esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Bioaugmentation on Biogas Yields and Kinetics in Anaerobic Digestion of Sewage Sludge
## 2 Materials and Methods
### Analytical Methods  
In the mixed sludge (SS), total chemical oxygen demand (COD), total solids (TS) and volatile solids (VS), were analyzed once a week. The same schedule was used for determining the values of the parameters that characterized the supernatant (sludge liquid phase) before digestion. These parameters included soluble chemical oxygen demand (SCOD), volatile fatty acids (VFA), alkalinity and pH level. The supernatant samples were obtained by centrifuging the sludge at 4000 rpm for 30 min. All analyses were carried out in accordance with the procedures listed in the Standard Methods for the Examination of Water and Wastewater [23].  
The bioaugmenting mixture was examined twice a week as an averaged and a collected sample taken from its storage tank. The following parameters were analyzed: COD, TS, VS, VFA, alkalinity, and pH.  
In the digested sludge, the specified parameters were determined two times a week, in accordance with the timetable adopted. Similarly, the supernatant of the digested sludge was examined using the same schedule.  
Anaerobic digestion efficiency was controlled by the daily evaluation of the biogas yield and its composition (CH\({}_{4}\), CO\({}_{2}\), and other gases). Moreover, the volatile solids removal efficiency was evaluated according to the American Public Health Association (APHA) [23].  
Figure 2: Laboratory installation. 1—anaerobic reactor, 2—mechanical stirrer, 3—heating jacket, 4—influent peristaltic pump, 5—influent storage vessel, 6—effluent storage vessel, 7—drum gas meter, 8—gaseous installation and gas sampler with a rubber septum, 9—dewatering valve, 10—inlet valve, 11—outlet valve.  
The biogas production was determined using Aalborg (USA) digital mass flow meter Its composition was measured using Trace GC-Ultra gas chromatograph coupled with a thermal conductivity detector (TCD) fitted with divinylbenzene (DVB) packed columns. The Rt-Q-Bond column was used to determine the CH\({}_{4}\) and CO\({}_{2}\) concentrations. The parameters used for the analysis were as follows--inject 50 \({}^{\circ}\)C and detector 100 \({}^{\circ}\)C. The carrier gas was helium with a flux rate of 1.5 cm\({}^{3}\cdot\)min\({}^{-1}\). Peak areas were determined by the computer integration program (CHROM-CARD).  
The biogas production curves were made on the basis of the averaged experimental data acquired from an XFM Control Terminal. Both the rate constant k and maximum biogas production V\({}_{\text{max}}\) were obtained by means of nonlinear regression method with Statsoft Statistica software (version 10, TIBCO Software Inc., Palo Alto, CA, USA). The strength of the relationships between groups of the results was determined using Pearson's correlation coefficient (R) and determination coefficient R\({}^{2}\).
# Evaluation of Hydrogen Generation with Hybrid Renewable Energy Sources
###### Abstract  
Generating hydrogen by electrolysis in an alkaline system with a green power source consisting of wind turbines (WTs) and photovoltaic (PV) power is a promising and sustainable way to produce clean hydrogen to reduce greenhouse gas emissions. This study utilized TRNSYS 16 software to perform a dynamic simulation of a hydrogen system. TRNSYS, which stands for Transient System Simulation Program, is a software package designed for simulating the dynamic behaviour of thermal and electrical energy systems. It is widely used to analyze and optimize the performance of various energy systems. This system incorporated a PV power source and a WT for electricity generation, along with an electrolyzer for hydrogen production. The analysis was carried out to evaluate variable weather conditions, specifically wind speed, solar radiation, and temperature. These factors have a direct impact on the system's performance, influencing the available power as a consequential outcome. The results reveal that, given the specific climate conditions in the Markham zone, Toronto, the integrated renewable system is capable of consistently providing electricity and meeting the load demand throughout the entire year. However, it is noteworthy that on cold days when solar radiation is limited, the WT emerges as the most effective and efficient power source. The analysis also indicates that the system reliably supplies enough energy to meet the laboratory's load demand. Moreover, the system's performance is particularly impressive with the WT as the power source, as it can generate a maximum of 9.03 kg of hydrogen per month. In contrast, the PV power source yields only 0.58 kg H\({}_{2}\). Additionally, the cost per kilogram of hydrogen (kg H\({}_{2}\)) is considerably lower when the WT is used, at USD 0.55/kg H\({}_{2}\), while it rises to USD 1.5/kg H\({}_{2}\) when PV is the power source. These findings underscore the significance of using the most suitable power source, such as a WT, in specific climatic conditions and regions in terms of both performance and cost-effectiveness.  
water electrolysis; hydrogen production; renewable energy; solar power; wind turbine; TRNSYS +
Footnote †: journal: Applied Sciences  
12 June 2024  
15 July 2024  
11 July 2024
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### ISM Band-Based Technologies
#### 2.2.7 IEEE Standards  
The Institute of Electrical and Electronics Engineers (IEEE) defined three standards for IoT communications. These are based on 802.15.4 and 802.11 specifications. IEEE 802.15.4k [35] is a standard operating in the ISM bands (sub-GHz and 2.4 GHz). The 802.15.4 modulation schemes are switched to DSSS and FSK, and MAC layer to CSMA/CA without Priority Channel Access (PCA), CSMA, and ALOHA with PCA. This allows a data rate between 1.5 bps and 128 kbps. It is worth noting that Ingenu's technology is compliant with this standard. IEEE 802.15.4g [36] is a different specification based on the same standard as 802.15.4k. Here, we have three different modulation schemes usable at the physical layer: FSK, OFDMA and QPSK. Data rate is between 4.8 and 800 kbps. The sub-GHz bands are also used. One of the differences with traditional 802.15.4 is that frames can be up to 1500 bytes instead of only 127 bytes to avoid IP fragmentation. MAC layer is the same as in 802.15.4e. IEEE 802.11 ah [37] a.k.a. Wi-Fi HaLow is based on OFDM and aims to increase the transmission range and decrease energy consumption of Wi-Fi, at the expense of a lower data rate (0.6 to 8 Mbps). It achieves better performance than e.g., ZigBee or Bluetooth but far from previous LPWAN technologies presented in this report.
# Reliability of a Portable Fixed Dynamometer During Different Isometric Hamstring Assessments
## 3 Results
### Supine 90:90 Assessment  
Once more, peak force, mean force and impulse were found to have excellent within-session reliability (CV = 3.0-6.1%, ICC = 0.96-0.99), while peak RFD had good relative reliability (ICC = 0.76-0.83) and poor absolute reliability (CV = 21.0-23.9%). The largest between-trial change was observed between trials 3 and 2 for peak RFD (11.8%). In terms of between-session reliability, peak and mean force had excellent reliability (CV = 6.5-9.7%, ICC = 0.90-0.95), while impulse had moderate to excellent reliability  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean \(\pm\) SD**} & \multicolumn{3}{c}{**\% Change in Mean**} & \multicolumn{3}{c}{**CV**} & \multicolumn{3}{c}{**ICC**} \\  & \multicolumn{3}{c}{**(95\% CI)**} & \multicolumn{3}{c}{**(95\% CI)**} & \multicolumn{3}{c}{**(95\% CI)**} & \multicolumn{3}{c}{**(95\% CI)**} \\ \cline{2-10}
**Variable** & \multirow{2}{*}{**Session 1**} & \multirow{2}{*}{**Session 2**} & \multirow{2}{*}{**Session 3**} & \multirow{2}{*}{**Session 2-1**} & \multirow{2}{*}{**Session 3-2**} & \multirow{2}{*}{**Session 3-2**} & \multirow{2}{*}{**Session 3-2**} & \multirow{2}{*}{**Session 3-2**} \\ \cline{2-10}  & & & & & & & & & \\ \hline \multicolumn{10}{c}{_Standing_} \\ \hline Peak force & 308.6 \(\pm\) & 310.9 \(\pm\) & 320.0 \(\pm\) & 1.7 & 2.6 & 7.8 & 5.9 & 0.91 & 0.94 \\ (N) & 70.9 & 60.9 & 67.1 & (\(-\)4.3-8.10) & (\(-\)2.1-7.5) & (5.6–12.8) & (4.2–9.6) & (0.75–0.97) & (0.82–0.98) \\ \hline Mean force & 254.2 \(\pm\) & 263.4 \(\pm\) & 265.7 \(\pm\) & 4.7 & 1.0 & 9.1 & 6.3 & 0.88 & 0.92 \\ (N) & 60.0 & 51.3 & 50.3 & (\(-\)2.5–12.4) & (\(-\)3.9–6.2) & (6.5–15.0) & (4.5–10.4) & (0.67–0.96) & (0.77–0.97) \\ \hline Impulse & 819.2 \(\pm\) & 839.6 \(\pm\) & 830.1 \(\pm\) & 2.8 & \(-\)1.0 & 6.9 & 6.4 & 0.90 & 0.91 \\ (N-s) & 167.7 & 155.7 & 146.1 & (\(-\)2.6–8.6) & (\(-\)5.8–4.2) & (5.0–11.4) & (4.6–10.4) & (0.73–0.97) & (0.73–0.97) \\ \hline Peak RFD & 1495.3 \(\pm\) & 1428.4 \(\pm\) & 1712.1 \(\pm\) & 2.5 & \multirow{2}{*}{16.1} & \multirow{2}{*}{28.3} & \multirow{2}{*}{29.2} & \multirow{2}{*}{0.80} & \multirow{2}{*}{0.74} \\ (N-s\({}^{-1}\)) & 856.3 & 579.9 & 894.3 & (\(-\)5.8–43.2) & (19.8–49.3) & (20.4–51.1) & (0.49–0.93) & (0.36–0.91) \\ \hline \multicolumn{10}{c}{_Supine 90:90_} \\ \hline Peak force & 321.7 \(\pm\) & 326.3 \(\pm\) & 333.6 \(\pm\) & 1.1 & 2.7 & 6.7 & 6.5 & 0.95 & 0.95 \\ (N) & 75.3 & 78.0 & 75.9 & (\(-\)4.0–6.6) & (\(-\)2.4–8.2) & (4.8–10.9) & (4.7–10.7) & (0.86–0.98) & (0.86–0.98) \\ \hline Mean force & 277.7 \(\pm\) & 279.0 \(\pm\) & 292.5 \(\pm\) & \multirow{2}{*}{\(-\)0.1} & \multirow{2}{*}{5.8} & \multirow{2}{*}{8.9} & \multirow{2}{*}{9.7} & \multirow{2}{*}{0.92} & \multirow{2}{*}{0.90} \\ (N) & 66.0 & 70.8 & 66.  
(CV = 7.4-15.4, ICC = 0.74-0.94). The largest change between sessions was observed for peak RFD (20.9%), which had moderate reliability (CV = 17.0-22.0%, ICC = 0.83-0.88).
## 5 Simulation Details
### Boundary Conditions  
The boundary conditions consistent with similarity functions are applied to modeling the atmospheric boundary stratification. Wind profile at inlet is estimated by Equation (37). The inlet profiles of potential temperature, TKE, and its dissipation are given by  
\[\Theta(z)=\Theta_{0}+\int_{z_{0}}^{z}\frac{\theta_{*}}{\kappa z}\phi_{h}\left( \frac{z}{L}\right)\text{d}z \tag{40}\]  
\[\varepsilon=\frac{u_{*}^{3}}{\kappa z}\phi_{\varepsilon}\left(\frac{z}{L}\right) \tag{41}\]  
\[k=\frac{u_{*}^{2}}{\sqrt{C_{H}}}\phi_{k}\left(\frac{z}{L}\right) \tag{42}\]  
The vertical profiles of potential temperature are estimated in numerical integration. At the outlet, zero gradients of \(U,\Theta,\varepsilon,k\) are applied. For the top boundary, the upstream flow properties are maintained constant. The left and right sides of the computational domain are set to be symmetry.  
The near-wall treatment of [PERSON] et al. [53] is implemented in the near ground region to calculate turbulent dissipation rate and the TKE production \(G_{k,p}\) due to shear (\(\mathcal{P}_{p}\)) and buoyancy (\(\mathcal{B}_{p}\)):  
Figure 10: Computational domain and meshing settings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} \\ \cline{2-5}  & \(u_{*}\) **(m/s)** & **TI** & \(u_{*}\) **(m/s)** & **TI** \\ \hline AM, Laan, El-Askary & 0.223 & 6.0\% & 0.198 & 4.5\% \\ Proposed & 0.297 & 10.2\% & 0.228 & 6.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test cases and corresponding parameters under stable conditions.  
\[\varepsilon_{p}=\frac{u_{sk}^{3}}{\kappa z_{p}}\phi_{c,p} \tag{43}\]  
\[G_{k,p}\equiv\mathcal{P}_{p}+\mathcal{B}_{p}=\frac{\tau_{w}^{2}}{\rho\kappa u_{sk }(z_{p}+z_{0})}-\frac{\theta_{s}|g|}{\Theta_{0}(z_{p}+z_{0})} \tag{44}\]  
where the subscript \(p\) denotes the first cell center above the ground, the equivalent friction wind speed is \(u_{sk}=C_{\mu}^{0.25}\phi_{k,p}^{-0.5}k_{p}^{0.5}\), and the wall shear stress is \(\tau_{w}=\mu_{t,p}(\mathrm{d}U/\mathrm{d}z)\). The eddy viscosity \(\mu_{t,p}\) and the turbulent heat conductivity are imposed at the first cell center above the ground as [54]  
\[\mu_{t,p} =\frac{u_{sk}\,z_{p}}{\int_{z_{0}}^{z_{p}}\frac{1}{\kappa z}\phi_{ m}\left(\frac{z}{t}\right)\mathrm{d}z}-\mu \tag{45}\] \[\alpha_{t,p} =\mu_{t,p}\phi_{m,p}/\phi_{h,p}-\mu/Pr \tag{46}\]  
where the laminar viscosity \(\mu\) is not ignored to keep consistent with Equations (13) and (14).
## 5 SCODE_plus_: Enhanced Secure Routing and Key Management Scheme
### Key Management
#### 5.2.4 Establishing/Revoking Keys of New/Existing Sensors  
To add a new sensor, the key setup server only needs to predistribute the related polynomial shares to the new node, similar to predistribution phase. Since the size of key-space is limited, the more sensors are added, the lower the security in that cell becomes.  
The revocation method is also straightforward. Each sensor node only needs to store a black list IDs of compromised sensors that share at least one bivariate polynomial with itself. If there are more thancompromised nodes sharing the same polynomial, the non-compromised nodes that have this polynomial will remove this polynomial and all related compromised nodes.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## 3 Results
### Response of Ecosystem-Scale Phenological Processes to Weather Disturbances  
All study sites experienced a severe, but short-term drought in March 2017; although there were no extreme air temperature events during that month (16.5, 16.7 and 16.7 \({}^{\circ}\)C, respectively), precipitation was 12.95, 13.97 and 11.17 mm mo\({}^{-1}\) for mesic, intermediate, and xeric sites, respectively (Appendix D, Figure A4). This resulted in anomalously high values of SOP during this year at all sites (Figure 7). Excluding this short, severe drought event, we found that as March air temperature anomalies increased, the SOP was significantly advanced in the mesic and intermediate sites, but there was no similar pattern in the xeric site. Due to this 2017 drought event, the SOP was delayed by 176, 148, and 162 d, at the mesic, intermediate, and xeric sites, respectively (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Mesic**} & \multicolumn{3}{c}{**Xeric**} & \multicolumn{3}{c}{**Intermediate**} \\ \hline \multirow{2}{*}{**Time Scale**} & **Climate** & \multirow{2}{*}{**SOS**} & \multirow{2}{*}{**EOS**} & \multirow{2}{*}{**LOS**} & \multirow{2}{*}{**SOS**} & \multirow{2}{*}{**EOS**} & \multirow{2}{*}{**LOS**} & \multirow{2}{*}{**SOS**} & \multirow{2}{*}{**EOS**} & \multirow{2}{*}{**LOS**} \\  & **Variables** & & & & & & & & & \\ \hline Annual & \(T_{a}\) anomaly & \(0.01\) & \(-0.04\) & \(-0.02\) & \(\mathbf{-0.70\,^{*}}\) & \(\mathbf{0.69\,^{**}}\) & \(\mathbf{0.90\,^{**}}\) & \(-0.15\) & \(0.25\) & \(0.08\) \\  & Prtn anomaly & \(0.14  
Based on an analysis of the length of recovery (LOR; Appendix E, Table A4), we found that the ecosystem-scale phenological response during the summer was different by site following short-term drought. The start of disturbance (SOD) was generally located in the early summer and late spring (130-160 d). Timing of recovery from disturbance (ROD) depended on when precipitation started to recover after this short-term water stress, and the peak of recovery (POR) was generally in the end of August (up to 252 d) after the restoration of precipitation. If precipitation did not recover after short-term, summer drought, it caused ecosystem GPP to continue to decline until EOS (autumn) with a short summer (<60 d).  
Short-term summer drought lasted from June to August in some years, and in these cases summer GPP did not recover (no POR) (Appendix E, Table A4). POR was significantly correlated with August precipitation (mm; \(p\) < 0.01). With increased precipitation in August after a short-term drought, POR was significantly delayed (end of summer) across the sites (Appendix F, Figure A5).
## 3 Results
### Test Quality Parameters of the Prediction Methods
#### 3.2.1 Tennis Recommendation Score
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Groups**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**M**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**SE**} & \multicolumn{3}{c}{**95\% CL**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} \\ \cline{5-8} \cline{7-10}  & & & & & & & **LL** & & **UL** \\ \hline Calendar age * (month) & LP & 158 & 93.8 & 5.0 & 0.39 & 93.0 & 94.6 & 83 & 110 \\ (Time of testing; U9) & TP & 16 & 97.5 & 6.4 & 1.60 & 94.1 & 100.9 & 88 & 112 \\ \hline \multicolumn{8}{c}{Test results} \\ \hline Body height * (cm) & LP & 158 & 129.1 & 5.7 & 0.45 & 128.2 & 130.0 & 117 & 145 \\  & TP & 16 & 132.3 & 4.5 & 1.13 & 129.8 & 134.7 & 127 & 143 \\ \hline Body weight (kg) & LP & 158 & 27.2 & 4.1 & 0.33 & 26.6 & 27.9 & 20.0 & 39.3 \\  & TP & 16 & 27.3 & 1.9 & 0.48 & 26.3 & 28.3 & 23.4 & 30.6 \\ \hline Sideward jumping ** (repeats) & LP & 157 & 27.3 & 6.1 & 0.49 & 26.3 & 28.2 & 6.5 & 40.5 \\  & TP & 16 & 33.0 & 5.1 & 1.27 & 30.3 & 35.7 & 27.0 & 45.0 \\ \hline Balance backward ** (steps) & LP & 158 & 30.3 & 8.5 & 0.67 & 28.9 & 31.6 & 8 & 48 \\  & TP & 16 & 38.3 & 5.7 & 1.42 & 35.2 & 41.3 & 28 & 48 \\ \hline Standing long jump ** (cm) & LP & 157 & 135.1 & 16.2 & 1.30 & 132.5 & 137.6 & 82 & 190 \\ (cm) & TP & 16 & 153.0 & 17.2 & 4.30 & 143.8 & 162.2 & 125 & 178 \\ \hline
20 m sprint * (s) & LP & 158 & 4.45 & 0.36 & 0.03 & 4.39 & 4.51 & 3.10 & 5.32 \\  & TP & 16 & 4.21 & 0.34 & 0.08 & 4.03 & 4.39 & 3.50 & 4.72 \\ \hline Push-ups * (repeats) & LP & 158 & 14.6 & 3.6 & 0.28 & 14.0 & 15.1 & 4 & 24 \\  & TP & 16 & 17.1 & 5.2 & 1.30 & 14.3 & 19.9 & 9 & 25 \\ \hline Sit-ups * (repeats) & LP & 158 & 19.2 & 5.1 & 0.40 & 18.4 & 20.0 & 2 & 30 \\  & TP & 16 & 23.4 & 4.1 & 1.02 & 21.3 & 25.6 & 15 & 29 \\ \hline Bend forward (cm) & LP & 158 & 1.98 & 5.94 & 0.47 & 1.05 & 2.92 & \(-\)11 & 18 \\  & TP & 16 & 4.16 & 4.90 & 1.22 & 1.55 & 6.77 & \(-\)10 & 12 \\ \hline
6 min run ** (m) & LP & 154 & 959 & 130.8 & 10.5 & 938 & 979 & 545 & 1259 \\  & TP & 16 & 1078 & 80.7 & 20.2 & 1035 & 1121 & 891 & 1200 \\ \hline Ball throw ** (m) & LP & 155 & 13.5 & 4.03 & 0.32 & 12.9 & 14.2 & 3.8 & 27.6 \\  & TP & 16 & 18.8 & 5.35 & 1.34 & 15.9 & 21.6 & 9.2 & 28.3 \\ \hline \hline \end{tabular}  
* M = mean; SD = standard deviation; SE = standard error; CL = confidence limit; LL = lower limit; UL = upper limit; Min = minimum; Max = maximum; LP = low performer; TP = top performer;
* \(p\leq 0.05\); *
* \(p\leq 0.01\).  
\end{table} 
Table 3: Descriptive statistics for the two anthropometric, five physical fitness, and four motor competence diagnostics of the U9 participants.
# Surface Defect Detection Methods for Industrial Products: A Review
## 4 The Key Problems
### Small Sample Problem  
In reality, surface defect detection methods based on deep learning often cannot be directly used for the surface defect detection task of industrial products. One of the main reasons is that the continuous optimization of modern industrial processes has led to fewer and fewer defective samples, that is, the number of defect images is very limited. This problem of learning from a small number of samples is usually called the small sample problem [81], which can easily lead to over-fitting problems during training. At present, there are mainly the following four mainstream solutions to solve the problem of small samples:
# Log Analysis-Based Resource and Execution Time Improvement in HPC: A Case Study
## 1 Introduction  
A batch job scheduler periodically monitors the status of computing resources in a cluster and distributes jobs efficiently. Such a batch job scheduler is called, in general, the Distributed Resource Management System (DRMS) [1], Workload Management System (WMS) [2], or simply a job scheduler.  
The basic role of the scheduler is to accurately reflect the resource status [3]. This includes the various computational resource, such as licenses, CPU, and memory, as well as many-core based acceleration systems, such as GPU and Intel PHI (many-core architecture) [4], which are of recent interest. It also meets requirements such as the fair-share policy [5] to ensure the fair distribution of resource distribution after awareness of resources, preemption support for high-priority jobs, resource scalability assurance, and support for various system environments. Most job scheduler software reflects the user job environment, from job submission to termination, as well as the state of the inventory and system status of the entire managed object. It also stores various pieces of information related to job execution, such as job scripts, environment variables, libraries, waiting, and the starting and ending times for a job. In order to configure the cluster environment and manage batch jobs, the scheduler is chosen to reflect the characteristics of the computation environment in software and hardware perspective [6].  
This study analyzes the job execution logs performed in the batch scheduler of the Tachyon2 system, which has been actively operated in the National Supercomputing Center at Korea Institute ofScience and Technology Information (KISTI) [7]. The acquired log of the supercomputer contains rich pieces of information related to submitted jobs, such as user information, execution time, resource size, job exit status, and so on [8]. When a large-scale job is submitted to the batch scheduler, it waits without using the returned compute resource until the requested resource is prepared. This supercomputer log shows that idle resources are not used during this waiting time, causing inefficient resource waste. Therefore, the efficiency of resource allocation has to be evaluated by the scheduling algorithm [9]. It can be seen that optimization can be performed by applying the backfilling. In this paper, we will show that turnaround time can be reduced by optimizing the inefficiency of waiting resources.  
The rest of this paper is organized as follows: In Section 2, we will introduce system configurations of Tachyon2, which is the supercomputer used for our analysis. In Sections 3 and 4, we will introduce the backfilling algorithm and our experimental results, respectively. We will conclude this paper in Section 5.
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 2 Models and Restrictions
### OLSR Routing Protocol
In the network (in TC packets), only information about the state of connections between an MPR and its MPRS is transmitted. This mechanism significantly reduces the amount of control packet transmissions compared to flooding. Additionally, OLSR control messages contain sequential numbers that increase in subsequent messages. Therefore, the recipient of a control message can easily determine which information is up-to-date. OLSR is designed as a fully distributed protocol and does not rely on any root nodes. Each node also periodically broadcasts control packets, ensuring protocol stability even in the event of partial message loss. The architecture and advantages of the OLSR base protocol have led to the development of numerous protocol modifications that support QoS in various forms. OLSR is a table-driven protocol. It supports several active tables: for tracking symmetric neighbors with one transition, neighbors with two transitions, MPR, a topology information base, a repeating message set, a multiple interface union set, and more. Based on input information from Hello packets and topology control packets, and information about its multiple network interfaces, each node creates sets of links, neighbor sets, two-hop neighbor sets, MPR sets, MPR selector sets, a topology information base, duplicate sets, and multiple interface join tables. Based on these tables, each node decides whether it will forward OLSR messages received by it to other neighbors. Based on the topology, it creates routes. Figure 3 covers the OLSR routing protocol mechanism [26].