereby projecting its idealistic vision; simultaneously, it provides a reference frame for defining the PLM strategy scope, pointing to the need to adapt the reference model for its application in different contexts, including dedicated functionalities aligned to company needs instead of standard ones.
## 4 Equivalent Small-Signal Admittance Model in Sequence Domain  
In the previous section, the small-signal admittance model of the converter in the dq domain has been obtained. However, the admittance model can also be obtained in the positive-negative sequence domain [18; 20]. The small-signal admittance matrix in the sequence domain can also be written by a \(2\times 2\) admittance matrix, where its off-diagonal elements show a coupling between the positive and negative components. This coupling is very small and can be neglected [7]. The relationship between the sequence domain admittance definition and dq domain admittance definition [20] can be obtained by the following equation as derived in Appendix A.  
\[\left[\begin{array}{cc}y_{pp}(s)&y_{pn}(s)\\ y_{np}(s)&y_{nn}(s)\end{array}\right]=\left[\begin{array}{cc}1&1\\ -j&j\end{array}\right]^{-1}\left[\begin{array}{cc}y_{dd}(s)&y_{dq}(s)\\ y_{qd}(s)&y_{qq}(s)\end{array}\right]\left[\begin{array}{cc}1&1\\ -j&j\end{array}\right] \tag{8}\]  
The detailed form of Equation (8) is obtained by substituting Equation (7). In this paper, the dq domain admittance is obtained, and then the equivalent sequence admittance is derived based on (8). For the linear stability analysis of the wind power plant in the sequence domain, the coupling terms may be neglected, and the conventional positive impedance is calculated as:  
\[Z_{p}(s)=\frac{1}{y_{pp}(s-j\omega_{1})} \tag{9}\]
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 1 Introduction
Having in mind the above-mentioned state-of-the-art description, the most important contributions of this paper can be described as follows:  
* We present the first-ever application of OTFS-based joint communication and sensing approach for human monitoring. We target the millimeter-wave license-exempt frequency band, and present our unique measurement test-bed for 60 GHz OTFS transmissions;
* We present persistent homology as an innovative approach for the detection of targets from two-dimensional delay-Doppler data;
* We demonstrate OTFS-based sensing with the realistic assumption of the targets representing non-integer multiples of designed delay and Doppler resolution of the OTFS system, and we compare the performance of the persistent homology detector with a conventional detector based on the constant false alarm rate. The rest of the paper is organized as follows. In Section 2, the principle of OTFS is briefly explained, together with embedded pilots for channel estimation as well as delay and Doppler resolution. Section 3 deals with CFAR target detection and presents the principle of the persistent homology approach. In the following Section 4, the used 60 GHz measurement test-bed is described, with the results presented in Section 5. The final Section 6 rounds up the paper and presents some ideas for further work.
# A Review of Experimental and Numerical Studies of Lithium Ion Battery Fires
## 4 Thermal Runaway
### Mitigating the Risk of Thermal Runaway and Fire
#### 4.3.2 Safety Devices  
Safety devices built into the structure of the battery to mitigate the risk associated with either overcurrent, overtemperature, or pressure are known as current interrupt devices (CID) and positive temperature coefficient (PTC) devices.  
The safety or pressure vent is crucial in protecting users from the risk of increased internal pressure of a LIB. The safety vent, composed of a gasket containing a puncture film and a spike, allows the venting of built-up pressure, which inhibits the dangerous rupturing of the battery. The severity or impact of TR is, in part, related to the build-up and subsequent release of pressure from within the cell. When the internal pressure of the LIB reaches a critical limit, the safety vent is designed to rupture and effectively reduces outward pressure on the casing of the cell [123; 124; 125; 4].  
The CID is activated by elevated temperatures commonly caused by overheating, overcharging and short circuiting, functioning when the vapor pressure build up breaks the solder joints welded to the aluminum sheet, releasing the pressure relief sheet that turns over and creates an open circuit with the battery [4].  
The main aim of PTC electrodes is to protect LIBs during abnormal electrical conditions such as overcharging and overdischarging, etc. PTC electrodes simply increase their internal resistance drastically with increasing temperature, effectively providing a self-current-limiting capability to reduce the heat generated by LIB reactions [126; 127; 128; 129; 130; 131].
## 4 Discussion
### Homogeneous Cylindrical Phantom  
As expected, ROI delineation considering 80% of the physical volume of the cylindrical phantom provided the best activity concentration estimate (deviation below 2%). This delineation method has the potential to exclude spill out of activity from the uniform phantom. Image noise on the same phantom was assessed in terms of coefficient of variation, i.e., the ratio of the standard deviation of the activity values to the mean activity value. We found COVs in the range 23-36%, in keeping with [PERSON] et al. [17] reporting COV values from 12 to 38% for the homogenous phantom. This finding suggests that image noise is intrinsically high and longer acquisition times should be preferred when possible.  
Concerning the image reconstruction method, xSPECT software using both OSCG and OSEM algorithm resulted to be equivalent when normalized to their maximum values. Our results suggest that xSPECT modality still presents wide margins of improvement since attenuation and scatter corrections performed by this software may introduce some artifacts in the activity distribution within hollow objects.
## 3 Smart and Secure HIE Systems: SSHES
### Integrating AI and Blockchain into HIE Systems: Practical Challenges and Solutions
#### 3.6.7 Cost-Benefit and Funding Strategies  
AI and blockchain technologies have been widely adopted in many health institutions and centers. However, one of the main reasons preventing the full adaptation of such technologies in all health organizations is the implementation cost. In this section, we discuss the cost factor when integrating AI and blockchain into HIE systems while highlighting some funding strategies that help the health institutions in the implementation process.  
* Infrastructure phase: this indicates the cost of using various hardware, software, and services needed in the adaptation of such technologies in the current infrastructure of the health entity.
- Training and development phase: this is the cost for training healthcare staff and IT personnel to be familiar with the use of the new technologies.
Maintenance and upgrades phase: this indicates the ongoing expenses for system maintenance, security updates, and periodic upgrades to keep the technology current. - Compliance and legal fees phase: this includes the costs for ensuring compliance with healthcare regulations and potential legal fees associated with data privacy and security. * Funding Strategies: implementation cost mostly represents a barrier for adopting AI-blockchain systems, particularly for smaller health entities with limited budgets. Thus, to cover the extensive implementation cost, some funding strategies are recommended: - Diversified funding sources: health entities can explore multiple funding sources such as government grants, private investments, and partnerships with technology vendors. - Phased implementation approach: this aims to integrate AI and blockchain technologies in phases to spread costs over time. This strategy is highly dependent on the scalability vision of the health entity: with a horizontal scalability vision, the entity can investigate technologies based on its current budget and then build up its resources over time. With the other vision, e.g., vertical scalability, the entity can use cloud services to implement an initial framework and may extend the resources over time.
## 3 Soil-Dependent Design of Vibration Control System  
### Earthquake Ground Motion Representation  
The structure's displacements and accelerations are within specified limits for the given fundamental structure period and damping ratio, according to the seismic design codes. The parameters that mainly control these limits are the ground conditions (soil type) and the expected seismic intensity. However, the selection of the VCS parameters is not possible by following such approaches due to complexity of the VCS and the geometrical and manufacturing limitation imposed for a realistic design.  
Therefore, analysis in the time-domain is necessary. Ground motions can be generated, among others, from: synthetic (seismological models), real earthquakes (not all soil types, not smoothed spectra), and artificial accelerograms, compatible with a specific design response spectrum [32; 33]. In this paper, a database of design response spectrum (EC8) compatible ground acceleration excitations for various soil types will be generated using the SeismoArtif Software [34]. More specifically, 10 artificial accelerograms are generated for each of the following soil types: (1) firm (ground type A), (2) medium (ground type C), and soft (ground type D). For each soil type, the following seismic properties are assumed: spectral acceleration 0.36 g, spectrum type I and importance class II. Figure 3 presents the mean acceleration response spectrum of all the artificial accelerograms and soil types, as compared to the respective EC8 design response spectrum. An accurate match is observed in all cases.
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
###### Abstract  
Orthogonal Time Frequency Space (OTFS) is a new, promising modulation waveform candidate for the next-generation integrated sensing and communication (ISaC) systems, providing environment-awareness capabilities together with high-speed wireless data communications. This paper presents the original results of OTFS-based person monitoring measurements in the 60 GHz millimeter-wave frequency band under realistic conditions, without the assumption of an integer ratio between the actual delays and Doppler shifts of the reflected components and the corresponding resolution of the OTFS grid. As the main contribution of the paper, we propose the use of the persistent homology technique as a method for processing gathered delay-Doppler responses. We highlight the advantages of the persistent homology approach over the standard constant false alarm rate target detector for selected scenarios.  
O tFS; millimeter-waves; persistent homology; CFAR; person detection +
Footnote †: journal: Sensors  
0000-0001-589-7880][PERSON]  
1,2,2,224
# ALReg: Registration of 3D Point Clouds Using Active Learning
###### Abstract  
After the success of deep learning in point cloud segmentation and classification tasks, it has also been adopted as common practice in point cloud registration applications. State-of-the-art point cloud registration methods generally deal with this problem as a regression task to find the underlying rotation and translation between two point clouds. However, given two point clouds, the transformation between them could be calculated using only definitive point subsets from each cloud. Furthermore, training time is still a major problem among the current registration networks, whereas using a selective approach to define the informative point subsets can lead to reduced network training times. To that end, we developed ALReg, an active learning procedure to select a limited subset of point clouds to train the network. Each of the point clouds in the training set is divided into superpoints (small pieces of each cloud) and the training process is started with a small amount of them. By actively selecting new superpoints and including them in the training process, only a prescribed amount of data is used, hence the time needed to converge drastically decreases. We used DeepBBS, FMR, and DCP methods as our baselines to prove our proposed ALReg method. We trained DeepBBS and DCP on the ModelNet40 dataset and FMR on the 7 Scenes dataset. Using 25% of the training data for ModelNet and 4% for the 7 Scenes, better or similar accuracy scores are obtained in less than 20% of their original training times. The trained models are also tested on the 3 DMatch dataset and better results are obtained than the original FMR training procedure.  
point cloud registration; active learning +
Footnote †: journal: Accepted: 14 June 2023
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 2 PRAN-Net Fabric Defect Detection Method Based on Faster R-CNN  
Figure 1 shows the fabric defect detection process of the PRAN-Net based on the Faster R-CNN proposed in this paper. First, the FPN [22] is used to extract feature maps at different scales from the fabric images. Then, priori anchors are adaptively generated in each scale feature map as the defect proposals. Finally, a classification network is established to classify and refine the position of defects in fabric images by the defect proposals.
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 2 Project Overview
### Numerical Models and Simulation Scheme
#### 3.2.3 Simulation Scheme  
Based on the traditional research method of loose zones in a surrounding rock mass, the following influencing factors were determined as the primary research objects: tunnel burial depth (\(H\)), tunnel span (\(B\)), tunnel height-span ratio (\(\lambda\)), lateral pressure coefficient (\(K\)), and GSI. There were five influencing factors in total, and each factor had four levels. This paper established 16 numerical models to carry out the orthogonal test in line with the orthogonal table \(L_{16}(4^{5})\).  
According to the geological conditions and cross-sectional dimensions of the large-span section of the bifurcation, \(H\) was 80-110 m, \(B\) was 20-35 m, \(\lambda\) was 0.6-1.2, \(K\) is 0.5-2.0, and the GSI was 25-40. The orthogonal table and the rock mass mechanical parameters corresponding to GSI are shown in Tables 3 and 4, respectively. The bulk density and Poisson's ratio of the rock mass were identified by geological exploration reports. The \(\sigma_{ci}\) value was mainly obtained from a laboratory test, and \(m_{b}\) and \(s\) can be evaluated via Correlation 2. The GSI-based empirical equations [32,33] used to determine the elastic modulus \(E\) and \(m_{i}\) are proposed as follows:  
\[E=100(1-D/2)/\{1+\exp[(75+25D-\text{GSI})/11]\} \tag{3}\]  
\[m_{i}=0.7375\text{GSI}^{0.7586} \tag{4}\]  
The numerical calculation model of the unit thickness was set as: height \(\times\) width = 180 m \(\times\) 300 m (210 m \(\times\) 300 m, 240 m \(\times\) 300 m, 270 m \(\times\) 300 m). The element mesh and model dimension are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GSI** & \(\sigma_{ci}\) **(MPa)** & **Bulk Density (kN/m3)** & **Elastic Modulus (GPa)** & **Poisson’s Ratio** & \(m_{i}\) & \(m_{b}\) & \(s(10^{-3})\) \\ \hline
40 & 70.65 & 23 & 4.73 & 0.28 & 12.11 & 1.136 & 1.018 \\
35 & 59.28 & 22 & 3.25 & 0.31 & 10.94 & 0.859 & 0.584 \\
30 & 48.89 & 21 & 2.21 & 0.32 & 9.73 & 0.639 & 0.335 \\
25 & 39.67 & 20 & 1.49 & 0.34 & 8.48 & 0.466 & 0.192 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical parameters of the rock mass corresponding to different GSI values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Buried Depth** & **Tunnel Span** & **Height–Span Ratio** & **Lateral Pressure Coefficient** & **GSI** \\
**Number** & **H/m** & **B/m** & \(\lambda\) & **K** & **GSI** \\ \hline
1 & 80 & 20 & 0.6 & 0.5 & 40 \\
2 & 80 & 25 & 0.8 & 1.0 & 35 \\
3 & 80 & 30 & 1.0 & 1.5 & 30 \\
4 & 80 & 35 & 1.2 & 2.0 & 25 \\
5 & 90 & 20 & 0.8 & 1.5 & 25 \\
6 & 90 & 25\(H\) represents the vertical distance from the tunnel vault to the ground. The model's boundary conditions were set so that the upper boundary was a free surface boundary, and both sides and the bottom edges were constrained by the method of normal displacement. The initial in situ stress was the self-weight stress of the rock mass.
# Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video
## 4 Experimental Results
### Embedding Capacity  
Although the primary goal of our schemes is to reduce the loss of video quality and the increase in file size, the embedding capacity should not decrease too much. In this section, the embedding capacity of different schemes is evaluated. The results of the schemes using the \((0,0)\) coefficient pairs are shown in Table 2. It can be learned that the capacity of [33] is lowest, which is significantly lower than that of our scheme and [34]. Furthermore, the capacity of our scheme is very close to that of [34], and the difference is generally around 1%, which is basically negligible.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Video**} & \multicolumn{6}{c}{**Embedding Capacity (bits)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**QP = 16**} & \multicolumn{2}{c}{**QP = 28**} \\ \cline{2-7}  & **Ours\({}^{+}\)** & [33] & [34] & **Ours\({}^{+}\)** & [33] & [34] \\ \hline \multirow{2}{*}{foreman} & 3,415,024 & 2,991,010 \((-12.4\%)\) & 3,411,252 \((-0.1\%)\) & 442,374 & 387,443 \((-12.4\%)\) & 445,023 \((+0.6\%)\) \\  & 2,197,152 & 1,914,967 \((-12.8\%)\) & 2,206,489 \((+0.4\%)\) & 180,453 & 156,344 \((-13.4\%)\) & 182,238 \((+1.0\%)\) \\ \cline{1-1} \cline{2-7} bus & 3,885,660 & 3,216,720 \((-17.2\%)\) & 3,874,676 \((-0.3\%)\) & 1,442,234 & 1,267,463 \((-12.1\%)\) & 1,453,286 \((+0.8\%)\) \\ \cline{1-1} \cline{2-7} crew & 3,888,517 & 3,488,241 \((-10.3\%)\) & 3,886,072 \((-0.1\%)\) & 1,048,725 & 928,943 \((-11.4\%)\) & 1,051,886 \((+0.3\%)\) \\ \cline{1-1} \cline{2-7} hall\_monitor & 4,213,795 & 3,762,970 \((-10.7\%)\) & 4,219,961 \((+0.1\%)\) & 250,843 & 217,846 \((-13.2\%)\) & 253,323 \((+1.0\%)\) \\ \cline{1-1} \cline{2-7} mobile & 2,814,823 & 2,134,882 \((-24.2\%)\) & 2,823,286 \((+0.3\%)\) & 1,597,461 & 1,357,855 \((-15.0\%)\) & 1,614,352 \((+1.1\%)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The embedding capacity of the schemes using the \((0,0)\) coefficient pairs, where the percentage figures in parentheses indicate the relative difference between other schemes and Ours\({}^{+}\).  
The results of the schemes without using the \((0,0)\) coefficient pairs are shown in Table 3. As can be seen from Table 3, although the embedding capacity of our proposed scheme is lower than that of [37], it is still higher than that of [29]. The experimental results are consistent with the previous analysis presented in Section 3.2.
## 2 Results and Discussion
### Profiles of Acid Compounds  
Sixteen acid compounds were identified in the different combinations of _L. delbrueckii_ subsp. _bulgarcius_ IMAU20401 and _S. thermophilus_ ND03 (Table 1). Hexanoic acid is a saturated fatty acid with six carbons and one carboxylic group [(37)]. This acid compound is an important aroma compound that has been detected frequently in a number of different dairy products [(14; 38)]. In this study, hexanoic acid was the most abundant acid compound and was found in all treatment combinations at concentrations ranging from 22.88 to 40.57 \(\upmu\)g/L. The next most abundant was acetic acid, followed by butanoic acid and octanoic acid. Acetic acid is the main subproduct of LAB fermentation [(39; 40)]. In this study, acetic acid concentrations ranged from 7.89 to 24.65 \(\upmu\)g/L, with the highest value of 24.65 \(\upmu\)g/L seen in the 1:1 treatment combination. [PERSON] et al. reported similar results [(34)]. In the present study, high levels of butanoic acid and octanoic acid were detected in all treatment combinations and ranged from 0.62 to 16.83 and from 6.08 to 13.43 \(\upmu\)g/L, respectively. These acid compounds are frequently found in dairy products, including fermented milk [(33)].  
3-Methylbutanoic acid and its corresponding alcohols (3-methylbutanol) and aldehydes (3-methylbutanal) are amino acid degradation products. 3-Methylbutanal can be converted to 3-methylbutanoic acid via oxidation [(40)]. In this study, 3-methylbutanoic acid concentrations ranged from 3.35 to 8.33 \(\upmu\)g/L, peaking in the 1:50 treatment combination. The concentrations of 2-methylhexaneoic acid, 2-methylbutanoic acid, and pentanoic acid were 2.74-8.15, 7.16-12.42, and 8.38-11.86 \(\upmu\)g/L, respectively. These values also peaked in the 1:50 treatment combination.  
Low levels of heptanoic acid, nonanoic acid, 2-undecenoic acid, and \(n\)-decanoic acid were also detected at concentrations of 1.04-2.02, 1.69-2.99, 0.35-1.11, and 1.07-3.79 \(\upmu\)g/L, respectively. Although these concentrations were relatively low compared with those of the other acid compounds, they have also been reported frequently in dairy products by other groups [(14; 16; 23)].