esa-sceva/satcom-chunk-collection huggingface dataset:
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 5 Performance Evaluation
### Data Dissemination Delay  
In this section, we compare the data dissemination delay of the compared algorithms. The compared algorithms are GPSR, the epidemic routing scheme, SW and CCR. We will show how data dissemination delay changes when the number of message copies varies from 100-500 in Figures 12 and 13. Figure 12 shows the results in the regular urban environment, and Figure 13 describes the situation of the highway. When the number of message copies increases, the dissemination time increases. The possible reason is that the more messages to be delivered, the more data transmissions occur until the network converges, hence causing the increasing dissemination delay. We again observe that our proposed EDDA achieves better dissemination delay.  
Figures 14 and 15 compare the effect of different vehicle densities when the number of message copies is equal to 300. From the differences in the figures, we can see that data dissemination delay increases with the increasing vehicle nodes. In the scenarios when the number of vehicles increases, the source vehicle typically needs to make multiple attempts to disseminate the messages to other  
Figure 11: Transmissions vs. number of vehicles on the highway.  
Figure 10: Transmissions vs. number of vehicles in urban VANET.  
vehicles in the destination area, which increases the overall dissemination time. Epidemic routing and the rest of the schemes manage to achieve good delays for low vehicle density, but perform poorly for most values. GPSR has a relatively low dissemination delay in the sparse environment, and the delay will increase when the network becomes dense. On the other hand, due to the limitation that the number of message copies that can be spread is bounded, SW, CCR and EDDA exhibit greater stability. They perform fewer transmissions in all scenarios, while achieving slightly increased dissemination delays as the level of network connectivity increases. As is evident from the figures, in terms of dissemination delay, if traffic loads are low, it is known that most of the schemes can obtain good delays under these conditions. Nevertheless, if traffic starts increasing, EDDA actually outperforms all schemes in terms of delay.  
Figure 12: Dissemination delay vs. number of message copies in Urban VANET.  
Figure 13: Dissemination delay vs. number of message copies on the highway.
## 3 State of the Art
### Proposed Coexistence and Coordination Techniques
#### Other Related Work
Furthermore, the survey discusses the concept of the scenario-oriented coexistence, in which coexistence-related problems are solved according to different deployment scenarios. Although the coexistence between LTE operating in the unlicensed spectrum and Wi-Fi is being investigated extensively, little attention is given to studies that investigate cooperation scenarios among the two technologies. As has been discussed in Section 2, in this paper, we distinguish two different types of cooperation between LTE in the unlicensed spectrum and Wi-Fi. Furthermore, for each solution, we propose and describe different techniques that can lead to efficient and fair spectrum use.
## 2 Materials and Methods
### Gross Energy and Protein Digestibility  
The frozen excreta samples were placed in a freeze dryer (Christ Alpha 1-4 LD plus, Osterode am Harz, Germany) for drying, and then dried samples were ground into fine particles using an ultra-centrifugal mill (Retsch ZM 200, Fisher Scientific, Hampton, NH, USA) with a 0.5 mm screen. Feed samples were also ground using the same technique. Protein concentration in both the excreta and feed samples was measured as described by [PERSON] [37] following the Dumas combustion method using a nitrogen analyser (LECO Corporation, St. Joseph, MI, USA), where EDTA was used as the calibration standard. The GE concentration in the excreta and feed samples was determined using a Parr Adiabatic Oxygen Bomb calorimeter (Parr Instrument Co., Moline, IL, USA) with benzoic acid as the calibration standard. Mixed feed samples were also oven-dried at 105 \({}^{\circ}\)C for approximately 24 h (to constant weight), as described above, for determination of diet DM, so GE and CP digestibility could be presented on a DM basis. Apparent GE and CP digestibility on a DM basis was calculated using the following equations described by [PERSON] and [PERSON] [38].  
\[\text{Apparent protein digestibility (\%) = (CP_{\text{retained}}/CP_{\text{ intake}}) \times 100}\] \[\text{Apparent energy digestibility (\%) = (GE_{\text{retained}}/GE_{\text{ intake}}) \times 100}\] \[\text{CP}_{\text{intake (g/day) = CP_{\text{feed}}\ (\%) \times FI (g/day/hen)}}\] \[\text{GE}_{\text{intake (kcal/day) = GE_{\text{feed}}\ (kcal/g) \times FI (g/day/hen)}}\] \[\text{CP}_{\text{retained (g/day) = CP_{\text{intake}}- CP_{\text{excreta }}\ (\%) \times excreta volume (g/day/hen)}}\]  
\[\text{GE}_{\text{retained (kcal/day) = GE_{\text{intake}}-GE_{\text{excreta }}\ (kcal/g) \times excreta volume (g/day/hen)}}\]  
where CP, GE and FI are crude protein, gross energy, and feed intake, respectively.
## 3 Lattice Construction Algorithm  
The algorithm is summarised in the flowchart (Figure 2). First, the inputs, given by the lattice frame and local shape parameters, are read. Then, the lattice frame is subdivided in order to split the construction task into small, independent, and parallelisable pieces. Afterwards, for each of the latter, the applicability of the general construction method is verified. Otherwise, a suitable special case workaround is considered. In any case, the construction is tested for failures and a fallback in geometric continuity is applied if necessary. A set of surface patches that describes the surface of the solidified lattice structure is given as an output. The algorithm is implemented as a macro in CATIA, but it is not limited to this software. It can be reimplemented in any CAD software that enables the construction of the three types of surfaces defined in Section 2.
## 2 Results
### DisA and CdaA Are Required for SAM in B. subtilis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Revertants**}} & \multicolumn{3}{c}{**Number of Revertants That Grew/Number Tested**} & \multicolumn{2}{c}{**Revertants with His\({}^{+}\) [PERSON]\({}^{+}\) [PERSON]\({}^{+}\)**} \\  & \multicolumn{3}{c}{**(\% of Revertants That Grew) on a Minimal Medium Lacking:**} & \multicolumn{2}{c}{**Phenotype**} \\ \cline{3-6}  & **His** & **[PERSON]** & **[PERSON]** & **(\% of Total)** \\ \hline \hline \multicolumn{1}{c}{**YB955**} & & & & \\ \hline His\({}^{+}\) & 104/104 (100) & 15/104 (14.4) & 0/104 (0) & 0 (0) \\ [PERSON]\({}^{+}\) & 68/78 (87.2) & 78/78 (100) & 0/78 (0) & 0 (0) \\ Leu\({}^{+}\) & 0/113 (0) & 0/113 (0) & 113/113 (100) & 0 (0) \\ \hline \hline \multicolumn{1}{c}{**\(\Delta\)cdaA**} & & & & \\ \hline His\({}^{+}\) & 110/110 (100) & 15/110 (13.6) & 1/110 (0.9) & 1 (0.9) \\ Met\({}^{+}\) & 75/76 (98.7) & 76/76 (100) & 5/76 (6.6) & 5 (6.6) \\ Leu\({}^{+}\) & 4/115 (3.5) & 9/115 (7.8) & 115/115 (100) & 3 (2.6) \\ \hline \hline \multicolumn{1}{c}{**\(\Delta\)disA**} & & & & \\ \hline His\({}^{+}\) & 168/168 (100) & 47/168 (27.9) & 2/168 (1.2) & 0 (0) \\ Met\({}^{+}\) & 71/84 (84.5) & 84/84 (100) & 14/84 (16.7) & 14 (16.7) \\ Leu\({}^{+}\) & 4/159 (2.5) & 0/159 (0) & 159/159 (100) & 0 (0) \\ \hline \hline \multicolumn{1}{c}{**\(\Delta\)cdaA/\(\Delta\)disA**} & & & & \\ \hline His\({}^{+}\) & 96/96 (100) & 18/96 (18.7) & 5/96 (5.2) & 1 (1.0) \\ Met\({}^{+}\) & 63/73 (86.3) & 73/73 (100) & 2/73 (2.7) & 2 (2.7) \\ Leu\({}^{+}\) & 1/102 (1.0) & 1/102 (1.0) & 102/102 (100) & 1 (1.0) \\ \hline \hline \end{tabular}  
* His, Met and Leu revertant colonies from days 4, 5 and 6 were tested on 1\(\times\) SSMM missing one required amino acid (His, Met, or Leu) to screen for suppressor mutations. Plates were scored after 48 h.  
\end{table} 
Table 1: Growth of stationary-phase DAC-deficient revertants on alternative selective media \({}^{\text{a}}\).
# Performance Model for Video Service in 5G Networks
## 1 Introduction
### Contribution  
In this paper, we present a two-level dynamic IC approach, referred to as intra-cluster and inter-cluster levels, coordinated in the time and frequency domains, to construct and optimize the QoS performance model, and an approach for utilizing the constructed QoS performance model for network design and optimization. The approach targets the SBS on the mmWave band, employs MU-MIMO to improve the system capacity, and is designed for delay-sensitive traffic (i.e., video services, which are fixed wireless access applications (FWA)). The main contributions of this paper include the following:  
* Our proposed approach targets improvement in performance models that map high-level customer-friendly business requirements to low-level network parameters and achieve QoS assurance for delay-sensitive traffic.
* The data analytics and ML approach is employed to construct the QoS performance model for network design and optimization, to identify the relationships and dependencies between SLA high-level requirements and low-level network attributes.  
The paper is organized as follows: the problem is formulated in Section 2, the proposed approach is described in Section 3, the simulation methodology is described in Section 4, the simulation results are presented in Section 5, the complexity analysis is shown in Section 6, and Section 7 concludes the paper.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Computational Infrastructure
#### 3.2.6 Performance Monitoring and Scalability  
Based on the demand for a service, the system must allow for horizontal scaling (i.e., adding more service instances). Horizontal scaling can be done manually or through the use of autoscaling [52], which requires monitoring of the instances (health check) and the resources they use (i.e., CPU, memory, bandwidth) to make real-time decisions about the resources assigned to each microservice. The creation of decision-making systems for scalability can be as simple as defining rules triggered depending on readings of resource consumption, or may involve complex solutions that rely on artificial intelligence algorithms [52; 53].
## 3 Results and Discussion
### Optimal Insulation Thickness Results
Although polyurethane has the best conductivity performance, its high cost resulted in the highest C\({}_{\text{t,c}}\) in the five cities, which emphasizes the importance of considering both performance and economic factors. A comparative analysis with a previous study [33] reveals shorter payback periods and adjusted optimal insulation thickness values in the present work. These improvements are attributed to updated economic parameters, enhanced optimization methodology, and refined consideration of climatic influences. The differences underscore the necessity of periodic updates to insulation strategies to account for evolving climatic and economic conditions, particularly in rapidly urbanizing regions like Riyadh and Jeddah.
## 3 Experimental Setup Testing and Analysis Results
### Experimental Setup and Procedure
#### 3.1.2 Explanation of Experimental Procedure  
The experimental procedure in this study followed the standard operating procedure of the impact-echo method and comprised the following steps:  
1. Wave velocity calculation: In this study, the wooden hammer was used as the impact echo device. when the impact echo device struck the concrete object that generated the first signal was considered the point of origin of the impact, and the first time when the echo wave reflected to the surface was considered the receiving point. The time lag between impact and reflection and the distance between
## 3 Results and Discussion
### Principal Component Analysis  
The previously discussed features have been studied by Principal Component Analysis (PCA) to highlight the relations between them, using only auto-scaled variables. Several variables show very low correlation with the most important PCs. Then, only the most important were retained: palmitic, palmitic, oleic, linoleic acids, \(\alpha\)-tocopherol, (\(E\))-2-hexenal, acetoxypinoresinol, and squalene. PCA on the selected variables permits a raw grouping of the samples, based on the different olive cvs., as shown in Figure 4.  
Figure 4: Biplot with projection of PC-1 vs PC-2, from PCA analysis on a subset of chemical parameters selected in order to optimize the olive cultivar separation in the monovarietal oils investigated (Ascolana tenera: ASC, Coroncina: COR, Mignola: MIG, Piantone di Mogliano: MOG, Raggia: RAG). PC: principal component, PCA: principal component analysis, LO: loading.  
This figure shows that (_E_)-2-hexenal, acetoxypinoresinol, and squalene divide the groups along PC-2 axis, that is, RAG and MIG from the others. Along PC-1, there is a mild separation between MIG and the others. In this direction, there are high loadings for palmitoleic, palmitic, and linoleic acids, low loading of oleic acid, and high loading of \(\alpha\)-tocopherol. PCA shows that few parameters, among the many investigated, are correlated with the olive cultivar. We can conclude that the analysis of the chemical parameters of the monovarietal oils analyzed permits to differentiate them, but are likely due to the relatively low number of samples the predictive ability is poor. The phenolic fraction of the analyzed parameters seems to be the most related to the varieties while the other parameters are related to quality (seasonal dependence) alone or confused with varietal differences.
## 5 Results
### Variation in the Exponent \(\gamma\) and in Hubs' Costs  
Table 7 shows the results of scenarios presented in Table 6. Analyzing the results, we can observe that with low values of the sensitivity distance exponent \(\gamma\), fewer hubs are open as smaller quantities are delivered from farmers to hubs; the opposite is true for larger values of \(\gamma\). The differences are important: For \(\gamma=0.1\) (low distance attractiveness), the number of open hubs ranged between 12 and 16, whereas for \(\gamma=3.0\), the number of hubs ranged between 24 and 25.  
Figure 8 illustrates the effects on the gross profit and the total cost of these parameter combinations. We can notice that the variation in hubs' costs represents a less significant change in the number of open hubs and on the traded quantities in comparison with changes in the exponent \(\gamma\). This behavior corroborates the results shown in Table 7. With values of \(\gamma\) less between 0.0 and 1.0, the variations are small; however, if the exponent \(\gamma\) is greater than 1.0, the gross profits rise steeply. On the other hand, Figure 8 also shows that net profit in all cases is greater than 0. This means that the hubs' network might be a cost-effective business for concessionaires.  
Figure 7: Transportation costs’ variation in the baseline scenario with and without hubs.  
For the purpose of comparison, we selected a scenario with low sensitivity to distance (\(\gamma\)) = 0.5 and 0% fixed cost variation. This is scenario 5 of Table 7. The results are illustrated in Figure 9a. The solution shows that 16 hubs are open, and that the pattern of hub locations is similar to the baseline scenario. However, in most cases, the traded quantities are smaller, as fewer farmers are attracted in this scenario. Only in the case of the Duitama hub (the large circle in the Boyaca department), the quantities handled are similar in comparison with the baseline. The explanation is that the strategic location and the lack of competitors of this hub make it the only trading option in this region for the small and medium size  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{\(\gamma\)} & \multirow{2}{*}{\begin{tabular}{c} **Hub Fixed Costs** \\ **Variation** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Average Quantities** \\ **Handled by Hubs** \\ **(Tons)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Total Quantities** \\ **(Weekly Tons)** \\ \end{tabular} } & \multirow{2}{*}{**Number of Hubs**} & \multirow{2}{*}{**Net Profit**} \\ \hline Baseline & 2.0 & 0\% & 77 & 8866 & 23 & 951 \\
1 & 0.1 & \(-\)20\% & 58 & 4601 & 16 & 413 \\
2 & 0.1 & 0\% & 60 & 4486 & 15  
farmers of northern Boyaca. In the department of Huila in this scenario, only two (as opposed to five in the baseline) hubs are open, and the corresponding trading quantities are smaller.
# Robotic System for Post Office Package Handling
## 3 Architecture
### Grasp Point Detection  
As the postal parcels have irregular shapes--see Figure 5a--for grasp point calculation, the Dex-Net 4.0 [23] library was selected, which delivers robust performance in the proposed application. It was configured to utilize the grasp quality convolutional neural networks (GQ-CNNs) method for grasp detection and delivers information for a system with a suction gripper, for which it is well suited. The camera intrinsics were obtained directly from the camera using its ROS driver and the calibration was performed beforehand. The chosen Zivid camera naturally produces images in 1920 \(\times\) 1200 resolution and such images take approximately 10 s to process with Dex-Net. To increase the performance, the images were firstly cropped to show only the region of interest (ROI) where the packages reside. Afterward, they were downscaled to 640 \(\times\) 480.  
The output quality of the Dex-Net is greatly enhanced if one provides a segmentation mask in its input showing precisely the region occupied with objects (the objects do not need to be segmented individually). To produce such a mask, the depth image of a clean table was taken during the calibration phase. When the system is operating, the active workspace data were compared with the empty table depth image to show only pixels with a depth value of fewer than 2 millimeters (minimal parcel height) above it. Such a setup allows Dex-Net to produce reliable grasps; see Figure 5.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 2 System Design
### Upstream Design  
Figure 2d depicts the TWDM design incorporating 32 undersea devices transmitting aggregate 10 Gbps NRZ-PRBS data signals by modulating four CW LDs which generated four UP wavelengths (1527.2 nm-1529.6 nm with 0.8 nm spectral width) at P\({}_{\text{in}}\) = 0 dBm using an MZM modulator. Furthermore, two Y-cascaded dynamic selects devices were employed with distinct switching times (0-1.6 ns) for all UP wavelengths. The received signals were forwarded to an Ideal MUX followed by AWG MUX at a remote node (RN). Then, on the OLT side, bidirectional SMF was used to receive the incoming signals. Following this, these signals passed through AWG DEMUX and passed to the CO side [11; 33]. The receiver section for the uplink is presented in Figure 2c.  
The -OCDMA design incorporated six wavelengths (1260-1262.4 nm with spectral width = 0.4 nm) that were originated as well as modulated with NRZ-PRBS combinations. Six wavelengths led to a 6 \(\times\) 1 AWG MUX, a 1 \(\times\) 3 fork and three subsystems, as shown in Figure 2g. Each Subsystem incorporated a 6 \(\times\) 1 AWG DEMUX and six different subsystems for defined time switching of optical signals using two continuous dynamic selects, as defined in [11; 33]. After passing across the 3 \(\times\) 1 ideal MUX and ODN sections, the signals were recognized and examined at a CO part in the receiver section, as shown in Figure 2f [32; 33]. The decoder section, which utilizes the MNZCC code, is shown in Figure 2k. In addition, the UP transmission in the NOMA-VLC section and satellite communication FSO system comprises similar components, such as the DN in the NOMA-VLC section and the DN satellite FSO system, respectively. The reverse VLC and FSO channels were used in the ODN section, as shown in Figure 2i. Table 2 tabulates the integrated FSO-SMF-VLC link parameters used.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Channel** & **Parameters** & **Value** \\ \hline \multirow{8}{*}{SMF} & Fiber Range & 0–100 km \\  & Attenuation & 0.2 dB/km \\ \cline{1-1}  & Temperature & 300 K \\ \cline{1-1}  & Dispersion & 17 ps/nm/km \\ \cline{1-1}  & Dispersion slope & 0.075 ps/nm\({}^{2}\)/k \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proposed SMF-VLC-FSO link parameters [11; 33].
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
## 3 Probability of Error  
In this section, the probability of error as a performance metric is derived and discussed. The probability of error is not the only metric addressed here, anyway. Energy entropy, energy capacity and energy cost are also evaluated in the following sections.  
Due to the presence of the zero-energy symbol in the constellation, the possible errors committed by the receivers are more complex than the traditional case.  
Table 1 shows the possible types of mistakes that a receiver can commit. These classes of errors, false activity, false zero and symbol error produce three types of corresponding error probabilities: \(P_{FA}\), \(P_{FZ}\) and \(P_{SE}\). We also define the aggregate probability of error as  
\[P_{E}=P_{FA}+P_{FZ}+P_{SE} \tag{12}\]In the Z-BPSK case, considering the decision region boundaries from Equation (8) we can write  
\[P_{FA}=P_{FA}|s_{0}=P(s_{0})\left(1-\int_{-\theta_{0,1}}^{\theta_{0,1}}p(r|s_{0}) dr\right) \tag{13}\]  
where \(\theta_{0,1}\) is the boundary point between the decision region of symbol zero and symbol +1. From Equation (8) we can observe that the boundary between symbol \(-1\) and symbol zero is \(\theta_{-1,0}=-\theta_{0,1}\).  
For what concern the probability of false zero we can write  
\[P_{FZ}=P_{FZ}|s_{1}+P_{FZ}|s_{2}=P(s_{1})\int_{-\theta_{0,1}}^{\theta_{0,1}}p(r| s_{1})dr+P(s_{2})\int_{-\theta_{0,1}}^{\theta_{0,1}}p(r|s_{2})dr \tag{14}\]  
While the probability of symbol error is  
\[P_{SE}=P_{SE}|s_{1}+P_{SE}|s_{2}=P(s_{1})\int_{-\infty}^{-\theta_{0,1}}p(r|s_{ 1})dr+P(s_{2})\int_{\theta_{0,1}}^{+\infty}p(r|s_{2})dr \tag{15}\]  
In the case of AWGN channel the above equation can be expressed by using the \(Q\)-funtion  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{+\infty}e^{-\frac{x^{2}}{2}}dx,\quad x\geq 0 \tag{16}\]  
resulting in  
\[P_{FA}= 2P_{0}Q\left(\frac{\theta_{0,1}}{\sigma_{n}}\right) \tag{17}\] \[P_{FZ}= (1-P_{0})\left[Q\left(\frac{\sqrt{E_{1}}-\theta_{0,1}}{\sigma_{n }}\right)-Q\left(\frac{\sqrt{E_{1}}+\theta_{0,1}}{\sigma_{n}}\right)\right]\] (18) \[P_{SE}= (1-P_{0})Q\left(\frac{\sqrt{E_{1}}+\theta_{0,1}}{\sigma_{n}} \right). \tag{19}\]  
The aggregate probability of error defined in Equation (12) results  
\[P_{E}=2P_{0}Q\left(\frac{\theta_{0,1}}{\sigma_{n}}\right)+(1-P_{0})Q\left( \frac{\sqrt{E_{1}}-\theta_{0,1}}{\sigma_{n}}\right) \tag{20}\]