esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Glycemic Index of SPI Noodles
In another study, fortification of gluten-free sweet potato flour spaghetti was attempted using whey protein concentrate (WPC) and chickpea flour (CPF) with an aim to increase the protein content and decrease the glycemic index. The results revealed that the protein content was increased by 192.20% and 150.08% for spaghetti fortified with WPC and CPF, respectively, compared to the control. The decrease in glycemic index was also observed with 15% CPF (59.43) and 15% WPC (58.73) in comparison to control (63.92) [53]. Similarly, increased protein content (14.5%) and decreased average glycemic index (39.19) were observed for wheat flour bread fortified with 20% soy flour [54]. Comparison with other reported studies indicated that the results of the present study were in line with those in the literature. A decrease in GI may aid in blood sugar stabilization, lower the risk of diabetes, high blood pressure, and cardiovascular disease, and boost high-density lipoprotein levels. Healthier foods with a low GI and high protein content are suitable for various groups such as diabetic and obese elderly patients.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver
### Proposed Synchronization Algorithm
#### 3.3.5 Step 5--Fractional STO Estimation  
To achieve an accurate receiver alignment, it is necessary to compensate the fractional STO \(\lambda\). Indeed, once the fractional CFO and the DR are estimated, the latter are compensated in the unmodulated up-chirps of the preamble to allow the receiver to perform the estimation of \(\lambda\). To this end, we propose to compute the following FFT, denoted \(R_{c_{d},\
u}[k,p]\), in the \(p^{th}\)\(T\)-long sequence of the preamble after the compensation of \(\hat{v}\) and \(\hat{c}_{d}\):  
\[R_{c_{d},\
u}[k,p]=\frac{1}{\sqrt{M}}\sum_{n=0}^{M-1}\Bigl{(}r_{c_{d}}(n,p)x_{ ref}^{*}(n)e^{-j2\pi\frac{M\hat{v}}{M}}\Bigr{)}e^{-j2\pi\frac{M\hat{v}}{M}} \tag{32}\]  
We notice in (32) that residual errors in estimating the values of \(\
u\) and \(c_{d}\) would impact the estimation process of \(\lambda\).  
To compute the fractional STO in the latter \(T\)-long sequence, an interpolation method can be used to find a finest estimation of the frequency that maximizes \(|R_{c_{d},\
u}[k,p]|\). We note \(\hat{\lambda}_{p}\) this estimate. Once again, and to be compliant with the low power constraints of LPWAN, we propose a low complexity method to accurately find \(\hat{\lambda}_{p}\). This method is based on the maximization of concave functions by dichotomy. It should be noted that LPWANs attempt to offload complexity from the end devices, as terrestrial gateways, do not have power consumption constraints. However, when implementing onboard decoding on the satellite, the complexity of the algorithms must be taken into account.  
To obtain a precise estimation of \(\lambda\), an averaging over the preamble up-chirps is performed as follows:  
\[\hat{\lambda}=\frac{1}{N_{p}}\sum_{p=1}^{N_{p}}\hat{\lambda}_{p} \tag{33}\]
## 2 System Model
### Local Spectrum Sensing  
The considered a CR network that is assumed to be composed of \(N\) SUs. Each SU performs spectrum sensing independently by using an energy detection method and then sends the outcome to the DC. The hypothesis test statistics for local spectrum sensing at SU \(i\) can be formulated as follows [35]:  
\[\left\{\begin{array}{ll}P:&x_{i}\left(t\right)=h_{i}s\left(t\right)+w_{i} \left(t\right),\\ A:&x_{i}\left(t\right)=w_{i}\left(t\right),\end{array}\right.\forall i\in\{1,2,\ldots,N\} \tag{6}\]  
where \(x_{i}(t)\) is the received signal by the \(i\)th SU in time slot \(t\), \(h_{i}\) denotes the channel gain of the link between the PU and the \(i\)th SU, \(s(t)\) denotes the PU signal, and \(w_{i}(t)\) is zero mean and unit variance AWGN. Regarding energy detection, the observed energy at the \(i\)th SU is expressed as follows [36]:  
\[xE_{i}=\sum\limits_{j=1}^{M_{i}}|x_{i}\left(j\right)|^{2},\forall i\in\{1,2, \ldots,N\} \tag{7}\]  
where \(x_{i}\left(j\right)\) is the \(j\)th sample of the received PU signal at the \(i\)th SU and \(M_{i}\) is the number of sensing samples during each sensing period. For simplicity, we assume that the number of sensing samples collected by each SU is the same for all the SUs. When \(M_{i}\) is relatively large (e.g., \(M_{i}>200\)),can be approximated as a Gaussian random variable under the two hypotheses (\(P\) and \(A\)) with mean \(\mu_{P},\mu_{A}\) and variance \(\sigma_{P}^{2},\sigma_{A}^{2}\) given as follows [37]:  
\[xE_{i}\sim\left\{\begin{array}{ll}\mathcal{N}\left(\mu_{P}=M_{i}\left(1+\gamma _{i}\right),\sigma_{P}^{2}=2M_{i}\left(1+2\gamma_{i}\right)\right),&P,\\ \mathcal{N}\left(\mu_{A}=M_{i},\sigma_{A}^{2}=2M_{i}\right),&A,\end{array} \forall i\in\left\{1,2,\ldots,N\right\}\right. \tag{8}\]  
where \(\gamma_{i}\) is the signal-to-noise ratio (SNR) of the sensing channel between the PU and the SU. The decision about the state of the PU can be made as follows:  
\[D_{i}\left(t\right)=\left\{\begin{array}{ll}1,&\text{if }xE_{i}\left(t \right)\geq\lambda_{i},\\ 0,&\text{otherwise},\end{array}\right. \tag{9}\]  
where 1 and 0 are single-bit data that correspond to the states \(P\) and \(A\) of the PU, respectively; and \(\lambda_{i}\) is a predefined decision energy threshold.
## 4 Results and Discussion
### Model Verification
#### 4.1.1 Single borehole  
For the single borehole problem, the temperature distribution over the \(x\)-coordinate over four seasons are plotted in Figure 5. The analytical results have an excellent agreement with the numerical data; in particular, the mean absolute deviation (MAD) between the analytical and numerical temperatures (\(T_{analytic}\) and \(T_{numeric}\)) over the \(x\)-coordinate with a set of \(m\) number of data points can be expressed as:  
\[MAD=\frac{1}{m}\sum_{i=1}^{m}\bigg{|}T_{analytic,i}-T_{numeric,i}\bigg{|}. \tag{18}\]  
Based on the four seasons computed in Figure 5, the MAD is found to be minimal falling within the range of 0.0428% to 0.4677%. Meanwhile, the changes in each season are captured, which implies the effect of the time-dependent heat flux. Since the undisturbed temperature (\(T_{0}\)) is at 10 \({}^{\circ}\)C, the temperature above and below \(T_{0}\) near the borehole represents the heat supply and extraction over the seasons. In other words, the charging and discharging processes are illustrated in solar-geothermal energy systems. Such temperature differential can convert into energy quantitatively, thus greatly benefiting the economic potential of geothermal systems.
## 2 Hybrid PLC and VLC Channel Based on Distinct Optical Beam Configurations  
To a large extent, the gain and coverage characteristics of hybrid PLC and VLC channels are dominated by the optical beam pattern of the adopted light-emitting diodes (LEDs) source in the envisioned transmitter. Objectively, the distinct beam radiation patterns open one unique design and one observation dimension for the enhancement of hybrid PLC and VLC performance characteristics.  
In this section, we will comparatively investigate the channel model of integrated PLC and VLC based on distinct optical beam configurations.
# Distributed Graph Diameter Approximation
## 1 Introduction
### Related Work
#### 1.1.2 Approximation Algorithms
However, the algorithm features a high approximation factor and might suffer from a long critical path if implemented in a distributed environment. Also, no analytical guarantees on the approximation factor are known in the presence of weights. A similar diameter-approximation strategy could be devised by using the parallel clusterings presented in [17; 18]. However, these strategies apply again only to unweighted graphs and, moreover, they aim at minimizing the number of inter-cluster edges but cannot provide tight guarantees on the clustering radius, which in turn could result in poor approximations to the diameter. In fact, in this work we adopt a similar approach but devise a novel distributed clustering strategy which is able to circumvent all of the aforementioned limitations. Clustering is also used to approximate shortest paths between nodes, which can indirectly be used to approximate the diameter. In particular, [PERSON] [19] presents a PRAM algorithm which uses clustering to approximate shortest-path distances. For sparse graphs with \(m\in\Theta(n)\), this algorithm features \(O(n^{\delta})\) depth, for any fixed constant \(\delta\in(0,1)\), but incurs a polylogarithmic space blow-up. The algorithm is rather involved and communication intensive, hence, while theoretically efficient, in practice they may run slowly when implemented on distributed-memory clusters of loosely coupled servers, where communication overhead is typically high.
## 1 Introduction
In the proposed ACRLPC protocol for a UWSN, an optimized communication channel utilizes a hybrid MAC protocol that employs both CSMA/CA and TDMA, as shown in Figure 2, by employing an AC-RL technique and a Communication Channel-Optimized Adaptive Power Control (OAPC-MAC) method to vary the network performance according to the changing underwater environment. To solve both of these problems, a hybrid TDMA/CSMA MAC layer protocol that takes both the availability of TDMA and CSMA/CA at the MAC layer of a UWSN was designed. A hybrid approach to MAC, combining CSMA/CA and TDMA, was designed for UWSNs. This framework tackles the key challenges of energy efficiency and throughput in UWSNs, both of which are essential for their sustainable operation. In addition, we introduced a new power control strategy aimed at reducing energy consumption while optimizing transmission slot allocation. The entire process, outlined in the Section 3, is managed using RL-AC. In Section 2, we will discuss related works, the material and methods will be discussed in Section 3, the results and discussion will be presented in Section 4, and the conclusion will be made in Section 5. Figure 2: Workflow of the proposed ACRLPC-MAC framework.
## 5 Discussion of Results
### Building Vibration Modes  
In Section 4.3, it was observed that the rotational modes 3 and 6 of the building equipped with TMD2 have participating mass ratios that are slightly different from those of modes 3 and 6 of the building with TMD1. This is due to the different arrangement in the building plan of LDRIs in TMD2 (Figure 15) with respect to LRIs in TMD1 (Figure 14), which induces a difference in the rotational stiffness.  
In Figure 23, the acceleration spectrum for the considered building site with 5% and 27.7% damping is shown. From this figure, it can be seen that the spectral accelerations of the first three modes of the OB belong to the constant acceleration branch. The spectral acceleration of the first mode is indicated as OBa in Figure 23.  
As observed in Section 4.3, the first six modes of the building equipped with TMD1 have participating mass ratios similar to those of the building with TMD2. The periods of the first three are higher than those of the OB and involve lower spectral accelerations belonging to the constant velocity branch. Conversely, the periods of the other three modes are lower than those of the OB, but involve a spectral acceleration that is still on the plateau. To show the effect of periods' change on spectral accelerations due to TMD, the spectral accelerations for \(\xi=5\%\) of modes 1 and 4 of the building with TMD1 are indicated in Figure 23 and labeled as TMDa and TMDc, respectively. The damping provided by TMD further reduces spectral accelerations, thus moving points TMDa and TMDc to points TMDb and TMDd, respectively (see Figure 23).  
The last three modes of the OB do not change remarkably with the installation of the TMD, but their spectral accelerations are reduced due to damping. Thus, it can be gathered that higher frequency modes only benefit from the TMD by damping and not by tuning. To show this, the spectral acceleration of mode 4 of the OB and that of the corresponding mode of the building with TMD1 (mode 7) are indicated by points OBb and TMDe in Figure 23, respectively.  
Figure 23: Reduction of the spectral accelerations of the modes with the most significant participating mass ratios in X direction for the building equipped with TMD1.  
From Figure 23, it can be seen that, thanks to the intervention with TMD1, the whole building is subjected to spectral accelerations ranging between 2.66 and 3.64 m/s\({}^{2}\), which are lower than those of the OB (5.07-6.60 m/s\({}^{2}\)). As a consequence, internal forces in structural members due to seismic action are considerably reduced.  
As for the base-isolated building, from Figure 23, it can be seen that the spectral acceleration of the first mode (point BIS) belongs to the spectrum constant displacement branch, which is characterized by the lowest accelerations. As a consequence, very low seismic forces arise in the building. Damping brought by RIs has very little relevance to spectral acceleration reduction.
## 2 Materials and Method  
### Site Description and Measurement Period  
Measurements were performed at the Hyytiala SMEAR II (Station for Measuring Forest Ecosystem--Atmosphere Relations) site during the PEGASOS 2013 campaign. The rural forested site [25] is in southern Finland (61\({}^{\circ}\)51\({}^{\prime}\) N, 24\({}^{\circ}\)17\({}^{\prime}\) E, 181 m above sea level), in the middle of a Scots pine forest. The nearest cities are: Tampere (235,000 inhabitants), located 50 km away to the southwest, and Jyvaskyla (140,000 inhabitants), about 100 km away. There is also the town of Orivesi (population approximately 9000) 19 km south of SMEAR II and the nearby small village of Korkeakoski 6-7 km to the southeast of the station.  
ACSM (Aerosol Chemical Speciation Monitor) measurements were performed at the ground during the campaign. A description of the ACSM and basic data processing can be found in [PERSON] et al. [26]. The period of the measurements is from 23 April 2013 until 19 June 2013. This period was selected to achieve the objectives of the PEGASOS project regarding air quality-climate interactions, in this case, in a relatively clean environment dominated by biogenic emissions.
## 5 Experiment and Analysis
### Effect of Bicubic Interpolation on the Relative Error of the Fit after Processing  
The original flat part image was doubled in length and width using bicubic interpolation so that the part element had more contour points as support, which could further improve the fitting accuracy of the element. The effect of interpolation processing on the relative error is shown in Figure 22. It can be seen that the relative error of the measurement results was generally reduced and tended to be stable after the interpolation process. In particular, the relative error was significantly decreased at the short graphic element (indices 4-8).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{5}{c}{**Arc Graphic Elements**} & \multicolumn{4}{c}{**Straight-Line Graphic Elements**} \\ \hline
**Index** & **Circle Center** & **Radius** & **Index** & **Tilt Angle** & **Length** \\ \hline \(l_{1}\) & (401.11, 544.51) & 362.35 & \(l_{2}\) & 9.91 & 104.56 \\ \(l_{3}\) & (164.15, 532.68) & 92.48 & \(l_{4}\) & \(-\)79.39 & 190.24 \\ \(l_{8}\) & (594.37, 643.38) & 92.38 & \(l_{5}\) & 69.86 & 32.31 \\ \(l_{9}\) & (775.13, 611.13) & 91.66 & \(l_{6}\) & \(-\)46.12 & 35.38 \\ \(l_{11}\) & (891.96, 216.48) & 92.26 & \(l_{7}\) & 10.04 & 182.01 \\ \(l_{12}\) & (803.02, 230.08) & 181.11 & \(l_{10}\) & 99.75 & 354.12 \\  & & & \(l_{13}\) & \(-\)169.87 & 284.42 \\  & & & \(l_{14}\) & \(-\)155.09 & 277.83 \\ \hline \end{tabular}
\end{table}
Table 4: Results of fitting on experimental plots.
## 4 Spatial Resolution of the Measurements
### Discretization between Two Bubbles Growing on the Plate  
With the same 2D model, the space meshing between two bubbles (depending on the thickness of the plate) is also checked, as shown in Figure 22. For a fixed thickness, the space between two bubbles of the same size increases until they can be detected independently from each other on the dry side of the plate (see Figure 22).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Bubble Size** & **Pressure** & **First Criterion: Detection** & **Second Criterion:** \\
**μm** & **Dear** & **Thickness** & **Measurement Thickness** \\  & & **μm** & **μm** \\ \hline
800 & 1 & 250 & 200 \\
120 & 20 & 170 & 110 \\
70 & 50 & 100 & 60 \\
50 & 80 & 70 & 40 \\
40 & 110 & 65 & 35 \\ \hline \end{tabular}
\end{table}
Table 1: Spatial resolution: thickness range associated with the bubble size (pressure dependent).  
Figure 22: Example of the determination of the minimum gap for two bubbles to be discernible. For the first example (**left**), the bubbles are too close, and the temperature on the dry side will not allow discretizing two objects except for one big bubble growing on the plate. For the second example (**right**), after expanding the distance between the bubbles, the temperature field allows detection of two different spots.  
Table 2 summarizes the minimum gap between two bubbles to be detected independently (and not seen as one). The thickness of 50 \(\upmu\)m will provide a discretization of 40 \(\upmu\)m for bubbles with a diameter of 200 \(\upmu\)m and 100 \(\upmu\)m for bubbles with a diameter of 50 \(\upmu\)m.
# On the Use of the Effective Width for Simply Supported Generally Loaded Box-Girders
## 6 Numerical Results
### Case Study
#### 6.1.2 Displacement Analysis  
The vertical displacement \(v(z)\) by the three analytical models considered (total Euler-Bernoulli, total Timoshenko and effective Euler-Bernoulli) are plotted in Figure 19 with those by the FEM analysis, for the three load cases. As expected, for all three cases analyzed, the \(EB_{t}\) model, in which the shear-lag is included by exploiting the concept of wavelength-dependent effective width, is the most consistent with the FEM results with a maximum error of about 0% for the DL, 1.6% for the SL and 2.8% for the CL. Furthermore, \(EB_{t}\) and \(TM_{t}\) model present respectively a maximum error of about 7% and 3.6% for the DL, 8.7% and 2% for the SL, and 8.9% and 5.3% for the CL.  
Figure 19: Vertical displacement (in mm) evaluated at half side of element 1 (at \(s_{1}=0\)): (a) uniformly distributed load (DL); (b) segment of uniformly distributed load (SL); (c) concentrated load (CL). Total Euler–Bernoulli (dashed line), total Timoshenko (thin line), effective Euler–Bernoulli (thick line), FEM (bullets).  
Figure 18: Maximum tangential stress (in N/mm\({}^{2}\)) vs. \(z\) on web: (a) \(n\)-th harmonic \(\tau_{u,max}^{up}\cos\left(\frac{n\pi}{L}z\right)\), \(n=1,2,3,4,5\); (b) by Equation (10b) for \(N=1,2,3,4,5,\ldots 50\) (from the lightest line to the darkest). FEM analysis (bullets). Concentrated load (CL).
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 2 BLE Signal Collection Setup
### Gateway Receiver Setup  
The study also investigated the configuration of gateways to assess their impact on BLE signal reception performance. Ten gateways were deployed across the area, with five placed on the walls and five on the ceiling. The ceiling-mounted gateways were attached to light frames and oriented toward the room's center to minimize signal loss due to narrow angles. To evaluate the configuration, the diode feature of the new gateway generation was employed, with the blinker diode used to monitor signal receipt from all corners of the room for each gateway. The findings were further validated through data analysis. Wall-mounted gateways generally did not encounter issues. Figure 3 provides a schematic representation of the deployment of gateway receivers within the indoor environment, illustrating their spatial arrangement and distribution.
## 1 Introduction
However, it has not been verified under transient conditions, and there is no explanation for the linear resistance value when the oscillatory flow rate amplitude is close to zero. On the other hand, there are no theories yet available for calculating the communication port inertance. To establish a communication port impedance calculation method, this paper employs a transient CFD (Computational Fluid Dynamics) simulation approach with \(k\)-\(\epsilon\) turbulence model to investigate the unsteady process of communication ports. The reliability of this research method is mainly verified by comparing the transient CFD simulation result with the experimental result of an orifice flow example in Section 2. In Section 3, a quasi-steady assumption-based impedance calculation method for the communication port is supplemented, which is then furthered validated in Section 4 through the transient CFD simulation results of a simplified communication port model. A linear frequency correction method for communication port impedance to extend its applicability to the pogo frequency range is also proposed. Concluding remarks are summarized in Section 5.
## 5 Conclusions  
In this paper, we propose a novel spectrum-sharing method for the integrated wireless multimedia sensor and cognitive satellite network based on the dynamic frequency allocation. The LEO satellite system which shares the same spectrum with the incumbent GEO system functions as the auxiliary to the WMSN. Specifically, the LEO satellite user plays the role of the sink for the terrestrial sensor network. To avoid the co-channel interference, the keep-out region is presented. Furthermore, the dynamic  
Figure 8: Comparison of SINR between the APC and the proposed algorithm. (a) GEO user; (b) LEO user.  
Figure 9: Throughput of the entire network during the satellite passage.  
frequency allocation algorithm is proposed. Numerical results evaluate the performance and compare it with the commonly used technique adaptive power control. Allowing frequency reuse between dual satellite systems, the integrated multimedia wireless sensor and cognitive satellite network can enhance the existing WSN applications. The proposed spectrum-sharing method is a potential solution to deliver high-speed content for satellite-based WMSNs with regard to the scarce spectrum.  
[PERSON] and [PERSON] proposed the spectrum-sharing method for the integrated wireless multimedia sensor and cognitive satellite network. [PERSON] and [PERSON] presented the interference analysis model and the interference analysis in the worst case. [PERSON] derived the dynamic frequency allocation algorithm. The simulations were implemented by [PERSON] and [PERSON]. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] read and approved the final manuscript.  
This research was funded by the National Natural Science Foundation of China Grant Nos. 91738201, 91438109 and 6160011378.  
The authors are deeply grateful for all the help from the papers quoted. Finally, the authors would like to express their gratitude to the reviewers for their constructive comments.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} APC & Adaptive Power Control \\ CCI & Co-Channel Interference \\ ECEF & Earth-Centered Earth-Fixed \\ GEO & Geostationary Earth Orbit \\ ITU & International Telecommunication Union \\ LEO & Low Earth Orbit \\ NGSO & Non-GeStationary Orbit \\ SINR & Signal to Interference plus Noise Ratio \\ SNR & Signal to Noise Ratio \\ WMSN & Wireless Multimedia Sensor Network \\ WSN & Wireless Sensor Network \\ \end{tabular}