esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Plant Material  
The edible mushrooms included in this investigation were purchased in the \"tianguis\" (local markets) of Atempan and Zaragoza, Puebla, located at 19\({}^{\circ}\)50\({}^{\prime}\)17\({}^{\prime\prime}\) N 97\({}^{\circ}\)27\({}^{\prime}\)23\({}^{\prime\prime}\) O (2049 masl) and 19\({}^{\circ}\)46\({}^{\prime}\)15\({}^{\prime\prime}\) N 97\({}^{\circ}\)33\({}^{\prime}\)18\({}^{\prime\prime}\) O (2300 masl), respectively. Fruiting bodies from seven different fungal morphologies (five kilograms each) were transported to the laboratory in paper bags and superficially washed with sterile distilled water and dried with sterile wipes for subsequent analysis and/or lyophilization. For this purpose, a FreeZone 2.5 L - 84C Benchtop Freeze Dryer--Laboconco (Kansas, MO, USA) was used. Mushroom identification was obtained by the mycologist [PERSON] at the medicinal mushroom collection of the Northern Highlands Association-Mexico through basic dichotomous keys [6; 7; 8]. The molecular identity of the macromycetes was endorsed by sequencing the internal transcribed spacer (ITS) of the 18S ribosomal gene as described by [PERSON] et al. [9]. Amplicons were sequenced employing the commercial services of Macrogen Inc. (Seoul, Republic of Korea) and the LANBAMA from IPICYT-Mexico. The resulting sequences were validated by BLASTn software version 2.2.24 and deposited in the nucleotide database of the National Center for Biotechnology Information (NCBI). The local name and scientific name supported by voucher code, as well as the NCBI accessions for each fungal isolate, are described in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Local Name** & **Scientific Name** & **Voucher** & **1 ITS Accession** \\ \hline Tzenzon & _Infundibularye spaumulosa_ & NM-0981 & OR656586 \\ Tecmate & _Amantia jacksonii_ & NM-0982 & OR663657 \\ Ilimorado & _Lepista nuda_ & NM-0983 & OR631737 \\ Borrego & _Russula delica_ & NM-0984 & OR659898 \\ Xochinacatl & _Russula brevipes_ & NM-0985 & OR659899 \\ Quesquec & _Lactarius indigo_ & NM-0986 & OR631739 \\ Talistac & _Clitocyhe subclavipes_ & NM-0986 & OR635626 \\ \hline \end{tabular} \({}^{1}\) Partial sequence of the Internal Transcribed Spacer of the small-subunit ribosomal RNA gene (rRNA) deposited at the NCBI nucleotide data base.  
\end{table}
Table 1: Some edible mushrooms eaten in the northeastern highlands of Puebla, Mexico.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 3 The Taylor Method
### Synthesizing Antenna Arrays Using Taylor One-Parameter Distribution
#### 3.1.3 Calculate Amplitude Weights  
The amplitude weights \(w_{n}\) for an antenna array using the [PERSON] one-parameter distribution are calculated as follows:Use the formula \(w_{n}(\alpha)=\frac{l_{0}(\alpha\sqrt{1-\rho^{2}})}{l_{0}(\alpha)}\) to calculate the amplitude weighting coefficients for each element of the array.
* \(\rho\) depends on the array geometry and the angle of radiation.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### High-Resolution AGB Maps from Field Measurements and LiDAR Data  
#### 3.3.1 LiDAR-Derived Estimates of Aboveground Biomass at Four Forested Sites, USA  
[PERSON] et al. [78] generated high-resolution biomass maps at four forest sites in the US (Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire) at 20-50 m resolution for the nominal year 2011, by combining in situ, airborne and satellite observations with various statistical models. Among these forest biomass maps, only the Maryland map was accompanied with an uncertainty map. The error bounds (5% and 95% quantiles), as well as high biomass values were predicted using the quantile random forest approach.
# High-Precision Chip Detection Using YOLO-Based Methods
## 2 Materials and Methods
### Model Architecture
#### 2.1.2 Ghost Module
Moreover, the computational cost ratio \(r_{s}\) and parameter ratio \(r_{c}\) between standard convolution and ghost convolution are expressed as in Equations (4) and (5), respectively:  
\[r_{s}=\frac{n\cdot h^{\prime}\cdot w^{\prime}\cdot c\cdot k\cdot k}{\frac{n}{s }\cdot h^{\prime}\cdot w^{\prime}\cdot c\cdot k\cdot k+(s-1)\cdot\frac{n}{s} \cdot h^{\prime}\cdot w^{\prime}\cdot d\cdot d}=\frac{c\cdot k\cdot k}{\frac{s }{s}\cdot c\cdot k\cdot k+\frac{s-1}{s}\cdot d\cdot d}\approx\frac{s\cdot c}{s +c-1}\approx s \tag{4}\]  
\[r_{c}=\frac{n\cdot c\cdot k\cdot k}{\frac{n}{s}\cdot c\cdot k\cdot k+(s-1) \cdot\frac{n}{s}\cdot d\cdot d}\approx\frac{s\cdot c}{s+c-1}\approx s \tag{5}\]  
As revealed in Equations (4) and (5), the parameter \(s\) determines the degree of model compression: the tensor size of intrinsic feature maps is compressed to \(\frac{1}{s}\) compared with that of standard convolution modules, since the magnitudes of d and k are similar and s \(\ll\) c.  
From Equations (2) and (3), it can be observed that the ghost convolution incorporates two hyperparameters: the kernel size (\(d\times d\)) and the number of ghost features generated from a single intrinsic feature map \(s\).
## 4 Results and Discussion
### Feature Contribution Based on the Catboost Feature Importances
If the Rehu had a moderate value, a static forest fire risk map might be generated since the prediction was mostly determined by Elevation, Roaddens, and Poptens. The interaction between the Rehu and the Roaddens changed from the Rehu feature value of 50. When the Rehu was less than 50, it had a larger positive SHAP value with the low Roaddens. This is because if the Rehu was low and the road density was high, both features have the same direction to high forest fire risk, which leads to the relative reduction of the Rehu's contribution. However, if the Roaddens was low despite the outbreak of a forest fire, the contribution of low Rehu would increase. This means that the contribution to the prediction varies depending on the feature value and the interaction between features. Figure 5: **(a)** is the summary plot of SHAP values in Catboost machine learning from the integrated model. The horizontal position means the impact of a feature value for prediction. The thickness and color indicate the sample size and original value for each feature, respectively. The feature with a large impact on prediction is located at the top. **(b)** is the SHAP value dependence plot between Rehu and Roaddens. The X-axis is the feature value of Rehu. The Y-axis is the SHAP value for Rehu. The color means the feature value of Roaddens.
## 3 SSMA Broadcast Protocol  
A proposed protocol and its variations are described formally, starting with the explanation of protocol structure, and followed by analysis on key protocol parameters. Tree construction and time synchronization are not explained in this paper since they are detailed in the data collection protocol named SSMA [1]. Note that SSMAb is always used together with SSMA. One more thing to note is that tree construction and global time synchronization are performed one time at initialization time and maintained locally using control messages such as request to send (RTS), clear to send (CTS), and acknowledgement (ACK), which are used for reliable data transmission in SSMA. Thus, overhead is minimal.
# Improvement of Boeing Bump Method Considering Aircraft Vibration Superposition Effect
## 2 Boeing Bump Method  
Figure 1 presents the classification standards for the Boeing Bump [9]. The horizontal coordinate is the bump length, which is defined as the shortest distance from the highest point of the bump to both ends. In the most unfavorable case, the highest bump is located precisely at the midpoint between both ends. In this case, the bump length can be regarded as a half wavelength. The range of the bump wavelength is from 0 to 120 m. The black curve is the upper limit of the acceptable zone, and the red curve is the upper limit of the excessive zone.  
The fitting functions for the two curves shown in Figure 1 are expressed as Equations (1) and (2), respectively.  
The upper limit of the acceptable zone:  
\[H=\left\{\begin{array}{ll}17.13+8.01\times L-0.31\times L^{2}+0.055\times L ^{3}&0\leq L\leq 20\\ 64+1.6\times L&20<L\leq 60\end{array}\right. \tag{1}\]  
Figure 1: Classification standards of the Boeing Bump.  
The upper limit of the excessive zone:  
\[H=\left\{\begin{array}{ll}27.47+14.33\times L-1.88\times L^{2}+0.13\times L^{3}& 0\leq L\leq 5\\ 27.59+10.86\times L-0.53\times L^{2}+0.01\times L^{3}&5<L\leq 20\\ 77.75+2.04\times L&20<L\leq 60\end{array}\right. \tag{2}\]  
where \(H\) is bump height (mm) and \(L\) is bump length (m).  
As an evaluation index for runway surface profiles, the Boe Bump sets the height limit of a single bump without taking into account the superposition of an aircraft's dynamic responses to multiple continuous bumps. Therefore, the current standard may be too limited to be applied to real situations. In addition, under different conditions, the corresponding responses of various aircraft also will be different from those of the B727, which is the reference aircraft for Boeing Bump standard formulations [9]
## 4 Results and Discussions  
### Characterization of Polypropolee Nanocomposite  
The study employed several advanced analytical techniques to characterize the materials thoroughly. These included X-ray diffraction (XRD, Model: X-Pert Pro) for analyzing the crystallographic structure, phase composition, and orientation of the filler material; Field Emission Scanning Electron Microscopy (FESEM, Model: ISM 7100) for examining the surface morphology and composition of the GNP material for investigating molecular structures, chemical compositions, and defects; Transmission Electron Microscopy (TEM, Model: JEM-2100F) for detailed nanoscale morphology and grain boundary analysis; and Thermogravimetric Analysis (TGA, Model: SHIMADZU TGA-50 & DSC-60) for studying the thermal stability and decomposition behavior.
# Mechanism of Stratum Instability and Dynamic Deformation under Discontinuous Boundary Conditions
## 3 Model of Relationship between Fault Plane and Stratum Movement Line  
The range of stratum movement angles in a stope is 60-80\({}^{\circ}\)[32]. When the dip of the fault plane is the same as that of the stratum movement line and the dip angle of the fault is less than 60\({}^{\circ}\), the stratum movement line will gradually approach the fault plane. The adjacent rock strata with similar mechanical properties and similar rock movement angles are grouped. Therefore, the consistent stratum movement angle of the same group can be approximated. The model of the fault plane and rock movement line is shown in Figure 4. The moving line coefficient of rock strata in different groups is as follows:  
\[K_{1}=\frac{h_{11}k_{11}+h_{12}k_{12}+\dots+h_{1n}k_{1n}}{h_{11}+h_{12}+\dots+ h_{1n}} \tag{5}\]  
\[K_{n}=\frac{h_{n1}k_{n1}+h_{n2}k_{n2}+\dots+h_{nn}k_{nn}}{h_{n1}+h_{n2}+\dots+ h_{nn}} \tag{6}\]  
\[k_{nn}=\tan\beta_{nn} \tag{7}\]  
where \(h_{nn}\) is the thickness of the nth rock layer in the nth group, in m; \(k_{nn}\) is the nth stratum movement line coefficient in the nth group; and \(\beta_{nn}\) is the movement angle of different strata in different groups, in \({}^{\circ}\).  
The stope rock movement line function is as follows:  
\[f(x)=\left\{\begin{array}{l}K_{1}(x-x_{0})\Big{(}x_{0}\leq x\leq x_{0}+\frac {h_{1}}{K_{1}}\Big{)}\\ K_{2}x+(H_{1}-K_{1}x_{1})\Big{(}x_{0}+\frac{h_{1}}{K_{1}}\leq x\leq x_{0}+\frac {h_{1}+h_{2}}{K_{2}}\Big{)}\\ K_{n}x+(H_{n-1}-K_{n
## 2 Materials and Methods
### PBM Parameters  
The patients in the PG were subjected to five sessions of intra-oral PBM per week (Monday to Friday), which started one day before the conditioning regimen, and continued until the 10 th day after transplantation (D+10). The laser device that was used was a double diode laser (LumixZ\({}^{\otimes}\); FISIOLINE, Verduno, Cuneo, Italy), which emits two wavelengths simultaneously at 650 nm and at 904-910 nm. The technical features of this device are as follows: the visible red source (650 nm) is operated in continuous mode (CW) with an average output power (at the source) of 100 mW. The infrared GaAs source can be operated in pulsed and super-pulsed emission mode with peak power at aperture 45 W, and a pulse duration of 200 ns.  
Before starting each PBM session, the device was cleaned and the hand-piece was covered with a plastic wrap (to avoid direct contact with patients). Appropriate safety glasses were used by both the operator and the patient. All the PBM sessions were performed by the same laser operator to avoid intraoperative bio-stimulation variations.  
Ten points on the at-risk mucosal surfaces were irradiated in each session, including buccal mucosa, tip, ventral and marginal surface of the tongue, floor of the mouth, soft palate, and inner surfaces of the lip.  
The laser device was programmed manually and the 650 nm wavelength was switched off. The only used wavelengths were the 904-910 nm and the laser light guide red 650 nm (for real visualization of the infrared beam application area). The laser was applied point by point in a defocused (non-contact) mode at a distance of \(\sim\)1 cm from the mucosal tissues. The parameters that were used for each point were as follows: energy of 4 J, power of 88.9 mW, fluence of 8 J/cm\({}^{2}\), frequency of 13 kHz, and application time of 45 s. The beam diameter was 8 mm, which is usually obtained when the handpiece lens is away from the targeted tissue of about 1 cm, and the calculated point area was \(\sim\)0.5 cm\({}^{2}\). The total energy per session was 40 J. Before the start of each session, a laser output test was performed by an optical sensor integrated into the device. The PBM parameters are reported in Table 2 as it is recommended in the literature to provide them in table [30].  
In case of the development of OM, the laser sessions continued (5 sessions per week) until the complete resolution of OM, even after the 10 th day after transplantation (D+10). The laser was applied (point by point) to cover all the ulcerated areas in a defocused (non-contact) mode at a distance of \(\sim\)1 cm from the mucosal tissues with the following parameters per point: energy of 6 J, power of 90.9 mW, fluence of 12 J/cm\({}^{2}\), frequency of 13 kHz, application time of 66 s, beam diameter of 8 mm, and point area of \(\sim\)0.5 cm\({}^{2}\).
## 5 Numerical Results and Discussion
### Performance Analysis
#### 5.1.1 Performance Analysis of PU1 Packets  
Figure 1 depicts the trends of PU1 packets' blocking rate \(B_{P1}\) and throughput \(T_{P1}\) under the proposed communication failure and repair mechanism.  
As shown in Figure 1, when the communication failure rate \(f\) increases, PU1 packets' blocking rate \(B_{P1}\) increases while throughput \(T_{P1}\) decreases. When the repair rate \(r\) increases, \(B_{P1}\) decreases while \(T_{P1}\) increases. When \(f\) increases, the possibility of the system state being failure increases, so the time PU1 packets are present in the channel increases due to the low-speed transmission of PU1 packets in the failure state. Therefore, the probability of newly arrived PU1 packets being blocked increases, and the number of transmitted PU1 packets decreases in unit time correspondingly. When \(r\) increases, the possibility of the system maintaining a normal state increases, the probability of PU1 packets being transmitted at a high speed increases, which reduces the transmission time, and the chance of the channel being idle to meet those newly arrived PU1 packets increases; therefore, less blocking occurs and the number of transmitted PU1 packets increases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Fixed Values** \\ \hline SU packets’ arrival rate \(\alpha_{2}\) & 0.3 \\ PU1 packets’ service rate \(\beta_{11}\) when the system state is normal & 0.7 \\ PU2 packets’ service rate \(\beta_{12}\) when the system state is normal & 0.6 \\ SU packets’ service rate \(\beta_{2}\) when the system state is normal & 0.5 \\ PU1 packets’ service rate \(\epsilon_{11}\) when the system is in a failure state & 0.4 \\ PU2 packets’ service rate \(\epsilon_{12}\) when the system is in a failure state & 0.3 \\ The size \(k\) of the cache that holds SU packets & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters with fixed values in the performance analysis experiments.  
Figure 1: The performance change trends of PU1 packets: (**a**) the change trend in \(B_{P1}\); (**b**) the change trend in \(T_{P1}\).  
From Figure 1, we can find that as PU1 packets' arrival rate \(\alpha_{11}\) increases, PU1 packets' blocking rate \(B_{P1}\) and throughput \(T_{P1}\) increases simultaneously. As PU2 packets' arrival rate \(\alpha_{12}\) increases, \(B_{P1}\) increases, and \(T_{P1}\) decreases. Considering the priority of PU1 packets is higher, as \(\alpha_{11}\) increases, the probability that PU1 packets are present in the channel increases. Therefore, the amount of PU1 packets with completed transmission rises. Meanwhile, there can only be at most one packet in the channel, so the probability of PU1 packets being blocked also increases. When \(\alpha_{12}\) increases, the probability of PU2 packets entering the channel increases. However, in the system's normal state, when PU2 packets are being transmitted in the channel, PU1 packets cannot t preempt the channel, so the probability that PU1 packets are blocked increases, and the number of PU1 packets with completed transmissions decreases accordingly.
# Shallow Velocity Estimation by Multiples for Monochannel Boomer Surveys
## 1 Introduction  
Offshore engineering requires estimating several lithological parameters of the shallow geological formations just below the seafloor. The stability of platforms for oil and gas exploration and production imposes avoiding shallow gas deposits. To achieve this goal, standard practice entails the execution of a high-resolution seismic survey by a multichannel streamer [1, 2] before the platform setting. This mature technology is effective but very expensive, so it can be rarely used for offshore constructions such as piers and breakwaters. Furthermore, the ultimate available technology (e.g., vessels with long multiple streamers) may considerably impact the marine environment or may not be able to operate in busy ports or across offshore infrastructures. The technology of monochannel systems is far simpler, requiring a cable that may be a dozen of meters long, and a source, e.g., a Boomer [3] that is much weaker than the air guns used for hydrocarbon exploration. In this way, the impact on marine life and the environment is minimized [4, 5]. Lower source energy results in the lower penetration of seismic waves; nevertheless, the obtained images in the time domain still allow building useful Earth models for engineering purposes (see also [6, 7, 8, 9, 10], etc.).  
Monochannel systems cannot estimate accurately thickness and P velocity of the rock layers using their reflecting interfaces. This is a major drawback. The time interval \(\Delta t\) between two reflections may be due to any combination of values \(\Delta z\) for the thickness and \(v\) for the average velocity within the corresponding layer  
\[\Delta t=(\lambda\ \Delta z)/(\lambda\ v)=\Delta z/v \tag{1}\]  
for any \(\lambda\) number. Thus, just one measurement cannot solve this ambiguity. This problem is solved nicely by multichannel systems by exploiting multiple offsets, by a simple normal moveout correction [11], or by the ultimate technology, i.e., a full-waveform inversion(see [12], etc.). In this paper, we propose an alternative way to cope with this ambiguity--namely, adding an equation by the travel time of a peg-leg multiple reflection.
## 2 Methodology
### Eligibility Criteria  
All included articles in this literature were selected based on the following aspects:  
* The degree of relevance to the context of communication networking for smart city applications: The selected articles are research and survey works that consider communication networks for smart cities. This included physical, network, and application layers protocol design and algorithms.
* The data of the publications: All included articles were published over the past five years. More than 80% of the considered works were published in the past two years.
* The rank of the journal or conference: All selected articles are conference or journal articles. Also, technical papers of well-known organizations in the field of ICT, including 3 GPP and ITU, were considered. The selected conference papers are of ranked conferences in the Scopus database, and the included journal articles are of high-ranked journals in both Scopus and Web of Science (WoS) databases. Most of the selected journal articles were published in a first- or second-quartile (Q1 or Q2) journal, as indexed by the WoS 2023 report.
* The articles should cover all the topics of the survey: Mainly, the articles cover the topics of smart city applications, specifications of communication networks of smart cities, and challenges in designing communications networks for smart city applications. Moreover, the articles covered the considered key enabling technologies of smart cities, including 5G, AI, IoT, blockchain, edge computing, AI, virtualization, and SDN. Figure 2 summarizes the main topics of the considered articles.  
Figure 1: Number of the selected and excluded articles during the main three phases of the work.  
Figure 2: Main topics covered in this work.  
During the search process, we used different filters of the search engines of the mentioned databases with certain keywords and strings. Also, Boolean operators were considered to include or exclude some terms to refine the search process. Table 1 highlights the keywords and index terms considered for the Internet paper searches. These terms were grouped into three main categories: smart city, key technologies, and applied research. The first group contained the index terms for gathering data related to smart city background, features, applications, specifications, and challenges. The second group considered the key terms used for gathering data related to key enabling technologies of smart cities that were considered in the work. The third group contained the keywords used to collect data about existing smart city proposals and studies.