esa-sceva/satcom-chunk-collection huggingface dataset:
# Ride Comfort Prediction on Urban Road Using Discrete
## 2 Roughness Indexes and Ride Comfort Standard  
### International Roughness Index  
The IRI is calculated using a standard quarter-car model traveling on a single road profile at 80 km/h, as shown in Figure 1. The quarter-car model is characterized by five constants.  
The mathematical expression of the IRI is shown in Equation (1) [11],  
\[\text{IRI}=\frac{1}{L}\int_{0}^{T}\big{|}\dot{z_{s}}-\dot{z_{u}}\big{|}dt \tag{1}\]  
where \(z_{s}\) and \(z_{u}\) are the vertical motion for the sprung and unsprung masses in Figure 1. Due to the quarter-car model being a linear system, state space representation is introduced to solve the suspension response excited by a given road profile, as shown in Equation (2),  
\[\begin{cases}\dot{\textbf{x}}(t)=A(t)\textbf{x}(t)+B(t)\textbf{u}(t)\\ \textbf{y}(t)=C(t)\textbf{x}(t)+D(t)\textbf{u}(t)\end{cases} \tag{2}\]  
where the vectors \(\textbf{x}\), \(\textbf{y}\), and \(\textbf{u}\) are the state vector, the output vector, and the input vector, respectively. The matrices \(A\), \(B\), \(C\), and \(D\) are the state matrix, the input matrix, the output matrix, and the feedthrough matrix, respectively. For the quarter-car model, the state space equations can be written as Equations (3) and (4):  
\[\begin{bmatrix}\dot{z}_{s}\\ \dot{z}_{s}\\ \dot{z}_{u}\\ \ddot{z}_{u}\end{bmatrix}=\begin{bmatrix}0&1&0&0\\ -k_{2}&-c&k_{2}&c\\ 0&0&0&1\\ \frac{k_{2}}{\dot{u}}&\frac{c}{\dot{u}}&\frac{-(k_{1}+k_{2})}{\mu}&\frac{-c}{ \mu}\end{bmatrix}\begin{bmatrix}z_{s}\\ \dot{z}_{s}\\ z_{u}\\ \dot{z}_{u}\end{bmatrix}+\begin{bmatrix}0\\ 0\\ 0\\ \frac{k_{1}}{\mu}\end{bmatrix}z(t) \tag{3}\]  
\[\dot{z}_{s}-\dot{z}_{u}=\begin{bmatrix}0&1&0&-1\end{bmatrix}\begin{bmatrix}z_ {s}\\ \dot{z}_{s}\\ z_{u}\\ \dot{z}_{u}\end{bmatrix} \tag{4}\]  
Based on Equation (4), the IRI can be obtained by accumulating the suspension responses over the travel time. For typical paved roads, the IRI ranges from 0 to 12 m/km, where a low IRI indicates a smooth road profile and comfortable driving, whereas a high index indicates an uneven road profile.
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 4 Evaluation
### Experiment II: Outdoor Navigation
#### 4.2.2 Method  
Prior to the test, both subjects completed experiment I in their own homes to get used to the navigational directions provided by the user interface. The results were considered satisfactory.  
The subjects were then transferred by car from their respective homes to the chosen environments. They were totally naive about the locations. The subjects were asked to move according to the pattern felt. They were asked to use the white cane as they would do normally in a common walk. They were requested to raise their hand if they felt lost at some point or if they needed the navigational instruction refreshed on the interface. In that case, a route recalculation would be performed, and new instructions would be generated. They had no time restrictions in which to complete the test. They were aware that an assistant was nearby and that they could terminate the test at any time if they felt uncomfortable.  
For both subjects, GPS coordinates and navigation times were registered in the GIS.
# Study of Annealing Temperature Effect on the Photovoltaic Performance of BiOI-Based Materials
## 4 Conclusions  
In our work, the synthesis and annealing treatment of BiOI, as well as its effect on photovoltaic activity, were studied. We highlighted that the annealing treatment of BiOI resulted in oxygen-abundant and iodine-deficient bismuth materials from the parent BiOI. The changing in temperature affected the structural, morphological, and its optical properties of BiOI films. As a consequence, it performed different parameters of the photovoltaic cell. The annealed BiOI at 300 \({}^{\circ}\)C, 450 \({}^{\circ}\)C, and 550 \({}^{\circ}\)C allowed for the transformation from BiOI to Bi\({}_{7}\)O\({}_{9}\)I\({}_{3}\), Bi\({}_{5}\)O\({}_{7}\)I, and \(\beta\)-Bi\({}_{2}\)O\({}_{3}\). The heated material at 300 \({}^{\circ}\)C, as Bi\({}_{7}\)O\({}_{9}\)I\({}_{3}\) had the best photovoltaic cell performance in comparison to other materials--its efficiency was three times higher than that of BiOI. The broadening on the FWHM of (001) as the effect of annealing treatment, the different grain size of this material, and an adequate harvesting light ability can be expected as the reasons why Bi\({}_{7}\)O\({}_{9}\)I\({}_{3}\) is more suitable in photovoltaic applications than BiOI. This behavior is in line with the performance of Bi\({}_{7}\)O\({}_{9}\)I\({}_{3}\) in reported photocatalytic reaction works. Based on this result, Bi\({}_{7}\)O\({}_{9}\)I\({}_{3}\) can be suggested to be implemented as the absorber in photovoltaic devices involving matched hole and electron transport layers.  
Conceptualization, A.A.P. and T.S.; methodology, A.A.P. and S.K.; validation, S.K., N.K. and T.S.; formal analysis, A.A.P.; investigation, A.A.P.; resources, T.S.; writing--original draft preparation, A.A.P.; writing--review and editing, S.K., N.K. and T.S.; supervision, S.K., N.K. and T.S.  
This research received no external funding.  
A.A.P. would like to thank the financial support from MORA Scholarship, Ministry of Religious Affairs, The Republic of Indonesia for the Ph.D. scholarship (No. 36/Dt.LIV/4/FP07/01/2017).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 7: The contact angle of annealed BiOI at different temperatures (**A**–**C**): 100 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, and 550 \({}^{\circ}\)C.
# Structure of All-Digital Frequency Synthesiser for IoT and IoV Applications
## 4 All-Digital Frequency Synthesiser for Multiband Transceivers
Also, it is assumed that frequency tuning step is 50 kHz, frequency of reference signal is 40 MHz, \(\Delta\Sigma\) dithering frequency is equal to divided-by-4 DCO's frequency, \(\Delta\Sigma\) modulator controls 8 bits and order of \(\Delta\Sigma\) modulator is 2. Also, the resolution of TDC is set to 4 ps and filter's coefficients are \(\alpha=2^{-7}\), \(\rho=2^{-10}\), \(\lambda_{0}=\lambda_{1}=\lambda_{2}=\lambda_{3}=2^{-5}\). Noise of reference signal was approximated by following equation, derived from data of crystal oscillator [35]:  
\[\mathcal{L}_{\text{Osc,REF}}(f)=\frac{10^{-6.72}}{f^{3}}+\frac{10^{-8.52}}{f^{ 2}}+\frac{10^{-11.33}}{f}+10^{-15.71}. \tag{12}\]  
It is seen from Figure 11 that the synthesiser's in-band phase noise is lower than \(-100\) dBc/Hz. Out-of-band phase noise corresponds to the oscillator's component of the DCO's phase noise and is equal to \(-116.4\) dBc/Hz and \(-134.0\) dBc/Hz respectively at 1 MHz and 10 MHz offset from 5.44 GHz carrier. Out-of-band \(\Delta\Sigma\) dithering noise is about 20 dBc/Hz lower than the DCO's noise. As can be seen, the presented blocks of the synthesiser meet the defined requirements. Comparison of calculated parameters of frequency synthesisers to other works is shown in Table 2. Here \(F_{\text{min}}\), \(F_{\text{max}}\)--maximum and minimum operating frequency, \(F_{\text{off}}\), \(F_{\text{c}}\)--frequency offset and carrier frequency at which phase noise is provided, \(\mathcal{L}[F_{\text{off}}]\)--phase noise, \(P\)--power consumption. Figure 11: Calculated achievable phase noise and its components of frequency synthesiser, when presented time to digital converter, digitally controlled oscillator and synthesiser’s filter are used.
## 3 Example Application
### Calculation of the GM(1,1) Prediction Model  
Taking the actual standing pre-camber of girder sections 1#-13# in the above table as an example, the sample data of the first 10 girder sections are taken for modelling using the modelling steps in Section 2.2, and the prediction of girder sections 11#-13# is carried out. The main modelling process is as follows:  
(1) This is the original sequence of the actual vertical mould's pre-camber:  
\[X^{(0)}=[0.0251,0.0314,0.0374,0.04,0.0421,0.045,0.0471,0.0511,0.0523,0.0553,0. 0564]\]  
The \(1\)-AGO sequence is obtained by accumulating the original sequence once:  
\[X^{(1)}=[0.0251,0.0565,0.0939,0.1339,0.1760,0.2210,0.2681,0.3192,0.3715,0.4268]\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Beam Section Number** & **T\({}^{\circ}\)C** & **G/KN** & **L/m** & **h/m** & **f1/m** & **f2/m** \\ \hline
1\# & 14.8 & 1436 & 9.5 & 6.89 & 0.0251 & 0.0251 \\
2\# & 13.6 & 1369 & 12 & 6.62 & 0.03  
We solve for the parameters \(a\) and \(b\) according to Equation (7), where the matrices \(B\) and \(Y_{n}\) are, respectively, as follows:  
\[B=\begin{bmatrix}-0.0408&1\\ -0.0752&1\\ -0.1139&1\\ -0.1549&1\\ -0.1985&1\\ -0.2446&1\\ -0.2936&1\\ -0.3454&1\\ -0.3992&1\end{bmatrix},Y_{n}=\begin{bmatrix}0.0314\\ 0.0374\\ 0.0400\\ 0.0421\\ 0.0450\\ 0.0471\\ 0.0511\\ 0.0523\\ 0.0553\end{bmatrix}\]  
To obtain \(a=-0.0616\) and \(b=0.0319\).  
Thus, the time response sequence is obtained as follows:  
\[\hat{x}^{(1)}(k+1)=\bigg{(}x^{(0)}(1)-\frac{b}{a}\bigg{)}e^{-ak}+\frac{b}{a}=0. 543e^{0.0616k}-0.5179,k=0,1,2,\ldots,9\]  
Substituting different values into the cumulative predicted value in turn and then subtracting them through Equation (11) will result in the reduced predicted value.  
The remaining sample data were modelled separately following the above steps, and the predictions are shown in Table 2.
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 5 Applications of Underwater Target Detection and Recognition Technology
### Target Detection and Recognition in Underwater Environments  
Coral reefs represent an important part of marine ecology, and their long-term monitoring is necessary because they directly reflect the health of the ecological environment. [PERSON] [70] used the deep learning convolutional neural network to automatically analyze coral reef images. After comparison, the unbiased agreement rate between expert observation and automatic observation was 97%. This approach enabled data analysis and reporting to be at least 200 times faster and cost only 1%, which significantly improved the processing speed of the coral reef monitoring data. [PERSON] [71] used different image enhancement methods to process coral images in turbid environments and conducted reliability tests, and the results showed that the Retinex algorithm performed the best in terms of the image quality and reliability.  
Many minerals are located at the bottom of the ocean, but the costs and difficulty associated with their detection are high. [PERSON] [72] used an AUV equipped with an underwater hyperspectral imager (UHI) to detect sulfide minerals in the mid-Atlantic ridge for the first time at a depth of 2350 m. [PERSON] [73] used a novel underwater hyperspectral imager (UHI) on a remote operating vehicle (ROV) to detect manganese nodule fields in the Peruvian Basin to a water depth of 4200 m. Two supervised classification methods were used to detect nodule surfaces, and the results showed that the support vector machine (SVM) method outperformed the spectral angle mapper (SAM) method.  
Deterioration of the Earth's environment has increased the risk of underwater habitat destruction, and monitoring helps to identify such environmental changes. Typically, underwater habitats are surveyed by divers or large hydrographic vessels, which is costly and risky. [PERSON] [74] used an AUV-mounted visible-light camera to capture habitat images, and then performed image enhancement and used a convolutional neural network to classify the habitats present in the images, which significantly reduced the cost of mapping. [PERSON] [75] used ROVs to map estuarine habitats, recording multiple sets of videos along the width of the estuary to identify, classify, and map habitats. This study also revealed the distribution of previously unknown invasive red seaweeds.
###### Abstract  
As a supplement to gauge observation data, many satellite observations have been used for hydrology and water resource research. This study aims to analyze the quality of the Integrated Multisatellite Retrieval for Global Precipitation Measurement (GPM IMERG) products and their hydrological utility in the Xinfengjiang River reservoir basin (XRRB), a mountainous region in southern China. The grid-based soil and water assessment tool (SWAT) model was used to construct a hydrological model of the XRRB based on two scenarios. The results showed that on a daily scale, the IMERG final run (FR) product was more accurate than the others, with Pearson's correlation coefficients (CORR) of 0.61 and 0.71 on the grid accumulation scale and the average scale, respectively, and a relative bias (BIAS) of 0.01. In Scenario I (the SWAT model calibrated by rain gauge data), the IMERG-based simulation showed acceptable hydrologic prediction ability on the daily scale and satisfactory hydrological performance on the monthly scale. In Scenario II (the SWAT model calibrated by the FR), the hydrological performances of the FR on the daily and monthly scales were slightly better than those in Scenario I (the CORR was 0.64 and 0.85, the BIAS was 0.01 and \(-\)0.02, and the NSE was 0.43 and 0.84). These results showed the potential of the FR for hydrological modeling in tropical mountain watersheds in areas where information is scarce. This study is useful for hydrological, meteorological, and disaster studies in developing countries or remote areas with sparse or low-quality networks of ground-based observation stations.  
GPM; IMERG; hydrological utility; SWAT model; rainfall--runoff modeling +
Footnote †: journal: Remote Sensing  
1  
[PERSON] Li  
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;  
[EMAIL_ADDRESS] (X.L.) [EMAIL_ADDRESS] (X.D.) [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (L.C.)
## 7 Results and Analysis
### Anomaly Classification in a Kitting Experiment
#### 7.2.3 Results
The standard VB inference algorithm [32] was used to learn the HDP-HMM model. \(\circ\)_HDP-HMM-AR-moVB:_ Finally, our results were evaluated on the HDP-HMM with first-order auto-regressive Gaussian observation for each anomaly class. A memorized online variational inference algorithm (moVB) [58] based on scalable adaptation of state complexity was used to learn this HDP-HMM model. Given that non-parametric sHDP-VAR-HMM learns the complexity of the model from the data, it produces more accurate models as reflected by the higher classification accuracies shown in Figure 11. The learned number of states for the different anomaly types is shown in Figure 12. Please note that \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**States** & **Methods** & **TC** & **HC** & **HCOO** & **NO** & **OS** & **WC** & **OTHER** & **Total Accuracy** \\ \hline \multirow{3}{*}{3} & HMM-Gauss-EM & 1.0 & 0.89 & 0.89 & 0.78 & 0.83 & 1.0 & 0.67 & 0.876 \\  & HMM-Gauss-VB & 1.0 & 0.94 & 0.95 & 0.96 & 0.88 & 1.0 & 0.67 & 0.915 \\  & HMM-AR-VB & 1.0 & 0.89 & 0.90 & 0.93 & 0.94 & 1.0 & 0.67 & 0.929 \\ \hline \multirow{3}{*}{5} & HMM-Gauss-EM & 1.0 & 0.78 & 0.84 & 0.79 & 0.79 & 1.0 & 0.67 & 0.838 \\  & HMM-Gauss-VB & 0.94 & 0.94 & 0.89 & 0.86 & 0.85 & 1.0 & 0.67 & 0.889 \\  & HMM-AR-VB & 1.0 & 0.89 & 0.95 & 1.0 & 0.98 & 1.0 & 0.67 & 0.957 \\ \hline \multirow{3}{*}{7} & HMM-Gauss-EM & 1.0 & 0.83 & 0.95 & 0.71 & 0.77 & 0.83 & 0.67 & 0.826 \\  & HMM-Gauss-VB & 1.0 & 0.83 & 0.86 & 0.79 & 0.82 & 1.0 & 0.67 & 0.863 \\  & HMM-AR-VB & 1.0 & 0.95 & 1.0 & 0.93 & 0.96 & 1.0 & 0.67 & 0.957 \\ \hline \multirow{3}{*}{10} & HMM-Gauss-EM & 0.94 & 0.72 & 0.85 & 0.86 & 0.79 & 0.94 & 0.67 & 0.826 \\  & HMM-Gauss-VB & 0.94 & 0.83 & 0.95 & 0.72 & 0.86 & 1.0 & 0.67 & 0.876 \\  & HMM-AR-VB & 0.95 & 0.83 & 0.82 & 0.93 & 0.92 & 0.95 & 0.67 & 0.889 \\ \hline \multirow{3}{*}{10} & HDP-HMM-Gauss-VB & 0.94 & 0.95 & 0.95 & 0.86 & 0.94 & 0.90 & 0.67 & 0.915 \\  & HDP-HMM-Gauss-moVB & 0.88 & 0.95 & 0.95 & 0.93 & 0.94 & 0.95 & 0.83 & 0.929 \\ \cline{1-1}  & HDP-HMM-AR-VB & 0.94 & 0.84 & 0.86 & 0.93 & 0.96 & 0.95 & 0.83 & 0.915 \\ \cline{1-1}  & HDP-HMM-AR-moVB & 1.0 & 0.95 & 0.95 & 0.93 & 0.98 & 1.0 & 1.0 & 0.971 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The classification accuracy of both classical parametric HMMs and non-parametric HMMs. The best model (observation model and number of hidden states) for each anomaly type is highlighted in blue. for equivalent parametric HMMs, a tedious model needs to be computed for each class to optimize the state cardinality between types. The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. It is evident from the confusion matrix that the classification outperforms other baseline methods.
### Requirements of Underwater Communication
#### 1.4.3 Node Placement and Algorithm  
The node placement becomes very important as the routing depends on it. Algorithms are used to find the optimal path to send the message. Less complex algorithms and proper placement of nodes are required. A complex algorithm depletes more energy and shortens the lifespan of nodes and networks [13].
## 1 Introduction  
Nickel-titanium alloy (Ni-Ti) rotary instruments must exert torque to cut and eradicate septic dentin during canal preparation; torsional stress, associated with friction between the instrument and dentin wall, accumulates in the instruments [1, 2]. The accumulated stress can be retained as residual stress--plastic deformation--after withdrawal of the instrument from the canal if the stress exceeds the elastic limit [3]. The engagement of rotary instruments, especially those with spiral-shaped active cutting edges, with the dentin wall can generate apically-directed screw-in forces, causing the instrument to become locked in the canal [4]. When this occurs, additional torque is required for the instrument to continue rotating. Thus, torsional stress is instantly accumulated in the instrument, leading to torsional fracture [5, 6], which is dissimilar to cyclic fatigue fracture caused by the repeated tension/compression stresses at the curvature [7]. Furthermore, screw-in forces may cause the instrument to engage beyond the apical foramen [8] and result in the extrusion of microbes into periodical tissue [9], root weakening, and cracks in the apical area [10].  
Numerous studies have been conducted to examine the dynamic torque and force characteristics of Ni-Ti instrument systems to identify factors having an impact on the stress generated within the rotary instruments. Though potential influencing factors, such as instrument pitch length [11; 12; 13; 14] and instrument rake angle [11; 15], have been discussed and debated, no single-most important factor has been identified. Thus, there continues to be debate on how the stress generated within Ni-Ti instruments during root canal instrumentation can be limited to a level at which clinical safety is ensured. The aim of this review was to conduct a detailed literature analysis to identify factors governing torque and force generation within Ni-Ti rotary root canal instruments during use.
# Effect of Different Drying Methods on the Quality of _Oudemansiella raphanipes_
## 3 Results and Analysis
### SEM Analysis of PS-V, PS-H and PS-I  
Scanning electron microscopy (SEM) allows us to characterize the surface morphology of the samples and to compare the morphological differences between the samples as well as the microstructure of the polysaccharides. Figure 5 shows the morphology of polysaccharide powders extracted from different drying methods of treating _O. raphanipes_ observed at 200 and 2000 electron microscopic magnifications, respectively. It can be seen that PS-H presents a loose and large lamellar structure at low magnification, and its surface can be seen to consist of a series of blocky projections at high magnification; PS-I presents a tight and smaller lamellar structure at low magnification and an irregularly arranged fragmented structure at high magnification, which may be due to the cross-linking and entanglement of polysaccharide molecules with each other. PS-V can be seen under high magnification as a reticular structure accompanied by spherical projections, indicating a large number of internal cavities, which is due to the direct sublimation of moisture from solid to gaseous states during vacuum freeze drying of the raw material without being damaged by external forces [56]. These different microscopic morphologies also indicate that the drying method affects the microstructure of polysaccharides from _O. raphanipes_.
# How Traditional Costing Methods Hinder the Development of Modular Product Architectures
## 3 Literature Review
### Economic Benefits and Effects of Modular Product Architectures
This produces a challenge for accurately assessing different product variants based on their costs [14]. It is a well-known fact that customized or specialized products incur a higher cost and will occupy a considerable part of the overhead cost pools [7]. Therefore, quantifying the cost incurred by a specific design at each stage of the life cycle may improve decision-making in product development. [PERSON] et al. [18] developed a design decision support tool to support decision-makers in the early phase of interdisciplinary product development. It is an advancement of the IMF developed by [PERSON] et al. [15], which encompasses more than 70 known impacts of modularization from the literature. The extension of the IMF focuses on quantifying the cost-saving potential of effects related to modularity by adding KPIs and cost measures. The tool is implemented in a case study, suggesting that considering economic modular effects quantitatively will greatly improve the evaluation of alternative product concepts in early-stage product development [18; 23]. However, the tool does not provide a generalizable approach, and it requires the further development of the empirical data underlying it. This means that the tool is tied to the specific results of the case study and cannot be considered generally applicable. Furthermore, it does not provide an opportunity to compare product design quantitatively in monetary terms and in accordance with existing costing methods. Only a few studies have investigated the actual feasibility of accurately allocating costs to a modular product structure using traditional Management Accounting approaches, primarily within MA research [5; 9; 20]. As argued in this study, the findings suggest that existing approaches hinder the development of modular product architectures. However, exactly why these approaches fail to provide an accurate and extensive quantitative assessment is not clearly defined. This will be investigated further in the following sections.
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
###### Abstract  
Multi-access edge computing (MEC) has become an essential technology for collecting, analyzing, and processing data generated by widely distributed user equipment (UE), wireless end-hosts, Internet of things (IoT) sensors, etc., providing real-time and high-quality networking services with ultralow end-to-end latency guaranteed between various user devices and edge cloud computing nodes. However, the cloud resources at the MEC on-site (access point) and edge site are restricted and insufficient mainly because of the operation and management constraints (e.g., limited space and capacity), particularly in the case of on-demand and dynamic service resource deployment. In this regard, we propose a selective MEC resource allocation scheme adopting a multitier architecture over a wide-area software-defined network (SDN) on the basis of our recent research work on virtual network slicing and resource orchestration. The proposed scheme provides an optimized MEC selection model considering end-to-end latency and efficient service resource utilization on the basis of the hierarchical MEC service architecture.  
virtual network; orchestration; multi-access edge computing (MEC); wide-area SDN Article