esa-sceva/satcom-chunk-collection huggingface dataset:
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
###### Abstract  
Wireless sensing has emerged as an innovative technology that enables many smart applications such as indoor localization, activity recognition, and user tracking. However, achieving reliable and precise results in wireless sensing requires an accurate distinction between line-of-sight and non-line-of-sight transmissions. This paper introduces PEiD, a novel method that utilizes low-cost WiFi devices for transmission path identification, offering real-time measurements with high accuracy through the application of machine-learning-based classifiers. To overcome the deficiencies of commodity WiFi in bandwidth, PEiD explores the peak energy index distribution extracted from the channel impulse responses. Our approach effectively captures the inherent randomness of channel properties and significantly reduces the number of samples required for identification, thus surpassing previous methods. Additionally, to tackle the challenge of mobility, a sliding window technique is also adopted to achieve continuous monitoring of transmission path status. According to our extensive experiments, PEiD can attain a best path identification accuracy of 97.5% for line-of-sight scenarios and 94.3% for non-line-of-sight scenarios, with an average delay of under 300 ms (92% accuracy) even in dynamic environments.  
Wifi sensing; line-of-sight identification; machine learning; channel state information +
Footnote †: journal: Applied  
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Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## Materials and Methods
### Methodology
#### 3.2.1 Data Preparation  
The raw data obtained consisted of 5041 aerial images with a resolution of 1920 \(\times\) 1080 (see Figure 3a). Given that the original image had much information, each one is split into four equal parts and the resolution of each picture is 960 \(\times\) 520 (see Figure 3b).  
Approximately 20,000 images were obtained after splitting all the original images. Given that not all images contained information on damaged buildings or ruins, and some were irrelevant to this study, such as images of seas and beaches, these images were initially screened out manually. Lastly, approximately 500 images containing objects to be identified are obtained. Thereafter, 70% of  
Figure 3: Examples of image pre-processing. (**a**) The raw data with resolution of 1920 \(\times\) 1080 has many information; (**b**) The raw data is split into four parts.  
the total number of images were selected as part of the training data set, while the remaining 30% were included in the test data set.
## 1 Introduction
used DEA window analysis with an ideal window width to dynamically assess the technological innovation efficiency of China's high-tech industry [18], and some scholars combined the traditional DEA model with the Malmquist index model to dynamically measure the innovation efficiency of China's high-tech industry [19; 20; 21]. Meanwhile, the traditional DEA model has the problem of the self-assessment mode, and [PERSON], [PERSON] et al. evaluated the efficiency of R&D activities in regional high-tech industries by combining the two-stage DEA model with the cross-DEA model [22]. Most of the models in the above-mentioned studies only focus on improving one or two major issues in the DEA basic model, failing to simultaneously address its multiple core problems. They also do not fully consider factors such as shared input, the impacts of different stages, and the time lag effect, which are of great significance in practical applications. From the above, it can be seen that current research and development should pay more attention to constructing dynamic network cross-efficiency models to make up for these deficiencies and improve the application effect of DEA models in complex environments. Therefore, this paper adopts the panel data of China's high-tech manufacturing industry, divides the innovation activities of the high-tech manufacturing industry into the stage of technology R&D and the stage of result transformation, and, at the same time, takes into account the initial input of the first stage, the additional input of the second stage, the shared input of the two stages, as well as the efficiency of the R&D lag, to construct a two-stage dynamic network cross-efficiency model and conduct an empirical study on the innovation efficiency of the high-tech manufacturing industry of different provinces in China. We construct a two-stage dynamic network cross-efficiency model to empirically investigate the innovation efficiency of high-tech manufacturing industries in different provinces of China and study the innovation efficiency in the technology R&D stage as well as the achievement transformation stage.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 3 System Model
### State and Action Space  
In the multi-agent resource sharing problem, numerous V2V agents navigate the environment. At each timestep, individual agent \(k\) processes observation \(o_{k}\) and takes action \(a_{k}\), thus collectively forming a joint action. The action \(a_{k}\) involves the selection of the power level and optimal sub-band for data transmission.
## 4 Wind Loading and Vibration Mitigation
### Coupled Simulations in CWE  
Wind effects on structures often imply a feedback mechanism, thus rendering arising phenomena as challenging to be investigated. This means that the force deforms the structure, whereas such changes in shape will alter the flow field, leading to a modification of the excitation itself. This is a crucial characteristic in the case of flexible constructions. Numerical methods constitute one of the viable ways to investigate such effects. The main advantage is that corresponding simulations are not affected by scaling as the digital models are created at full (i.e., real) size. Following a _partitioned approach_, dedicated solvers are linked to the wind flow and structure, respectively. Nonetheless, this will lead to particular requirements of corresponding computing power, alongside issues related to the accuracy,  
Figure 8: Spectral assessment of the energy content in the aerodynamic forces: turbulent flow for the \(0^{\circ}\) angle of attack, smooth in case of \(90^{\circ}\) angle of attack.  
Figure 7: The typical metrics for characterizing the flow conditions: turbulent for the \(0^{\circ}\) angle of attack, smooth (i.e., low turbulent) for \(90^{\circ}\) angle of attack.  
robustness and stability of the scheme. Additional details characterize the kind of coupling schemes used, OWC or TWC. This choice is problem-dependent, with potential effects being highlighted by the aerodynamic and elastic studies in [45]. Further generalizing the partitioning, numerical simulations permit the inclusion of additional components, such as AMDs, as shown in Figure 9. Whereas extending a structural model with a TMD would be straightforward by adding additional entries in the mass, damping and stiffness matrices (native to the FEM-formulation of structures), this is not a universal solution. For the particular case of a CFD model for a TSD, this submodule (or component) has to be treated in a dedicated manner. It also needs to be included in the broader computational scheme. The generalized concept follows the developments in [41; 46]. This is a viable approach for investigating the efficiency of TSDs and TMDs alike, at full scale, while capturing all relevant details. It is also possible to use it for general controllers, with the applicability highlighted by [47].  
For our purpose, we set up the TMD and the TSD based on the presented structural properties and following the principles enumerated in Section 2. In Table 2, all crucial parameters are detailed for the weak and strong bending mode, respectively.  
Figure 9: Representation of the coupled wind–structure–damper simulation.
# Synthesis of Mesoporous MWCNT/HKUST-1 Composite for Wastewater Treatment
## 2 Experimental Procedures
### Synthesis and Characterization of MOF and CNT/MOF Nanocomposites  
Porous HKUST-1 MOFs were prepared using the solvothermal method described by [PERSON] et al. [35] and [PERSON] et al. [37] (but slightly modified). In this study, 1.018 g of copper (II) nitrate hemi-penta-hydrate was dissolved in 10 mL of deionized water, and 0.508 g of benzene-1,3,5-tricarboxylic acid (BTC) was dissolved in 10 mL of ethanol. The two solutions were mixed together and stirred thoroughly for 15 min until the H\({}_{3}\)BTC was completely dissolved. The slurry was transferred into a 240-mL Teflon-liner stainless steel autoclave: 2 mL of water was used to rinse the mixture of Cu-salt and H\({}_{3}\)BTC inside the autoclave, and an additional 2 mL of ethanol was added. The autoclave was placed in an oven, and the temperature was kept at 120 \({}^{\circ}\)C for 24 h. Then, the autoclave was cooled down, and the blue crystals were extracted and washed with deionized water and ethanol to remove the unreacted species. The slurry was dried in an oven for 15 h to obtain the HKUST-1 MOF sample.  
In order to prepare the HKUST-1/MWCNT composite material, 0.5 g of functionalized MWCNTs were ultrasonicated in 32 mL of ethanol and vigorously stirred using a magnetic stirrer. Then, 16 mL of water was added ultrasonicated and stirred. The slurry was further ultrasonicated for 17 min and stirred for 3 min. The Cu(NO\({}_{3}\))\({}_{2}\).2.5H\({}_{2}\)O and H\({}_{3}\)BTC were prepared separately in 10 mL of water and 10 mL of ethanol, respectively. The Cu-salt and H\({}_{3}\)BTC ligand was mixed as described above and then mixed with the FMWCNT slurry and stirred. The solution was then ultrasonicated for 10 min and stirred. The slurry was poured inside the autoclave and put in an oven, and the temperature was set at 120 \({}^{\circ}\)C for 24 h. The resulting material was washed with deionized water and ethanol to remove the unreacted species. The slurry was dried in an oven to obtain the MWCNT/HKUST-1 MOF hybrid composites.  
The synthesized MOF and MWCNT/MOF composite samples were characterized with SEM and XRD. The BET surface area was measured using a Micromeritics TriStar II apparatus. Prior to the measurement, the samples were degassed in an ultrahigh purity nitrogen at 90 \({}^{\circ}\)C for 1 h and then for 5 h at 200 \({}^{\circ}\)C. thermogravimetric analysis (TGA) of the samples was carried out using PerkinElmer (Pyris 1 TGA) scanning from 35 to 900 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C/min under air/nitrogen conditions.
## 2 Fundamental Theory
### Features for Fatigue Crack Characterization
#### 2.2.1 Magnitude/Amplitude-Based DIs  
In this study, in order to interpret ultrasonic guided waves features acquired by individual actuator-sensor paths in an active sensor network. A damage index (DI) is established across the entire inspection area, within which fatigue cracks, if there are any, are expected to be visualized by an intuitive DI. Here, the DI is defined with the magnitude/amplitude alteration of a scattering wave caused by the occurrence of a crack at a particular actuator-sensor path in the inspection area, denoting a magnitude/amplitude-based DI.  
\[DI(i,j)_{magnitude\text{-}k\text{-}t} =\frac{u(i,j)_{B\text{-}k}(t)-u(i,j)_{D\text{-}k}(t)}{u(i,j)_{B \text{-}k}(t)} \tag{2}\] \[DI(i,j)_{amplitude\text{-}k\text{-}f} =\frac{u(i,j)_{B\text{-}k}(f)-u(i,j)_{D\text{-}k}(f)}{u(i,j)_{B \text{-}k}(f)} \tag{3}\]  
In Equations (2) and (3), DI(i,j) stands for the DI calculated at the A\({}_{i}\)-S\({}_{j}\) actuator-sensor path, magnitude/amplitude refers to the temporal magnitude/spectrum amplitude features of an ultrasonic guided wave, and subscript k represents the kth (A\({}_{i}\)-S\({}_{j}\)) actuator-sensor path. The t and f refer to the time and frequency domain, respectively. \(u(i,j)_{B\text{-}k}(t)\) and \(u(i,j)_{D\text{-}k}(t)\) exclusively correspond to  
Figure 1: Dispersion curves of ultrasonic guided waves in an SMA490 BW steel plate (**a**) Phase velocity curves; (**b**) Group velocity curves.  
the baseline signal and current signal of the magnitude peaks of the S0 mode, which are acquired by the \(A_{i}\)-S\({}_{j}\) actuator-sensor path in the time domain. Similarly, their corresponding benchmark signal and current signal in the frequency domain are \(u(i,j)_{B\_k}(f)\) and \(u(i,j)_{D\_k}(f)\). \(u(i,j)_{B\_k}(t)-u(i,j)_{D\_k}(t)\) and \(u(i,j)_{B\_k}(f)-u(i,j)_{D\_k}(f)\) are the scatter magnitude/amplitude signal of the S0 mode caused by fatigue cracks in the time and frequency domain. On account of Equations (2) and (3), the defined magnitude/amplitude-based DIs are used for quantitatively characterizing the crack propagation at each path in the inspected engineering structure.
## 2 Reverification of the Multicomponent Hydration Model
### Overview of the Original Model Related to Micro-Pore Structures and Moisture Equilibrium/Transports
Interlayer pores are caught in the layer structures of C-S-H gel and each pore size is similar to that of a water molecule [1; 25]. Internal moisture states are expressed by modeling the thermodynamic equilibrium and hysteretic behavior of isotherms dependent on micro-pore structures. The degree of saturation in gel or capillary pores is calculated based on both condensed and absorbed water according to the Kelvin equation and the modified Brunauer-Emmett-Teller (B.E.T) theory proposed by [PERSON] [26], while considering ink-bottle effects of condensed water during the drying-wetting process. That one in the monotonic drying process is formulated in Equation (6). \[S=S_{c}+S_{ads}+k\cdot S_{ink}\ (0\leq k\leq 1) \tag{6}\]  
where \(S\) is the degree of saturation with condensed water and absorbed water, \(S_{c}\) is the degree of saturation with moisture present in pores below radius \(r_{c}\), with which the equilibrated interface of liquid and vapor is created, \(S_{ads}\) is the degree of saturation due to absorbed water, \(S_{ink}\) is the degree of saturation with trapped water due to the ink-bottle effect and \(k\) is a parameter considering the time-dependent dispersion of ink-bottle water. \(k\) takes the value of 1.0 immediately after the start of the drying process and decreases with the progress of drying up to zero on the wetting curve as shown in Figure 14. Herein, the rate of moisture transfer (_dk/dt_) is modeled as a function including the environmental temperature and humidity [1; 24]. On the other hand, the moisture isotherm of interlayer water is expressed by the envelope curve as shown in Figure 15. Once an environmental condition is changed, the initial equilibrium point is moved toward the revised one defined by the environmental temperature and humidity with the rate depending on both conditions [1; 24]. In this way, the thermodynamic moisture states in micro-pore structures are closely linked with the progress of hydration and moisture equilibrium/transports. Figure 14: Enhanced moisture isotherm model considering the time-dependent dispersion of ink-bottle water [1; 24].
## 3 Problem Definition
### Environmental Conditions  
The reference environmental conditions used in this paper were also extracted from IEA-15-240-RWT documentation [37]. Wind speed is defined as a Weibull distribution with a shape factor of 2.12. Sea states are characterized by significant wave heights, the peak spectral wave period, and the wave direction. Wave heights and peak spectral periods are representative of the east coast of the USA for a 30 m water depth site. Extreme Sea States (ESSs) and Extreme Wave Heights (EWHs) are calculated following DNVGL-ST-0437 [4], with four categories, which are defined based on the height and frequency of the waves during certain periods. ESS, 1-year and EWH, 1-year refer to extreme waves within a return period of 1 year, where ESS, 1-year is based on the average significant height and EWH, 1-year is based on the maximum recorded height. ESS, 50-years and EWH, 50-years focus on a 50-year period, where ESS, 50-years is based on the average significant height and  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Turbine class & IEC Class 1B \\ Rating & 15 MW \\ Cut-in wind speed (\(U_{\text{in}}\)) & 3 m/s \\ Rated wind speed (\(U_{\text{R}}\)) & 10.59 m/s \\ Cut-out wind speed (\(U_{\text{out}}\)) & 25 m/s \\ Rotor diameter (\(D\)) & 240 m \\ Hub height (\(H\)) & 150 m \\ RNA mass & 1017 ton \\ Tower mass & 860 ton \\ Tower top diameter & 6.5 m \\ Tower base diameter & 10 m \\ Tower top thickness & 24 mm \\ Tower base thickness & 36.5 mm \\ Water depth (\(W\)) & 30 m \\ Pile diameter (\(d\)) & 10 m \\ Pile thickness & 55.3 mm \\ Pile depth (\(L\)) & 45 m \\ Transition piece height & 15 m \\ Monopile mass & 1318 ton \\ Monopile aspect ratio (\(L/d\)) & 4.5 \\ \hline \end{tabular}
\end{table}
Table 1: Key parameters of the IEA-15-240-RWT OWT. Extracted from [37].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Soil profile & Single layer \\ Type of soil & Dense sand or gravel \\ Poisson’s ratio & 0.4 \\ Density & 2000 kg/m\({}^{3}\) \\ Shear modulus & 140 MPa \\ Shear wave velocity & 264.5 m/s \\ Damping ratio & 0.05 \\ \hline \end{tabular}
\end{table}
Table 3: Properties of the soil deposit. Extracted from [37].  
EWH, 50-years is based on the maximum height. Significant wave heights for each return period are defined in [37], and the corresponding extreme wave heights are calculated as follows:  
\[H_{\text{EWH,}T_{\text{R}}}=\left(\sqrt{\frac{1}{2}\left(\frac{3\cdot 3600}{T_{ \text{wave}}}\right)}+\frac{0.2886}{\sqrt{2\,ln\left(\frac{3\cdot 3600}{T_{\text{wave}}} \right)}}\right)\,H_{\text{ESS,}T_{\text{R}}} \tag{10}\]  
where \(T_{\text{R}}\) is the return period considered (1 yr or 50 yr), and \(T_{\text{wave}}\) is the significant wave period. The values considered in this study are shown in Table 4. The wave direction is considered to be aligned with the wind direction (fore-aft direction) in all cases. Finally, the OWT reference documentation does not provide information about current data for the site. Due to this lack of data, the current loads are not considered in this study.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 4 Discussion and Conclusions
### Limitations of Voice Mapping  
A basic prerequisite for making a voice map at all is that the _phonation must be periodic_ enough for a fundamental frequency to be estimated. This would seem to run at odds with the fact that severely pathological voices are often characterized by low or even absent periodicity. There will of course be cases in which it is not possible to make a map, and then it is not relevant to do so, either. Once a minimum range of phonation has been regained, however, mapping can document its extent and its character. As yet, we have had too little experience of mapping severely pathological voices to say more about this.  
Often, when first encountering voice maps, clinicians will ask, \"well that's all very nice, but _how do I get a number_ from all these visualizations?\" That will depend entirely on the research question; and with rich visual and auditory feedback of a given phenomenon or relationship, one is in a much better position to pose the right research questions. A voice map is made entirely of numbers, arranged so as to supply the pixel values in an image. As the radiologist examines a scanned image of some body part, so the voice clinician canexamine and interpret the images from the voice map, and this of course takes experience. With increased experience, it will be possible to design numerical scores that summarize some aspects of maps that are especially relevant in a particular clinical or pedagogical context. However, this remains to be seen.  
Making a voice map necessarily _takes more time_ than sustaining a few vowels. For several kinds of clinical assessment, making a full voice map would certainly be overkill. It will be important to develop protocols that are expedient, yet still yield enough data to resolve the intervention effects in the face of the copious variability in the voice. This work will continue, and is likely to require separate approaches for different pathologies, for instance.
## 3 Results and Discussion
### Thermal Activation Process Test Results  
#### 3.3.1 Effect of Different Calcination Temperatures on the Activity Index  
As shown in Figure 23, the activity index of the uncalcined volcanic rock powder (25 \({}^{\circ}\)C) remained between 53% and 58% from 3 to 90 days. At calcination temperatures from 300 \({}^{\circ}\)C to 700 \({}^{\circ}\)C, the activity index of the volcanic rock powder increased significantly,  
Figure 22: SEM patterns of H-1 (**a,b**) and HS-4 (**c,d**) at 7 d and 28 d.  
reaching a maximum value of 80.4% at 600 \({}^{\circ}\)C at 28 days, which is much higher than that of the uncalcined group.  
According to the Chinese standard JG/T315-2011 [2], the activity index at 28 days should be \(\geq\)65%. The calcined volcanic rock powder exceeded this standard within the specified temperature range. However, when the temperature was increased to between 800 \({}^{\circ}\)C and 1000 \({}^{\circ}\)C, the activity index did not continue to rise, showing little variation compared to the uncalcined group. Upon reaching a calcination temperature of 1200 \({}^{\circ}\)C, the activity index, at all ages, was lower than that of the uncalcined group. Specifically, the activity index at 28 days, across the temperature range from 800 \({}^{\circ}\)C to 1200 \({}^{\circ}\)C, was below 65%.  
As shown in the TG-DTA curve in Figure 24, it was observed from the TG curve that the thermal weight loss process of volcanic rock powder was divided into two stages: the first stage was between 25 \({}^{\circ}\)C and 300 \({}^{\circ}\)C. Due to the evaporation of adsorbed water, the mass decreased by about 1.1%. The second stage was between 300 \({}^{\circ}\)C and 1200 \({}^{\circ}\)C, and the mass loss was about 6.5%. The mass loss at this stage was mainly due to the decomposition of carbonates, the precipitation of active oxides, and the disappearance of structural water caused by the dehydroxylation reaction of minerals [65]. It could be seen from the DTA curve that the first endothermic valley was located at 135 \({}^{\circ}\)C when the mineral components desorbed adsorbed water. The second endothermic valley was located at 500 \({}^{\circ}\)C, during which the mineral components underwent a dehydroxylation reaction and the layered structure gradually was destroyed. The dehydroxylation reaction was basically completed at 700 \({}^{\circ}\)C, and volcanic rock powder was most active in this temperature range. The third endothermic valley was located at 900 \({}^{\circ}\)C, and the overall activity of volcanic rock powder decreased.  
Figure 23: Relationship curve of activity index with temperature at different ages.  
Figure 24: Volcanic rock powder TG-DTA curve.