esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 5 Results and Analysis
### 2 What Do the Proposed IoT Maturity Models Consider the Main Dimensions?
#### 5.2.2 Q2
The papers found that the only study that reports an explicit roadmap for advancing maturity is [61], which supports improvement by providing a structured and stepwise approach to integrating metrological principles and methods into IoT sensor networks. The maturity model also provides a framework for assessing sensor network metrology's current and desired states and identifying gaps and actions needed to improve this section. Likewise, another maturity model explicitly outlines a continuous improvement process, where the mechanism provides a continuous improvement process that allows the organization to monitor and evaluate the progress and results of improvement actions and initiatives and adjust strategies and actions accordingly. This process involves collecting and using data and feedback, the regular and frequent use of the maturity model for a self-assessment or external audit, and the iterative and incremental improvement cycle aligned with the organization's objectives and strategies. In this sense, the proposal by [PERSON] et al. [63] supports post-measurement maturity improvement by providing a roadmap for the organization to identify the strengths and weaknesses of its current processes, prioritize areas for improvement, set goals and objectives for the desired maturity level, implement actions and changes for improvement, monitor and control improvement progress and performance, and evaluate and validate the results and benefits of the improvement. In this sense, the proposed maturity models that support maturity improvement after measurement allow for identifying several drawbacks, such as the lack of empirical validation [46] or practical application of improvement suggestions, the lack of specific guidelines or methods on how to improve the maturity level, giving an ambiguous improvement process [63], and the lack of customization or adaptation of the models to the specific context and needs of the organization [66]. At a general level, the information gathered in this review allows for the conclusion that the proposed maturity models do not explicitly support the improvement of maturity after measurement, positioning themselves more as analysis tools that provide a snapshot of the current state of maturity than as improvement elements to achieve higher maturity levels except in specific cases. In this sense, driving organizational maturity improvement seems to be an area that deserves further research for this type of mechanism in the IoT domain, so in future work, providing a clear and comprehensive framework that helps the organization to understand its current situation, compare it with best practices or benchmarks, identify strengths and weaknesses, and set objectives and goals for improvement seems to be a significant contribution to this domain.
# Depth-Extrapolation-Based True-Amplitude Full-Wave-Equation Migration from Topography
## 3 Examples
### Horizontal Layer Model
Figure 3: (**a**) Comparison of the calculated reflection coefficient (blue line) of Method 1 and the theoretical reflection coefficient (red line); (**b**) The relative error between the calculated reflection coefficient and the theoretical coefficient of Method 1; (**c**) Comparison of the calculated reflection coefficient (blue line) of Method 2 and the theoretical reflection coefficient (red line); (**d**) The relative error between the calculated reflection coefficient and the theoretical coefficient of Method 2; (**e**) Comparison of the calculated reflection coefficient (blue line) of Method 3 and the theoretical reflection coefficient (red line); (**f**) The relative error between the calculated reflection coefficient and the theoretical coefficient of Method 3. In this experiment, there is a lateral velocity change in the upper layer of the velocity model. The vertical partial derivative calculation method used in Method 1 needs to assume a uniform surface velocity. When this assumption is not satisfied, there will be a non-negligible error in the calculated derivative. It will affect the amplitude-preserving performance of the imaging result. The performance of Method 2 and Method 3 are very close, and both methods can handle lateral surface velocity variation. However, these two methods are based on different acquisition systems: Method 2 is based on a dual-sensor acquisition system, and Method 3 is based on a conventional acquisition system. All in all, compared with traditional methods, our proposed vertical partial derivative calculation method can handle lateral surface velocity variation. Compared with the method based on a dual-sensor acquisition system, our imaging effect is similar, but our acquisition system is simpler and more practical.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 4 Locata Applications  
Locata technology provides centimetre-level accuracy positioning that could complement, or even replace, conventional GNSS in classically difficult GNSS signal environments, such as open-cut mines, deep valleys, heavily forested areas, urban environments, and even indoor locations [55].
## 6 Autonomous Driving
### Autonomous Vehicle Technologies
#### 6.1.4 Telematics Technology  
Telematics is compounded by the terms \"telecommunications\" and \"informatics\". Generally, Telematics is every application of telecommunications that integrates information exchange and services technology. As a technology covering everything about GPS and navigation systems, it can send, receive, and store information related to remote items--such as vehicles--through telecommunication devices [142]. Telematics systems help insurance companies predict traffic dangers based on recorded human driving behavior and readily identify valid and independent safety features [143]. Telematics technologies used in autonomous driving include mobile networks, for example, 5G and LTE, and global navigation satellite systems such as GPS, Galileo, and BDS [144]. As described in the previous section, \"GPS and the cloud\", the high accuracy of real-time positioning guidance from GNSS and traffic information from the cloud will ensure that the vehicle drives to the destination accurately and quickly, following the optimal path.  
Figure 9: (**a**) Generated three-dimensional map for navigation [138]; (**b**) Visual odometry in an autonomous car (red dots for map points) [139].
## 2 Design and Performance of Ultra-Intense \"J-KAREN\" Laser System
### OPCPA Preamplifier
After exiting the OPCPA unit pulses pass through the second saturable absorber and an ultrafast Pockels cell for eliminating the nanosecond ASE pedestal on the OPCPA output to further enhance the contrast. Our Pockels cell (UPC 068, Laysop Ltd.) has a rise time of 150 ps, an aperture of 6 mm in diameter, and the temporal window of 2.5 ns. Figure 2(a) displays typical output spectra at the OPCPA preamplifier exit with and without OPCPA amplification. The spectrum with OPCPA amplification, as shown in Figure 2(a), exhibits significant broadening by almost a factor of two to reach 70 nm (FWHM). Figure 2(b) shows the output spectra after the second 1 mm thick saturable absorber with and without OPCPA adjustment. The dashed blue curve in Figure 2(b) shows the output spectra by just inserting the saturable absorber after OPCPA amplification, as shown in Figure 2(a). This extra saturable absorber at the OPCPA exit removes much of the parametric fluorescence and thereby enhances the contrast, but with about 40% transmission efficiency (about 60% energy loss) at an incident intensity of 10 MW/cm\({}^{2}\) and a small bandwidth reduction due to the transmittance characteristic of our saturable absorber, as seen in Figure 2(b). However, as can be seen from the spectra in Figure 2(b), it is possible to obtain shaped and broadened amplified pulse spectra to obtain near 70 nm bandwidth (FWHM) with OPCPA amplification by adjusting pulse timing and phase-matching conditions [56]. Using a saturable absorber is a simple way to remove the ASE, but it can also result in spectral narrowing in CPA systems. Within the OPCPA, altering the phase-match setting makes it possible to tune the central wavelength of the amplified pulse and, therefore, spectral shaping of the amplified pulse in a given spectral range is possible by cascading two BBO crystals working under slightly different phase-match settings. Another significant advantage of the OPCPA system [56,61] is that spectral control can also be achieved by varying the noncollinear pump-signal angle and the amplification pulse timing. The OPCPA output spectrum can also be optimized to better match the gain characteristics of the subsequent amplifiers. Significant changes of spatial profile quality have not been observed with and without the second saturable absorber. Figure 2: Gain from optical parametric chirped-pulse amplification (OPCPA) preamplifier stage _versus_ pump energy.
# MixFormer: A Self-Attentive Convolutional Network for 3D Mesh Object Recognition
## 3 MixFormer
### 3D Mesh Convolution Block
#### 3.1.4 Three-Dimensional Sampling  
After the convolution operation by the polynomial convolution kernel, each vertex aggregates the local surface features of its neighborhood. However, this introduces a new problem, as each vertex contains the local surface features of its neighborhood, so there is a large amount of data redundancy. To reduce the amount of data in the network, 3D sampling can be performed based on the vertices.  
Unlike the 3D point cloud models, the 3D mesh models represent the contour features of the model by the combination of vertices and triangulated facets, and the vertices and triangulated facets are also not uniformly distributed on the model, showing a partly dense and partly sparse feature. Therefore, if the vertices are to be sampled, the farthest sampling method, which is commonly used in point cloud models to cover all points in space as uniformly as possible, is not very suitable for 3D mesh models. To solve this problem, we found that Poisson disk sampling can achieve uniform sampling according to the model contour.  
The algorithm for Poisson disk sampling is shown in Algorithm 1.  
```
Input: Input vector \(x_{i}\), desired number of samples \(N\) Output: Output vector \(y_{j}\)
1. Build a kd tree for samples
2. Allocate a heap \(S_{i}\) for each sample to store the weights \(w_{i}\)
3. Assign initialized weights to each sample. \(w_{i}=\sum_{j}w_{ij}\) \[w_{ij}=\left(1-\frac{d_{i}}{2\sqrt{\frac{d_{i}}{2\sqrt{\frac{d_{j}}{2\sqrt{ 2N}}}}}}\right)^{8}\]
4. While number of samples \(>\) desired : \(S_{i}\leftarrow\) pull the top sample from heap  For each sample \(S_{i}\) around \(S_{j}\)  Remove \(w_{ij}\) from \(w_{i}\)  Update the heap position of \(w_{i}\)
```  
**Algorithm 1**Poisson disk sampling
## 2 Methods  
### Vehicle-Selection Criteria and Test Cycle  
Table 2 lists the specifications of the vehicles used in this study. High-market-share vehicles were preferentially selected--using data provided by the Transportation Pollution Research Center in the National Institute of Environmental Research (NIER)--and vehicles with similar engine displacements were considered for minimizing differences between emission patterns. To investigate differences between pollutant-emission patterns corresponding to different engine types, gasoline-powered vehicles considered in this study included those incorporating MPI technology, wherein fuel is anteriorly dispersed to the cylinder intake valve, and GDI technology, wherein fuel is directly injected into the cylinder [10]. Vehicles incorporating GDI technology were further classified as pre- and post-PM-emission-regulation vehicles depending on whether they were manufactured prior to or after 2014 (when PM-emission regulations were first imposed on GDI-incorporated vehicles), respectively. PM-emission regulations concerning GDI vehicles influence the combustion technology employed during engine operation. This, in turn, affects the composition of exhaust gases emitted by GDI-incorporated vehicles via hardware and software control to satisfy PM regulation requirements. As already mentioned, diesel vehicles considered in this study were classified in accordance with relevant pollutant-emission regulations, such as the EURO 6 (current regulations), EURO 5 (previous regulations), and EURO 3--which includes vehicles without diesel particulate filters (DPFs) to account for the pollution contribution of older vehicles. EURO-6 compliant vehicles feature three reinstalled after-treatment devices--DPFs, diesel oxidation catalyst (DOCs), and lean NOx traps (LNTs). Accordingly, EURO-5-compliant vehicles are equipped with DOCs and DPFs, whereas EURO-3-compliant ones exclusively feature DOCs. All vehicles were run on a chassis dynamometer in accordance with NIER drive cycle test procedures followed in Korea to measure emissions of GHGs, regulated gases, and SA precursors. The NIER drive cycles have been developed based on Korean road-traffic patterns and comprise 15 different cycles based on the average vehicle speed (from low to high). Drive cycles considered in this study included NIER-01 (average speed: 4.7 km h\({}^{-1}\)), NIER-05 (average speed: 17.3 km h\({}^{-1}\)),
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 3 System Model  
The video edge scheduling model consists of two layers with different functionalities, the end layer and the edge layer, as shown in Figure 1. In the end layer, a set of UEs is randomly distributed on the ground, which can be expressed as M = {1,2,...,\(M\)}. N = {1,2,...,\(N\)} represents a set of clusters of NOMA, and K = {1,2,...,\(K\)} denotes a set of subchannels. All UEs in a NOMA cluster share one subchannel at the same time for offloading, with each subchannel having an equal bandwidth. The UEs adopt a binary offloading rule, that is, the task of each UE must be processed locally or offloaded to the edge layer. The UEs continuously transmit video analysis tasks to the edge layer. In the edge layer, a MEC server is integrated on a computation access point (CAP), which receives the tasks offloaded to the edge layer, and the MEC server provides computing services for tasks.  
The video stream calculation task of the UE \(m\) is expressed as \(D_{m}\)= {\(l_{m}\), \(c_{m}\), \(T_{m}^{max}\)}, where \(l_{m}\) represents the data size of the video stream task \(D_{m}\), \(c_{m}\) represents the total central processing unit (CPU) cycles required for the video stream task \(D_{m}\), and \(T_{m}^{max}\) denotes the maximum tolerance time for task \(D_{m}\). After \(T_{m}^{max}\), task \(D_{m}\) will be declared the process ended in failure. The data size \(l_{m}\) of task \(D_{m}\) can be expressed as  
\[l_{m}=\tau\cdot\eta_{m}^{2}, \tag{1}\]  
where \(\tau\) represents the number of bits required for a pixel to carry information. \(\eta_{m}^{2}\) indicates the video frame resolution of task \(D_{m}\).  
Although the edge environment is constantly changing, the network state and video data are stable within a short time range, therefore we split the time into discrete time slots and each time slot has a duration. At the start of each time slot, the resources are reconfigured according to the present state and historical trend to obtain the best resource distribution status for the overall and long-term results. In the rest of this section, we explain the NOMA-enabled transmission model (Section 3.1) and the edge computing model (Section 3.2).  
Figure 1: The video edge scheduling architecture.
###### Abstract  
An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model for traffic flow forecasting, named EnGS-DGR, is designed based on ensemble learning of graph convolutional network (GCN), sequence-to-sequence (Seq2 Seq) learning model, and dynamic graph reconfiguration (DGR) algorithm. At the first stage, instead of employing entire nodes in the traffic network, the DGR algorithm is proposed to reconstruct the traffic graph topology consisting of traffic nodes with tight correlation under a specific forecasting interval, where the degree of correlation among the traffic nodes is quantized from the perspective of multi-view clustering. At the second stage, GCN-Seq2 Seq integration strategy is introduced to extract the data of the spatio-temporal dependence and forecast traffic flow. We applied the proposed EnGS-DGR to two different datasets from the highways of Los Angeles County and of California's Bay Area; the simulation results show that the proposed EnGS-DGR is superior to other eight popular models for traffic flow forecasting in terms of three common performance metrics.  
traffic flow forecasting; GCN; Seq2 Seq: dynamic graph reconfiguration; traffic network Article
# Simplified Plastic Hinge Model for Reinforced Concrete Beam-Column Joints with Eccentric Beams
## 2 Current Design Codes for Effective Joint Width  
Current design codes, such as ACI 318-19 [12], ACI 352R-02 [13], and NZS 3101-2006 [14], specify the effective joint width \(b_{s}\) for concentric and eccentric beam-column joints (Figure 1). On the other hand, Eurocode 8 [15] does not consider the beam eccentricity in calculation of the effective joint width.  
\[b_{s}=\min[b_{b}+2x,b_{b}+h_{c},b_{c}]\text{ in ACI }318-19 \tag{1}\]  
\[b_{s}=\min\left[\frac{b_{b}+b_{c}}{2},b_{b}+\sum m\frac{h_{c}}{2},b_{c}\right] \text{ in ACI }352\text{R}-02 \tag{2}\]  
\[b_{s}=\min\left[b_{c},b_{b}+\frac{h_{c}}{2},\frac{b_{b}+b_{c}}{2}+\frac{h_{c} }{4}-e\right]\text{ in NZS }3101-2006 \tag{3}\]  
\[b_{s}=\min[b_{c},b_{b}+0.5h_{c}]\text{ in Eurocode }8 \tag{4}\]  
where \(b_{b}\) is the beam width; \(h_{c}\) is the column depth; \(b_{c}\) is the column width; \(x\) is the smaller distance between the beam and column edges; \(m=0.3\) when the eccentricity \(e\) is greater than \(b_{c}/8\) or \(m=0.5\) otherwise; and \(e\) is the eccentricity between the beam and column centerlines. In Equation (2), the value of \(ml_{c}/2\) is limited to the distance between the beam and column edges at each side.  
Figure 2 compares the effective joint width \(b_{s}\) calculated by the current design codes (Equations (1)-(4)). As shown in the figure, for concentric beam-column joints (Figure 2a), the effective joint width was increased with the increase in the column width, and ACI 352R-02 [13] provided the smallest estimation. On the other hand, for eccentric beam-column joints (Figure 2b with \(x=0\)), Eurocode 8 [15] ignoring the beam eccentricity provided the largest estimation, while ACI 318-19 [12] having a limitation of \(x=0\) provided the smallest estimation. As compared in Figure 2c, the effective joint width was decreased as the beam eccentricity increased (except Eurocode 8 [15]), and ACI 352R-02 [13] provided a smaller effective joint width than NZS 3101-2006 [14].
## 3 Functional Roles of Nicotinamide and Pyridoxine
### Biochemical Structure and Classification
#### 3.1.1 Nicotinamide (Vitamin B3)  
Nicotinamide, also known as niacinamide, is one of the two principal forms of vitamin B3 (the other being nicotinic acid). It is structurally composed of a pyridine ring with a carboxamide group at the 3-position. It serves as a precursor to nicotinamide adenine dinucleotide (NAD\({}^{+}\)) and its phosphate form NAD\({}^{+}\)--crucial cofactors in redox reactions, mitochondrial function, DNA repair, and cellular signaling [20; 31; 105; 109; 110].
# The Influence of Substrate and Strain on Protein Quality of _Pleurotus ostreatus_
## 3 Results and Discussion  
### Protein Solubility  
The influence of pH on the protein solubility of flour from _P. ostreatus_ cultivated on different substrates is shown in Figure 1. In all cases, a U-shaped solubility profile was developed with minimum solubility at pH 4 and maximum solubility at pH 11-12. Most plant proteins also exhibit a U-shaped solubility curve due to their acidic nature connected to the greater amount of Asp and Glu residues than of Lys, Arg and His residues [22; 36]. The lowest solubility (at pH 4) was noted for AMRL150 cultivated on WS (0.65 \(\pm\) 0.08 mg/mL) (Table S1). Obviously, pH 4, where the minimum solubility of proteins was observed, is near their isoelectric point, which favors their precipitation. For this reason, pH 4 was selected to accomplish the protein separation from the rest of the compounds in the current study. [PERSON] et al. (2021) [18] also reported that a pH between 3.0 and 4.0 coincides with the lowest solubility of flour protein from _P. ostreatus_ cultivated on sorghum forage. The extraction of proteins from our samples was performed at pH 12. The solubility of 150 WS-F (Table S1) at pH 12 is significantly higher than at 11, whereas for the rest of the _P. ostreatus_ flours tested, the solubility did not differ significantly between the aforementioned pHs. Among all cases, the maximum solubility (1.67 \(\pm\) 0.19 mg/mL) was noted for the strain 150 cultivated on BOS at pH 11. In the limited literature on the extraction of _P. ostreatus_ proteins [21], the conventional alkaline extraction followed by acidic precipitation around the isoelectric point was usually employed [18; 34].  
Figure 1: Effect of pH on the protein solubility of flours from two strains of _P. ostreatus_ (AMRL144 and 150) cultivated on wheat straw (WS) and barley and oat straw (BOS).
## 3 Results and Discussion
### Multiple Freeplay Nonlinearities  
For a system with both bending and torsion freeplay nonlinearities, when continuing to use the DF method, it is necessary to assume two LCO amplitudes \(A_{\rm B}\) and \(A_{\rm T}\) to obtain the equivalent stiffnesses \(\bar{K}_{\rm B}\) and \(\bar{K}_{\rm T}\). Using the root-locus method, the corresponding flutter dynamic pressure, frequency, and eigenvector of the equivalent linear system can be obtained. The LCO amplitude ratio \(R\) in the bending and torsional DOFs can be calculated using Equation (21). Then, it is necessary to solve the problem that \(R\) is inconsistent with the assumed amplitude. In this section, a discrete scanning method is first introduced, and then the influence of the freeplay size ratio \(\delta_{\rm B}/\delta_{\rm T}\) on the LCO characteristics in two different stiffness cases is analyzed.