esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of the Obstacle Full Shadow and Half Shadow on Indoor VLC Systems
By changing the room size L and the transmitting power, the relationship between the outrage probability of indoor single light source and multiple light sources VLC system and the changing room size and transmitting power was simulated and analyzed, as shown in Figure 8.  
Figure 7: Outrage probability simulation diagram under different light sources. (**a**) Two light sources; (**b**) Three light sources; (**c**) Four light sources. As can be seen from Figure 8, with the same transmitting power, the system outrage probability of both single and multiple light sources gradually increased with the increase in room size L. This was because, with the increase in room size L, the communication distance between the photodetector and the visible light source also increased. As the distance increased, the signal-to-noise ratio reaching the photodetector also decreased, and the system outrage probability eventually increased. In the case of the same room size L, the system outrage probability of both single and multi-light sources gradually decreased with the increase in the transmitting power. This was because when the room size L remained unchanged, the signal-to-noise ratio to the photodetector increased with the increase in the transmitting power, and the system outrage probability eventually decreased. At the same time, it could also be seen from Figure 9b that the outrage probability of the half-shadow occlusion was always larger than that of full shadow, and the outrage probability curve of the half-shadow occlusion did not decline smoothly with the increase in the constant amplitude of the transmission power. The reason for this phenomenon was that the position of obstacles set in this paper was generated randomly. Since the position of obstacles generated each time was uncertain, the obstacle generated shadow areas of different sizes in different positions and eventually led to the above phenomenon in the outrage probability of the system. Figure 8: Simulation diagram of channel model applicability of indoor VLC system with shadow occlusion under different light sources. (**a**) Two light sources; (**b**) Four light sources. Figure 9: Optimization diagram of light source layouts. The above simulation results show that the model can be applied to different room sizes, but in the actual environment, different room sizes should be selected according to different performance requirements to obtain the optimal value of an indoor VLC room.
## 2 Theoretical Derivations
### Calculation of Prestress Loss, \(\sigma_{st,l}\)
#### 2.1.3 Prestress Loss Caused by Anchorage Deformation, \(\sigma_{I2}\)  
The prestress loss caused by deformation at the end of the anchorage should be taken into consideration. This refers to the prestress loss caused by the deformation of the anchor and the retraction of the clip due to the concentrated stress. A slip at the anchorage depends on the particular prestressing system adopted and is not a function of time. This loss can be written as per Equation (16):  
\[\sigma_{I2}=E_{st}\frac{\Delta l_{re}}{2\pi\tau_{st}}. \tag{16}\]  
1.4 Prestress Loss Caused by the Elastic Compression of Concrete During Batch Tensioning, \(\sigma_{I3}\)  
The prestress loss can be adjusted using certain construction technologies, including ultra-tensioning and repeated tensioning. For example, the tensioning can be started from the middle of the pipeline and gradually pulled symmetrically to both sides when batch tensioning is adopted. The steel strands that would later be tensioned would cause elastic compression deformation of the concrete, which would contribute to the prestress loss of the previously anchored strands. This prestress loss can be simplified by the following formula [21]:  
\[\sigma_{I3}=\frac{m-1}{2m}n_{y}\sigma_{h1} \tag{17}\]  
where \(m\) is the total number of batches and \(\sigma_{h1}\) is the normal stress of the concrete produced by the combined force of the steel strands at the action point (the center of gravity of all steel strands), which is equal to the sum of the normal stresses of the concrete produced by the batch of steel strands, which is \(\Delta\sigma_{h1}\). That is, \(\sigma_{h1}=\sum\Delta\sigma_{h1}=m\Delta\sigma_{h1}\). \(n_{y}\) is the combined force of all of the steel strands. \(\Delta\sigma_{h1}\) is the normal stress of the concrete generated by the subsequent batch of steel strands at the center of gravity of the first tensioned steel strand, as calculated by the following formula: \(\Delta\sigma_{h1}=\frac{n}{m}\Big{(}\frac{1}{A_{j}}+\frac{\sigma_{y}\Psi_{i}}{ l_{j}}\Big{)}\).  
During engineering, ultra-tensioning or repeated tensioning technologies can be utilized in the first several batches of strands, so that the actual effective prestress of the pipe is substantially equal to the design level. After ultra-tensioning or repeated tensioning, the prestress loss caused by the elastic compression of the concrete during batch tensioning, \(\sigma_{l3}\), is considered to be approximately zero.
# Bilinear and linear forms  a = dot(grad(v), grad(u))*dx + k*djump(v)*djump(u)*dc  L = v*f*dx
## 5 Examples
### Weighted Poisson Equation  
We consider first the weighted Poisson with discontinuities in the solution \(u\) across the surfaces \(\Gamma_{d}\) and fluxes acting on the discontinuity surfaces. The bilinear and linear forms associated with this problem read:  
\[a\left(u_{h},v_{h}\right) =\int_{\Omega\setminus\Gamma_{d}}w_{h}\
abla v_{h}\cdot\
abla u_{h} \,d\Omega+\int_{\Gamma_{d}}\llbracket v_{h}\rrbracket\,k\,\llbracket u_{h} \rrbracket\ d\Gamma, \tag{23}\] \[L\left(v_{h}\right) =\int_{\Omega}v_{h}f\,d\Omega, \tag{24}\]  
where \(f\) is the source term and \(w_{h}\) is the interpolant of the supplied weighting function \(w\) in a finite element space. The flux across the discontinuity surface is \(q_{d}=k\,\llbracket u\rrbracket\), where \(k>0\). The relevant finite element function space is given in Equation (20). The form compiler input for an extended finite element formulation of this problem is shown in Figure 2 for the case of quadratic basis functions on triangular cells.  
We present computed results using the form compiler input in Figure 2 for the case \(\Omega=(0,1)\times(0,1)\), \(f=1\), \(w=1+e^{x^{2}}\) and \(k=10\). Dirichlet boundary conditions are applied along the bottom and top edges (\(u_{h}=0\) for \(y=0\) and \(u_{h}=1\) for \(y=1\)) of the domain. Along the sides of the domain (\(x=0\) and \(x=1\)) a zero flux condition is applied. Three disjoint discontinuity surfaces are included in the domain. Precise details of the crack paths can be found in the supporting material [12]. Having compiled the input file with the form compiler, the generated C++ code serves as input for the solver, which is where the discontinuity surface is defined. For this problem, an extract of the C++ program is shown in Figure 3. The code for the objects Poisson::BilinearForm, Poisson::LinearForm and Poisson::PostProcess has been generated by the form compiler. The other elements of the code which appear in Figure 3 are reusable components which are independent of the considered equation. The computed results for this problem are presented in Figure 4 which shows the discontinuity surfaces, which are not aligned with the mesh, and contours of the solution field in which the jumps in the solution across the discontinuity surfaces can be observed.  
Figure 2: Form compiler input for the weighted Poisson equation with discontinuous \(u\).  
Figure 4: Weighted Poisson problem in two dimensions: (a) mesh and discontinuity surfaces and (b) solution contours.  
Figure 3: C++ code extract for the two-dimensional weighted Poisson problem with discontinuities in the solution.
## 2 Experimental Procedure
### Materials  
Ordinary type I Portland cement (OPC), conforming to the KS L 5201 standard (equivalent to ASTM C 150), was used for preparation of the base concrete. Ground granulated blast furnace slag (GGBFS), conforming to KS F 2563 (equivalent to ASTM C 989), was replaced 40% by weight of the Portland cement. The GGBFS used in this work was reported to contain calcium sulfate anhydrite as an activator. The chemical compositions of cementitious materials used in this work are shown in Table 1.  
The photographic images of steel and polyolefin fibers used in this work are presented in Figure 1. Natural sand with a fineness modulus of 2.64 was used as fine aggregate. The crushed stone with nominal maximum size of 10 mm was used as coarse aggregate.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **SO\({}_{3}\)** & **LO.I** \\ \hline OPC & 62.5 & 22.1 & 5.4 & 3.2 & 2.1 & 2.2 & 0.9 \\ \hline GGBFS & 45.9 & 32.6 & 11.7 & 0.5 & 4.4 & 2.8 & - \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cementitious materials [unit = wt%].
# LightR: A Fault-Tolerant Wavelength-Routed Optical Networks-on-Chip Topology +
## 2 Background
### Performance Factors  
In ONoCs, insertion loss and crosstalk noise are two important performance factors, which can decrease the signal-to-noise ratio (SNR) and cause power penalties [19].  
Insertion loss is the power loss of signals. Typically, in a WRONoC topology, the insertion loss of a signal can be considered as the summation of three main losses [6; 16]: the crossing loss that depends on the number of waveguide crossings that the signal passes; the drop loss when the signal is on-resonance with an MRR; the through loss when the signal passes through an off-resonance MRR. In particular, the worst-case insertion loss of  
Figure 3: A \(2\times 2\) PSE supports (**a**) two on-resonance signals and (**b**) two off-resonance signals.  
a WRONoC topology is the maximum insertion loss of all signals, which determines the power consumption of the network.  
Crosstalk noise refers to the noise signals generated at MRRs and waveguide crossings [19]. As shown in Figure 4, when a signal passes through a waveguide crossing or an off-resonance MRR, or when a signal is on-resonance with an MRR, a portion of the signal power will leak to other outputs and become noise. Noise generated by the original signals is denoted as the first-order noise and has the same wavelength as the original signals [20]. When a noise signal reaches a slave, it will decrease the SNR of the desired signals on the same wavelength. Specifically, the SNR of a signal on wavelength \(\lambda_{i}\) is calculated as \(10 log\frac{P_{output}^{\lambda_{i}}}{P_{noise}^{\lambda_{i}}}\), where \(P_{output}^{\lambda_{i}}\) denotes the output power of the desired signal, and \(P_{noise}^{\lambda_{i}}\) denotes the power of the noise signals [20]. For the calculation of the SNR, we only consider the first-order noise, since the power of the noise generated by other noise signals is relatively small.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Seismic Resilience Approach  
Seismic-related measures and approaches have been historically considered critical solutions for predicting the overall resilience of both standalone infrastructures and interdependent critical infrastructures (ICIs) [60]. A promising method has been introduced by the Multidisciplinary and National Center for Earthquake Engineering Research (MCEER).  
MCEER developed a comprehensive resilience framework [37] to define the seismic resilience of infrastructure in response to a specific event, such as an earthquake. The impact of an earthquake can be estimated by calculating the predicted degradation in the quality of service associated with the infrastructure, \(Q(t)\). Assuming an earthquake occurs at time \(t_{0}\), this degradation is estimated for the period immediately following the shock (\(t_{0}\)) until \(Q(t)\) recovers to its pre-earthquake levels (\(t_{1}\)). Resilience loss, \(RL\), is determined as:  
\[RL=\int_{t0}^{t1}[100-Q(t)]dt \tag{16}\]  
Alternatively, the overall system loss can be predicted based on the shaded area in Figure 16. The framework assumes that infrastructure performance levels are at 100 percent before the shock event and will recover to this level after the earthquake. Although this approach is demonstrated in the context of earthquakes, it can be adapted for other types of shocks as well.  
_Limitations:_ As one of the oldest approaches for measuring ICIs resilience, the seismic approach has several weaknesses. One significant limitation is its time-dependent (static) nature, which only considers the event time and does not cover all resilience phases (i.e., anticipation, absorption, and adaptation) along the timeline. Furthermore, this approach relies heavily on accurate and available data, which may not always be feasible.
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 2 Material and Methods
### Computer Vision
#### 2.2.1 Faster R-CNN  
Faster R-CNN is a two-phase model because it includes two phases: first, it looks for the Regions of Interest (RoI) to predict if an object exists; then, using that RoI, it predicts the object's class and coordinates. This model has three main steps. The backbone is where a Feature Pyramid Network (FPN) obtains the feature maps of the images. The Region Proposal Network (RPN) predicts the RoI, that is, the regions that have objects and their coordinates. Lastly, there are the RoI classifier and the bounding box regressor. The first classifies each object in the bounding boxes (regions that have an object). The latter improves predictions for the dimensions of those boxes [23] (see Figure 3).
## 2 Materials and Methods
### Gas Chromatography Analysis  
Volatile emissions were analyzed by GC-MS Agilent 7890B GC coupled with 5977B mass selective detector (Agilent Technologies, Santa Clara, CA, USA). A DB-5 column (30 m \(\times\) 0.25 mm inner diameter with 0.25 \(\upmu\)m film thickness) was used and GC oven temperature program was 60 \({}^{\circ}\)C for 1.3 min, rose at 3 \({}^{\circ}\)C/min up to 246 \({}^{\circ}\)C. The injector and detector temperatures were kept at 220 \({}^{\circ}\)C and 230 \({}^{\circ}\)C. Helium was used as a carrier gas at a flow rate of 1.3 mL/min. Mass spectra were recorded at 70 eV. Mass range was \(m/z\) 35 to 450 Da and the scan rate was 2.8 scans/s. Mass Hunter B.07.06 software (Agilent Technologies) was used for data acquisition and processing. Volatile compounds emitted by symbionts were identified by comparing retention times, retention indices (RI) calculated using the [PERSON] and [PERSON] [20] equation in relation to a homologous series of \(n\)-alkanes (C\({}_{9}\)-C\({}_{21}\)) and libraries MassFinder [21], Adams Library [22], Flavours and Fragrances of Natural and Synthetic Compounds 3 [23], and Wiley 12/NIST 2020 [24], and in-house library \"SHRS Essential Oil Constituents-DB-5\" which was built up from authentic standards and components of known essential oils [5; 7; 16]. Results expressed as the relative percentage of each compound peak area to the total GC-MS peak area. Compounds released from Petri plates without fungal culture and siloxanes in procedural blanks as background contamination produced by SPME fiber were subtracted. All sample preparations and analyses were independently performed in triplicate.
## 2 System Definition
### System and Channel Model
Assuming that from 3 GPP 3D channel model [25], it is defined as follows:  
\[A(\psi,\theta)=-\text{min}\left[-\left\{A(\psi)+A(\theta)\right\},\,30\right] \tag{8}\]  
\[A(\psi)=-\text{min}\left[12\,\left(\frac{\psi-\pi/4}{\theta_{3\text{dB}}} \right)^{2},\,30\right] \tag{9}\]  
\[A(\theta)=-\text{min}\left[12\,\left(\frac{\theta-\pi/4}{\theta_{3\text{dB}}} \right)^{2},\,30\right] \tag{10}\]where \(\psi_{\rm 3 dB}\) and \(\theta_{\rm 3 dB}\) denote half power beamwidth (HPBW), and are set to 1.135 (=65\({}^{\circ}\)) [25]. The above settings are commonly applied to the transmitter and receiver side. \(G(\psi,\theta)\) is the joint probability density function of AoAs, which is assumed to follow Laplacian distribution [26]:  
\[G(\psi,\theta)=\frac{1}{\sqrt{2}\sigma_{\psi}}e^{-|\frac{\sqrt{2}\sigma_{\psi }}{\sigma_{\psi}}|}\ \frac{1}{\sqrt{2}\sigma_{\psi}}e^{-|\frac{\sqrt{2}\sigma_{\psi}}{\sigma_{ \psi}}|} \tag{11}\]  
Standard deviation value, \(\sigma_{\psi}\) and \(\sigma_{\theta}\), are set to 0.087 (=5\({}^{\circ}\)) to simulate the LoS dominant channel. \(W_{p,q}(\psi,\theta)\) is phase difference between the \(p\)-th and the \(q\)-th antenna elements, where suffixes \(x\) and \(y\) indicate row-wise and column-wise directions, respectively. \[W_{p,q}(\psi,\theta)=2\pi\frac{\delta x(p_{x}-q_{x})}{\lambda}\text{cos}\psi \text{sin}\theta+2\pi\frac{\delta y(p_{y}-q_{y})}{\lambda}\text{cos}\theta \tag{12}\]  
Since the calculation of Equation (7) for massive antenna elements requires heavy complexity, integrations are approximated using clustered angular spread [25] (Table 7.3-3). Time variation of the NLoS component follows Jakes'model [27].
# ML-Based 5G Network Slicing Security:
## 7 Future Directions and Research Challenges
Current 5G approaches employ C-RAN, but with increasing challenges and needs, it is necessary to reduce the operating cost, accelerate operation over the network, enhance the QoS, and save energy. Therefore the aim is to flexibly interconnect D-RAN and core network functions hosted over cloud network infrastructure. Xhaul architecture can enable such flexibility and reconfigure the network quickly and cost-effectively. 6. **Secure deployment**--5G has been designed by considering multiple security mechanisms with secure control over the network to provide mutual authentication, subscriber identity protection, secure service migration, secure slicing, and many other benefits. For successful deployment of 5G, the provider needs to certify all the connections and verify all the carriers' IDs, frequencies, and cell coverage in the network [118]. 7. **Performance Metrics**--In 5G communication networks, several parameters are involved, all of which have an impact on the network's performance. Communication network performance can be measured in terms of the network's capacity, quality, lifetime, efficiency of routing, low latency, and high reliability. 5G networks allow lots of traffic, requiring a higher load balancing capacity to keep the networks running well. According to [PERSON] et al. [98], collaboration between the RAN and backhaul will open up new possibilities for improved performance. According to recent breakthroughs in the field, the energy consumption of communication networks is based on carried traffic modeling and topology options. Throughput can be increasedthrough effective load balancing. Future research should concentrate on improving QoS and traffic control. * **Optimization--** The 5G network is made up of several sub-modules, each of which has an essential function in ensuring secure data transmissions. A network's efficiency will be harmed by channel interference and path loss. The bandwidth utilization of networks is affected by link failure and node isolation. Network slicing is a feature of 5G networks that allows for virtual network partitioning. In heterogeneous networks, time synchronization is a significant issue that affects inter-cell coordination, which are directly proportional to each other. This new idea aids in a variety of application-based network allocations. As a result, effective network slicing aids in network traffic optimization, load management, and traffic management efficiency [119].
## 2 Results and Discussion
### Recovery of FAME from Transesterification Reagent using Hydrophobic Resin  
The results from Figure 1 showed that the sulfuric acid/methanol transesterification reagent could be reused multiple times for eluting algae from resin. We next tested whether recycling the sulfuric acid/methanol reagent led to a progressive decrease in FAME synthesis in comparison to fresh sulfuric acid/methanol [Figure 3(a)]. For the recycled sulfuric acid/methanol solution, each eluate was extracted with hexane and analyzed for FAME content before it was reused. The results showed that both fresh and recycled sulfuric acid/methanol solutions gave comparable amounts of FAME during four successive cycles of algal loading and elution.  
Reuse of the sulfuric acid/methanol solution would minimize chemical costs, and removal of the FAME with each cycle should promote product formation. Hexane extraction of FAME is a relatively cumbersome and time consuming process leading us to explore a simple hydrophobic resin-based method to collect the FAME generated during each cycle. For comparison, the sulfuric acid/methanol eluate was either directly extracted with hexane or passed over a hydrophobic ethylene glycol dimethacrylate:hexyl methacrylate (EGDMA:HMA) resin [Figure 3(b)] which was then eluted with hexane in a separate recovery step. The results show that FAME recovery from the resin (11.0% of DCW) was comparable to that obtained by direct hexane extraction [11.8% of DCW; Figure 3(c)]. While the hydrophobic column was effective at collecting FAME on the lab bench scale, we suspect that other methods, such as a hollow fiber membrane extractor [28] would be more useful commercially.
## 4 Discussion
Several studies reported no significant differences in ADC values between 2 \(\times\) SMS ss-EPI and conventional ss-EPI in several organs [26; 27; 28], including the rectum [11]. Our study also revealed that there was no significant difference of ADC values between rs-EPI and 2 \(\times\) SMS rs-EPI in the tumor tissue and normal tissue of the rectal cancer. A few previous studies have reported decreased ADC values using SMS-DWI in the liver and pancreas in comparison with conventional DWI [15; 22; 29; 30]. These studies hypothesized that the elevated noise floor in SMS-DWI may result in generally lower ADC values [22]. However, several subsequent studies reported no significant differences in ADC values between SMS-accelerated sequences and conventional DWI in several organs [26; 27; 28], which is in line with our results. This difference may be explained by the fact that the TR was chosen to be as low as possible to minimize SMS technology, resulting in a lower SNR of DWI images and unstable calculated ADC values [22; 30]. Our study had limitations. First, this was a retrospective, single-center study with a small sample size. Second, the study population included only patients with biopsy-confirmed rectal cancer, while further studies should include patients with benign rectal lesions or those who received radiotherapy for rectal cancer to validate the study findings further. Third, manual human measurement of ROI values increases the likelihood of sample error. Fourth, our study only examines the acceleration factor and does not discuss the impact of slice distance and number of shots on image quality, it will be designed and added prospectively in subsequent studies.